# PiAPI > PiAPI is a developer-first unified API gateway for image, video, audio, music, 3D, and language models. Base URL: https://api.piapi.ai Authentication: X-API-Key header (get a key at https://piapi.ai/workspace) ## Model and tool pages - [Ace-Step API](https://piapi.ai/ace-step): ACE Step is the most innovative AI model designed to transform music generation. Bridging the gap between speed, musical coherence, and control, ACE-Step sets a new standard in music AI, creating music that empowers artists and creators to explore new realms of music gneration. Machine-readable guide: https://piapi.ai/ace-step/llms.txt - [AI Hug Generator](https://piapi.ai/ai-hug): Use our hug AI generator to turn any photo of two people into a hugging video in minutes. Try the free AI hug video generator playground or scale with the hugging AI API via PiAPI. Machine-readable guide: https://piapi.ai/ai-hug/llms.txt - [Claude Fable 5 API](https://piapi.ai/claude-fable-5): Claude Fable 5 API - Advanced AI with breakthrough autonomous execution, coding, and vision capabilities. Work for hours without intervention. Pay-as-you-go. Machine-readable guide: https://piapi.ai/claude-fable-5/llms.txt - [Deep Research API from ChatGPT](https://piapi.ai/deep-research): Autonomous AI agent that synthesizes hundreds of online sources into expert reports—saving hours on complex research tasks Machine-readable guide: https://piapi.ai/deep-research/llms.txt - [DeepSeek API](https://piapi.ai/deepseek): Try the most cost effective Large Language Model DeepSeek R1 API that is open source and fully licensed to empower your applications! Machine-readable guide: https://piapi.ai/deepseek/llms.txt - [DiffRhythm API](https://piapi.ai/diffrhythm): Discover DiffRhythm, the first open-sourced diffusion-based AI music generation model that creates full-length songs with vocals and accompaniment in just 10 seconds! Machine-readable guide: https://piapi.ai/diffrhythm/llms.txt - [Dream Machine API](https://piapi.ai/dream-machine-api): Want to allow your users to create breathtaking videos from a simple text prompt or an image? Checkout out PiAPI's Dream Machine API - start generating high-quality videos today! Machine-readable guide: https://piapi.ai/dream-machine-api/llms.txt - [F5-TTS API](https://piapi.ai/f5-tts-api): Try the F5-TTS zeroshot text-to-speech API in an interactive playground, then integrate it via PiAPI to clone voices and generate high-quality speech audio from text. Machine-readable guide: https://piapi.ai/f5-tts-api/llms.txt - [Face Swap API](https://piapi.ai/faceswap-api): Experience the power of AI with our FaceSwap API. Embed face-swapping feature into your applications and offer your users the ability to swap faces on their images! Machine-readable guide: https://piapi.ai/faceswap-api/llms.txt - [Flux API](https://piapi.ai/flux-api): Want to bring the best open source visual generation model from Black Forest Labs to your users? Try Flux API from PiAPI to get started! Machine-readable guide: https://piapi.ai/flux-api/llms.txt - [Flux Kontext API](https://piapi.ai/flux-kontext): Flux Kontext is an advanced image-to-image editing model that comprehends both your visuals and creative vision. Created by Black Forest Labs and optimized for production use, this powerful Kontext model enables you to transform, enhance, and reimagine images through natural language commands. From adjusting design elements to completely changing artistic styles, the Kontext API makes complex editing feel effortless. Machine-readable guide: https://piapi.ai/flux-kontext/llms.txt - [Flux Kontext Dev](https://piapi.ai/flux-kontext/flux-kontext-dev): FLUX Kontext Dev is an open-weight, developer-oriented AI image editing model with ultra-fast inference, batch support, and multi-image capabilities. Machine-readable guide: https://piapi.ai/flux-kontext-dev/llms.txt - [Flux Kontext Max](https://piapi.ai/flux-kontext/flux-kontext-max): FLUX Kontext Max offers maximum precision and control for AI image editing with improved prompt adherence, typography, and premium consistency. Machine-readable guide: https://piapi.ai/flux-kontext-max/llms.txt - [Flux Kontext Pro](https://piapi.ai/flux-kontext/flux-kontext-pro): FLUX Kontext Pro delivers professional-grade image editing with context-aware AI. Fast iterative editing while maintaining character consistency across scenes. Machine-readable guide: https://piapi.ai/flux-kontext-pro/llms.txt - [Framepack API](https://piapi.ai/framepack): Use Framepack API via PiAPI to generate long-form AI videos from static images. Extend videos with image-to-video generation, flexible duration, API docs, pricing and free credits. Machine-readable guide: https://piapi.ai/framepack/llms.txt - [GPT Image 1 API](https://piapi.ai/gpt-image-1): Bring gpt-image-1 image generation capability through API to your users! Machine-readable guide: https://piapi.ai/gpt-image-1/llms.txt - [GPT Image 1.5 API](https://piapi.ai/gpt-image-1-5): Access GPT Image 1.5 via PiAPI for image generation and editing. Supports JPG/PNG/WEBP, high-resolution outputs, code samples, pricing, and free credits to start fast. Machine-readable guide: https://piapi.ai/gpt-image-1-5/llms.txt - [GPT Image 2.5 API](https://piapi.ai/gpt-image-2-5): PiAPI provides GPT Image 2.5 Flare and Sunburst for image generation and editing. Both models share the same USD rates per 1M tokens. Machine-readable guide: https://piapi.ai/gpt-image-2-5/llms.txt - [GPT Image 2 API](https://piapi.ai/gpt-images-2-0): Explore the GPT Image 2 API on PiAPI for high-quality image generation, examples, pricing, and playground access. Build with GPT Image 2 and OpenAI Image 2 workflows. Machine-readable guide: https://piapi.ai/gpt-images-2-0/llms.txt - [Hailuo API](https://piapi.ai/hailuo): Use Hailuo API via PiAPI for text-to-video, image-to-video, and subject-reference videos. View docs, Hailuo 2.3 pricing, free credits, and start integrating. Machine-readable guide: https://piapi.ai/hailuo/llms.txt - [Hailuo 2 API](https://piapi.ai/hailuo-02): Discover MiniMax Hailuo 2's groundbreaking video generation capabilities with state-of-the-art instruction following and extreme physics mastery—all delivered at world-class cost efficiency. Machine-readable guide: https://piapi.ai/hailuo-02/llms.txt - [Hunyuan API](https://piapi.ai/hunyuan): Use the Hunyuan Video API to generate text-to-video and image-to-video with simple pricing and a free playground. View API docs and integrate in minutes. Machine-readable guide: https://piapi.ai/hunyuan/llms.txt - [AI Image Background Remover](https://piapi.ai/image-remove-background): Remove image backgrounds instantly with PiAPI's AI image background remover tool. API Docs, Pricing and Free credits available. Start with PiAPI today! Machine-readable guide: https://piapi.ai/image-remove-background/llms.txt - [AI Image Segmentation Tool](https://piapi.ai/image-segmentation-tool): Segment any region in your images using the Segmentation API. Generate clean masks or foreground cutouts in seconds for ecommerce, creative tools, and automated workflows. Machine-readable guide: https://piapi.ai/image-segmentation-tool/llms.txt - [AI Image Upscaler](https://piapi.ai/image-upscaler-api): Upscale and enhance images instantly with PiAPI's AI Image Upscaler API. Improve resolution and sharpen details so your images look great everywhere. Machine-readable guide: https://piapi.ai/image-upscaler-api/llms.txt - [Kling 2.5 API](https://piapi.ai/kling-2-5): Access Kling 2.5 via PiAPI. Use Image-to-Video and Text-to-Video with Turbo mode, view pricing, explore API docs, and start generating videos with a developer-ready endpoint. Machine-readable guide: https://piapi.ai/kling-2-5/llms.txt - [Kling 2.6 API](https://piapi.ai/kling-2-6): Use Kling 2.6 API via PiAPI to generate image-to-video and text-to-video with native audio. View API docs, pricing, free credits, and start integrating in minutes. Machine-readable guide: https://piapi.ai/kling-2-6/llms.txt - [Kling 2.6 Dance Generator](https://piapi.ai/kling-2-6/dance-generator): Try the Kling AI Dance Generator with a free 5-second demo. Upload an image and short video to preview AI-generated dance motion before using Kling 2.6 via PiAPI. Machine-readable guide: https://piapi.ai/kling-2-6-dance-generator/llms.txt - [Kling 2.6 Motion Control](https://piapi.ai/kling-2-6/motion-control): Try Kling 2.6 Motion Control in a live playground. Upload reference videos, control motion strength, test features with free credits, then scale with the Kling 2.6 API via PiAPI. Machine-readable guide: https://piapi.ai/kling-2-6-motion-control/llms.txt - [Kling 2.6 Motion Poster](https://piapi.ai/kling-2-6/motion-poster): Try the Kling AI Motion Poster Playground to turn images or references videos into dynamic motion posters. Free demo. Upload your own assets, then integrate Kling 2.6 via PiAPI. Machine-readable guide: https://piapi.ai/kling-2-6-motion-poster/llms.txt - [Kling 3.0 API](https://piapi.ai/kling-3-0): Use Kling 3.0 API via PiAPI to generate text-to-video and image-to-video with multi-shot control and native audio, built on Kling 2.6. View docs, pricing, free credits, and start integrating in minutes. Machine-readable guide: https://piapi.ai/kling-3-0/llms.txt - [Kling 3.0 Omni API](https://piapi.ai/kling-3-omni): Use Kling 3.0 Omni API for multi-shot text-to-video and image-to-video with native audio. Compare $0.10-$0.20/sec pricing, docs, and free credits. Machine-readable guide: https://piapi.ai/kling-3-omni/llms.txt - [Kling AI Avatar API](https://piapi.ai/kling-ai-avatar): Create AI avatars with Kling: facial animation, consistent characters, voice-driven motion, and avatar video generation. API docs, avatar features & model capabilities via PiAPI. Machine-readable guide: https://piapi.ai/kling-ai-avatar/llms.txt - [Kling AI Kiss Generator](https://piapi.ai/kling-ai-kiss): Create short AI kiss videos from photos using Kling Effects. Upload an image, generate realistic romantic kiss animations, and try the free demo before integrating via PiAPI. Machine-readable guide: https://piapi.ai/kling-ai-kiss/llms.txt - [Proposal Video Generator](https://piapi.ai/kling-ai-proposal): Create romantic AI videos from photos using Kling Effects. Generate proposal-style, cinematic, or affectionate image-to-video animations in a free playground, then integrate via PiAPI. Machine-readable guide: https://piapi.ai/kling-ai-proposal/llms.txt - [AI Squish Generator](https://piapi.ai/kling-ai-squish): Create squishy, elastic animation videos from photos using Kling AI. Upload an image, generate playful squash-and-stretch effects, and try the free demo on PiAPI. Machine-readable guide: https://piapi.ai/kling-ai-squish/llms.txt - [Kling API](https://piapi.ai/kling-api): Want to bring your users the Kling 1.0, 1.5 or 2.0 model from Kuaishou? Check out our Kling API to generate video content from texts, static images, or existing videos! Machine-readable guide: https://piapi.ai/kling-api/llms.txt - [Kling API V2](https://piapi.ai/kling-api/v2): Kling 2.0 Master - Bringing your video generation to the next level! Machine-readable guide: https://piapi.ai/kling-api-v2/llms.txt - [Kling API 2.1 Master](https://piapi.ai/kling-api/v2.1/master): Kling 2.1 Master - Unparalleled image and text to video generator with great prompt adherence Machine-readable guide: https://piapi.ai/kling-api-v2-1-master/llms.txt - [Kling API 2.1 Pro](https://piapi.ai/kling-api/v2.1/pro): Kling 2.1 Pro - superior video gneration with flexibility and precision! Machine-readable guide: https://piapi.ai/kling-api-v2-1-pro/llms.txt - [Kling API 2.1 Standard](https://piapi.ai/kling-api/v2.1/standard): Kling 2.1 Standard - Outstanding video generation with great value! Machine-readable guide: https://piapi.ai/kling-api-v2-1-standard/llms.txt - [Kling Effects API](https://piapi.ai/kling-effects-api): Create AI video effects like squish, expansion, birthday, and water effects in the Kling Effects playground. Integrate the Kling Effects API via PiAPI with simple POST calls. Machine-readable guide: https://piapi.ai/kling-effects-api/llms.txt - [Kling O1 API](https://piapi.ai/kling-o1): Use Kling O1 API via PiAPI to generate high-quality short-form videos. View API docs, pricing, free credits, and start integrating in minutes. Machine-readable guide: https://piapi.ai/kling-o1/llms.txt - [MiniMax H3 API](https://piapi.ai/minimax-h3): MiniMax H3 API generates MP4 video with native stereo audio from text prompts or a first-frame image through PiAPI. Machine-readable guide: https://piapi.ai/minimax-h3/llms.txt - [MMAudio API](https://piapi.ai/mmaudio-api): Try the MMAudio video-to-audio API in an interactive playground, then integrate it via PiAPI to transform silent videos into immersive audio experiences with perfectly matched, professional soundtracks. Machine-readable guide: https://piapi.ai/mmaudio-api/llms.txt - [Moshi API](https://piapi.ai/moshi-api): Looking to integrate Moshi into your application? Checkout out PiAPI's Moshi API! Machine-readable guide: https://piapi.ai/moshi-api/llms.txt - [Nano Banana 2 API](https://piapi.ai/nano-banana-2): Nano Banana 2 API via PiAPI provides high-quality AI image generation and image editing with flexible aspect ratios, resolutions, and output formats. Machine-readable guide: https://piapi.ai/nano-banana-2/llms.txt - [Nano Banana Pro API](https://piapi.ai/nano-banana-pro): Use the Nano Banana Pro API for high-quality image generation and editing. Transparent pricing from $0.105 per image, full API docs, and commercial usage via PiAPI. Machine-readable guide: https://piapi.ai/nano-banana-pro/llms.txt - [OmniAvatar API](https://piapi.ai/omniavatar): Generate realistic AI avatars & full-body videos with OmniAvatar API. Audio-driven, high-quality results. API docs, free trial & commercial licensing via PiAPI. Machine-readable guide: https://piapi.ai/omniavatar/llms.txt - [OmniHuman 1.5 API](https://piapi.ai/omnihuman-1-5): Create realistic AI human avatars and talking-head videos with OmniHuman 1.5. Audio-driven lipsync, full-body avatars, and API access via PiAPI. Try the playground or integrate via API. Machine-readable guide: https://piapi.ai/omnihuman-1-5/llms.txt - [Pixal3D API](https://piapi.ai/pixal3d-api): PiAPI is preparing Pixal3D API access for teams that want image-to-3D generation in apps, tools, and production pipelines. Machine-readable guide: https://piapi.ai/pixal3d-api/llms.txt - [Qwen Image API](https://piapi.ai/qwen-image): Qwen Image API provides multilingual text rendering (26+ languages) and precise image editing via PiAPI. Machine-readable guide: https://piapi.ai/qwen-image/llms.txt - [Seed Audio 1.0 API](https://piapi.ai/seed-audio-1-0-api): Create speech with BytePlus Seed Audio 1.0 via PiAPI, using permitted voice references plus speed, pitch, loudness, format, and sample-rate controls. Machine-readable guide: https://piapi.ai/seed-audio-1-0-api/llms.txt - [Seedance 2.0 API](https://piapi.ai/seedance-2-0): Use Seedance 2.0 API for text-to-video, first/last-frame, and omni-reference video generation. Compare $0.07-$0.50/sec pricing, docs, demo, and free credits. Machine-readable guide: https://piapi.ai/seedance-2-0/llms.txt - [Seedance 2.5 API](https://piapi.ai/seedance-2-5): Seedance 2.5 is a production video-generation model available through PiAPI with 480p, 720p, and 1080p output. Machine-readable guide: https://piapi.ai/seedance-2-5/llms.txt - [Seedream 5 Lite API](https://piapi.ai/seedream-5-lite): Powered by Bytedance, Seedream 5.0 Lite is a powerful image generation model that delivers high image quality up to 3K. Get started with the Seedream 5.0 API today! Machine-readable guide: https://piapi.ai/seedream-5-lite/llms.txt - [Seedream 5 Pro API](https://piapi.ai/seedream-5-pro): Generate premium 1K/2K images with reference image support via a single API call — no subscription, pay per image. Machine-readable guide: https://piapi.ai/seedream-5-pro/llms.txt - [Skin Tokens API](https://piapi.ai/skin-tokens-api): Skin Tokens is an automatic rigging API. Submit a GLB character mesh and receive a rigged GLB containing the original mesh, a fitted skeleton, and per-vertex skinning weights. Machine-readable guide: https://piapi.ai/skin-tokens-api/llms.txt - [SkyReels API](https://piapi.ai/skyreels): Generate human centric videos with 33 distinct facial expressions and 400 natural movement combinations, reflecting true human emotions in the output videos Machine-readable guide: https://piapi.ai/skyreels/llms.txt - [Sora 2 API](https://piapi.ai/sora-2): Experience OpenAI's Sora 2, the next generation of video generation with unprecedented realism, physics accuracy, and creative control. Access Sora 2 through PiAPI's powerful API. Machine-readable guide: https://piapi.ai/sora-2/llms.txt - [Trellis 2 API](https://piapi.ai/trellis-2-api): Generate 3D assets from text or images with the Trellis 2 API. View examples, pricing, and developer-ready API docs on PiAPI. Machine-readable guide: https://piapi.ai/trellis-2-api/llms.txt - [Trellis 2 Playground](https://piapi.ai/trellis-2-playground): Experiment with Trellis 2 API via PiAPI's universal playground. Upload an image and generate high-quality 3D assets using Trellis.2. Machine-readable guide: https://piapi.ai/trellis-2-playground/llms.txt - [Trellis 3D API](https://piapi.ai/trellis-3d-api): Want to use the best open source 3D generation model from Microsoft Research? Try Trellis 3D and Trellis API from PiAPI! Machine-readable guide: https://piapi.ai/trellis-3d-api/llms.txt - [Veo 3 API](https://piapi.ai/veo-3): Access Google Veo 3 via API on PiAPI. Generate cinematic videos with native audio and realistic motion. Docs, pricing, and examples for developers. Machine-readable guide: https://piapi.ai/veo-3/llms.txt - [Veo 3.1 API](https://piapi.ai/veo-3-1): Create cinematic videos with Veo 3.1 from Google, the latest Veo AI API built on the Veo 3 architecture. Experience sharper realism, smoother motion, and richer audio fidelity - or revisit Veo 3 for the earlier model. Machine-readable guide: https://piapi.ai/veo-3-1/llms.txt - [AI Video Background Remover](https://piapi.ai/video-remove-background): Remove video backgrounds instantly with PiAPI's AI background remover. Studio-quality cutouts without green screens or manual masking, plus a Background Remover API for automation. Machine-readable guide: https://piapi.ai/video-remove-background/llms.txt - [AI Video Watermark Remover](https://piapi.ai/video-remove-watermark): Remove watermarks from videos with PiAPI's AI watermark remover. Fast, accurate results and a developer-ready API for automation. Machine-readable guide: https://piapi.ai/video-remove-watermark/llms.txt - [AI Video Upscale Tool](https://piapi.ai/video-upscale-tool): Upscale and enhance videos instantly with PiAPI's Video Upscale API. Improve resolution and sharpen details so your videos look great everywhere. Machine-readable guide: https://piapi.ai/video-upscale-tool/llms.txt - [Kling Virtual Try-On](https://piapi.ai/virtual-try-on): Build an AI-powered virtual fitting room for your ecommerce store. Use PiAPI’s Virtual Try-On API to place garments onto model images and create realistic outfit previews with simple integration and pricing. Machine-readable guide: https://piapi.ai/virtual-try-on/llms.txt - [Wan 2.2 API](https://piapi.ai/wan/wan-2-2): Wan2.2 by Alibaba is a gamer in the Open Source Video Generation Space! Machine-readable guide: https://piapi.ai/wan-2-2/llms.txt - [Wan 2.6 API](https://piapi.ai/wan/wan-2-6): Access Wan 2.6 API for cinematic I2V and T2V video generation. See pricing, claim free credits, read docs, and start generating high-quality videos on PiAPI. Machine-readable guide: https://piapi.ai/wan-2-6/llms.txt - [Wanx API](https://piapi.ai/wanx): Wan 2.1 by Alibaba generating high-quality videos with significant leap forward in AI-driven visual content creation! Machine-readable guide: https://piapi.ai/wanx/llms.txt - [Z-Image API](https://piapi.ai/z-image-turbo): Powered by Alibaba's Tongyi-MAI, Z-Image Turbo API delivers ultra high-speed, high-fidelity AI image generation. Get started with Z-Image Turbo API now with PiAPI! Machine-readable guide: https://piapi.ai/z-image-turbo/llms.txt - [AI Advertisement Video Generator](https://piapi.ai/ai-advertisement-video-generator): Upload a product, add an optional actor or logo, write a script, and generate a campaign-ready advertisement with Seedance 2.5 or Kling 3.0. Machine-readable guide: https://piapi.ai/ai-advertisement-video-generator/llms.txt - [AI Age Filter](https://piapi.ai/ai-age-filter): AI Age Filter is a portrait image-editing workflow that creates an older or younger version while preserving recognizable identity. Machine-readable guide: https://piapi.ai/ai-age-filter/llms.txt - [AI Face Rater](https://piapi.ai/ai-face-rater): AI Face Rater is a selfie-to-dashboard workflow powered by GPT Image 2 image editing. Machine-readable guide: https://piapi.ai/ai-face-rater/llms.txt - [AI Headshot Generator](https://piapi.ai/ai-headshot-generator): AI Headshot Generator turns an uploaded portrait into a professional LinkedIn-ready headshot using GPT Image 2 image-to-image editing with outfit and backdrop presets. Machine-readable guide: https://piapi.ai/ai-headshot-generator/llms.txt - [AI Sports Video Generator](https://piapi.ai/ai-sports-video-generator): AI Sports Video Generator turns an uploaded photo into a Korean Baseball or FIFA World Cup fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Machine-readable guide: https://piapi.ai/ai-sports-video-generator/llms.txt - [Claymation AI Generator](https://piapi.ai/claymation-ai-generator): Claymation AI Generator creates clay-style images and clay AI videos from text, images, or uploaded video using Seedream 5 Lite, Kling 3.0, and Seedance 2.0 Fast. Machine-readable guide: https://piapi.ai/claymation-ai-generator/llms.txt - [FIFA World Cup AI Video Generator](https://piapi.ai/fifa-world-cup-ai-video-generator): FIFA World Cup AI Video Generator turns an uploaded portrait into a World Cup-inspired football fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Machine-readable guide: https://piapi.ai/fifa-world-cup-ai-video-generator/llms.txt - [Ghibli Style AI Generator](https://piapi.ai/ghibli-style-ai-generator): Ghibli Style AI Generator converts an uploaded photo into a Studio Ghibli-inspired anime illustration with Qwen Image Edit, then animates the result into a short cinematic scene with Kling 3 Omni. Machine-readable guide: https://piapi.ai/ghibli-style-ai-generator/llms.txt - [Korean Baseball AI Video Generator](https://piapi.ai/korean-baseball-ai-video-generator): Korean Baseball AI Video Generator turns an uploaded portrait into a Korean baseball-inspired stadium fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Machine-readable guide: https://piapi.ai/korean-baseball-ai-video-generator/llms.txt ## Guides - [Blog](https://piapi.ai/blogs): Product guides, API tutorials, and integration notes. - [Seedance 2.5 API: Fewer Restrictions, More Creative Freedom](https://piapi.ai/blogs/seedance-2-5-less-restriction): Learn what Seedance 2.5 Less Restriction changes, how reviewed face references fit the workflow, and how to start a policy-qualified API request. - [Bring Your Own Kling Account: API Access via PiAPI](https://piapi.ai/blogs/kling-subscription-api): Bring your existing Kling account into an API workflow with PiAPI Host-your-account. Explore seat pricing, account credits, and availability in the dashboard. - [MiniMax H3 Explained: Open-Weight Video Model](https://piapi.ai/blogs/minimax-h3-explained): Learn what MiniMax H3 is, why its open-weight ecosystem matters, what PiAPI supports, how much it costs, and where its video quality fits. - [Seedance 2.5 for AI Short Films: A 30-Second Storytelling Workflow](https://piapi.ai/blogs/seedance-2-5-ai-short-film): See three Seedance 2.5 short films, then learn a simple creative workflow for scripting dialogue, guiding characters, and shaping a complete 30-second scene. - [MiniMax H3 vs Seedance 2.0: Is Seedance Worth the Higher Price?](https://piapi.ai/blogs/minimax-h3-vs-seedance-2-0): Compare MiniMax H3's low-cost, open-weight approach with Seedance 2.0's premium video quality using matched PiAPI tests, pricing, and real outputs. - [How to Create Product Ads With the Seedance 2.5 API](https://piapi.ai/blogs/create-product-ads-seedance-2-5-api): Create Seedance 2.5 product ads with prepared references and reusable 9:16 prompts. See three original examples and the real cost per usable clip. - [How to Use the MiniMax H3 API: Text-to-Video and Image-to-Video Examples](https://piapi.ai/blogs/minimax-h3-api-guide): Use the MiniMax H3 API through PiAPI with text-to-video and image-to-video examples, native audio, pricing, parameters, polling, and troubleshooting. - [Seedance 2.5 vs Seedance 2.0: A PiAPI Video Comparison](https://piapi.ai/blogs/seedance-2-5-vs-seedance-2-0): Compare Seedance 2.5 vs Seedance 2.0 using paired PiAPI video tests. See quality, consistency, cost, API differences, and whether 2.5 is worth 3x more. - [5 Seedance 2.5 Prompts With Real Video Examples](https://piapi.ai/blogs/seedance-2-5-prompts-video-examples): See 5 Seedance 2.5 prompts with real video examples, PiAPI settings, reference inputs, API requests, output costs, and practical lessons for creators. - [Seedance 2.5 API Specifications on PiAPI](https://piapi.ai/blogs/seedance-2-5-api-specifications): Explore Seedance 2.5 API specifications on PiAPI: model ID, three modes, 1-30 second videos, 480p/720p pricing, reference limits, and access. - [How to Make AI Ad Videos: 3 Product-and-Actor Examples](https://piapi.ai/blogs/how-to-make-ai-ad-videos-product-examples): Learn how to make AI ad videos from product and actor images. Review three real outputs, scripts, formats, limitations, and a practical publishing checklist. - [How to Create a Professional Resume Photo With AI](https://piapi.ai/blogs/how-to-create-a-resume-photo-with-ai): Learn when to use a resume photo, how to choose the right outfit, background, crop, and file format, and how to review an AI-generated result. - [How to Use an Image Background Remover API](https://piapi.ai/blogs/how-to-use-image-background-remover-api): Learn how to use PiAPI's image background remover API with cURL: create a task, poll its status, download the result, and check transparent PNG quality. - [How to Make AI Dance Videos: Presets, Reference Videos, and Troubleshooting Tips](https://piapi.ai/blogs/ai-dance-video-guide): Learn how to make an AI dance video from one photo, choose between dance presets and reference videos, prepare better inputs, and troubleshoot unstable motion. - [Seedream 5 Pro vs Nano Banana Pro: Which Model Should You Use?](https://piapi.ai/blogs/seedream-5-pro-vs-nano-banana-pro): Compare Seedream 5 Pro vs Nano Banana Pro with matched playground examples, PiAPI pricing, text rendering, editing, and API differences. Find your best fit. - [How to Upscale Images and Increase Resolution](https://piapi.ai/blogs/how-to-upscale-images): Learn how to upscale images, increase resolution, choose between 2x, 4x, and 8x, and process repeat image workflows with PiAPI online. - [Seed Audio 1.0 Stress Test: How It Handles Difficult TTS Scripts](https://piapi.ai/blogs/seed-audio-1-0-tts-stress-test): Hear how Seed Audio 1.0 through PiAPI handles names, numbers, acronyms, punctuation, long scripts, and reference audio in our transparent TTS stress test. - [Seedream 5 Pro vs Seedream 5 Lite: Which Model Should You Use?](https://piapi.ai/blogs/seedream-5-pro-vs-seedream-5-lite): Seedream 5 Pro vs Seedream 5 Lite compared through real prompt tests, PiAPI pricing, resolutions, and API differences. See which model fits your workflow. - [Seedream 5 Pro API Examples: Multi-Reference Product Images, Text Rendering, and Precision Editing](https://piapi.ai/blogs/seedream-5-pro-api-guide): See Seedream 5 Pro API examples for multi-reference product images, accurate short text, and precision editing, with prompts, outputs, and a live playground. - [How to Use Seed Audio 1.0 API for AI Voice Generation](https://piapi.ai/blogs/how-to-use-seed-audio-1-0-api): Learn how to use Seed Audio 1.0 API through PiAPI: create tasks, poll results, add voice references, understand pricing, and test the generator. - [AI Video Ad Generator: How to Make Product Ads for TikTok, Reels, YouTube, and E-commerce](https://piapi.ai/blogs/ai-video-ad-generator-product-ads): Learn how to create AI product video ads from images for TikTok Shop, Instagram Reels, YouTube Shorts, Amazon, Etsy, eBay, and other ecommerce platforms. - [クレイアニメとは？AIでクレイアニメ風画像・動画を作る方法](https://piapi.ai/blogs/claymation-ai-japanese-guide): クレイアニメの意味や粘土アニメとの違い、AIで画像や動画をクレイアニメ風に変換する方法を解説。PiAPIで写真・動画を粘土風コンテンツにできます。 - [LinkedIn AI Headshot: How to Create a Professional Profile Photo with AI](https://piapi.ai/blogs/virtual-headshot-linkedin): Create a LinkedIn AI headshot from a clear portrait. Learn what photo to upload, which outfit and background to choose, and try PiAPI's AI headshot generator. - [Seedance 2 Real Face Tutorial: Generate Human Video Assets with PiAPI](https://piapi.ai/blogs/seedance-2-real-face-asset-tutorial): Learn how Seedance 2 real face workflows work, how to prepare human-face assets, when verification may be required, and how to generate realistic face videos with PiAPI. - [AI 외모 평가란? 얼굴 분석과 AI 얼굴 평가 점수 이해하기](https://piapi.ai/blogs/ai-face-rater-korean-guide): AI 외모 평가가 무엇인지, AI 안면 분석이 어떤 얼굴 특징을 보는지, AI 얼굴 평가 점수를 안전하게 해석하는 방법을 알아보세요. - [AI Age Filter Online: How to Make Yourself Look Older or Younger](https://piapi.ai/blogs/ai-age-filter-online): Use an AI age filter online to make yourself look older or younger. Learn how age progression works, what photo to upload, and how to try PiAPI's AI Age Filter. - [Seedance 2.5 API Is Live: Model, Pricing & Playground Guide](https://piapi.ai/blogs/seedance-2-5-ai-video-model): Seedance 2.5 is live on PiAPI. Explore its API task type, 4-15 second duration, 480p/720p/1080p pricing, generation modes, references, and playground. - [How to Create a Korean Baseball AI Trend Video From Your Photo](https://piapi.ai/blogs/korean-baseball-ai-trend-video): Use an AI sports video generator to turn a photo into a Korean baseball-style image, review it, then animate it into an AI baseball trend video. - [How to Create a FIFA World Cup AI Video From Your Photo](https://piapi.ai/blogs/fifa-world-cup-ai-video-generator): Create a FIFA World Cup-style AI video from your photo. Learn the photo-to-fan-cam image workflow, video animation step, prompt tips, and how to try it on PiAPI. - [How to Use Seedance Private Assets with the Seedance API](https://piapi.ai/blogs/seedance-private-assets-api): Learn how Seedance private assets work in the Seedance API. Upload reusable face, character, or product references and generate videos with asset IDs. - [AI Kissing Video from Image: Examples of What Different Photos Generate](https://piapi.ai/blogs/ai-kiss-video-examples): See AI kissing video examples from different image types, including couple photos, character images, wedding portraits, casual selfies, and low-light beach photos. - [Why Your AI Face Rating Changes: Photo Tips for More Consistent Face Scores](https://piapi.ai/blogs/why-ai-face-rating-changes): AI face ratings can change based on lighting, camera angle, expression, filters, blur, and face visibility. Learn the best selfie tips for more consistent AI face scores. - [AI Face Rating Explained: What Face Scores and PSL Ratings Mean](https://piapi.ai/blogs/ai-face-rating-guide): Learn what AI face rating means, how PSL face scores became a social-media trend, what face rating AI tools measure, and how to interpret scores safely. - [New Working Dance Presets for PiAPI's Kling 2.6 Dance Generator](https://piapi.ai/blogs/kling-2-6-dance-presets): PiAPI's Kling 2.6 Dance Generator now includes new working dance presets while still supporting custom reference video uploads for your own motion style. - [How to Consistently Animate Ghibli-Style AI Images Into Cinematic Videos](https://piapi.ai/blogs/ghibli-ai-image-to-video): Learn a Ghibli image-to-video workflow for consistent AI videos: stylize your photo first, then animate the still image with simple motion prompts. - [Ghibli Style Images: Convert Photos With AI](https://piapi.ai/blogs/ghibli-style-images): Upload a photo and turn it into Ghibli-style AI art with PiAPI's preset playground. Learn source-photo tips, edit notes, examples, and fixes. - [Clay Animation Maker: Create Clay-Style Videos and Images with AI](https://piapi.ai/blogs/clay-animation-maker): Use an AI clay animation maker to create clay-style videos from prompts, transform existing footage, and generate claymation-style images from text or uploaded images. - [Best Free AI Dance Generator to Create Dance Videos Online](https://piapi.ai/blogs/best-ai-dance-generator): Use an AI dance generator to turn a photo into a dance video online. Learn how it works, see one example, and try PiAPI's AI dance demo with 0.5 free signup credits. - [Best Claymation AI Generator for Images and Videos](https://piapi.ai/blogs/best-claymation-ai-generator): Looking for a claymation AI generator? Learn how to create clay-style images and clay AI videos from text or uploaded images with PiAPI's claymation playground. - [시댄스 2.0으로 광고 영상과 숏폼 마케팅 영상 만드는 방법](https://piapi.ai/blogs/seedance-2-0-ai-marketing-video): 시댄스 2.0으로 제품 광고, 숏폼 마케팅 영상, 앱 소개 영상 등을 만드는 방법을 알아보세요. 이미지-to-video 워크플로우, 프롬프트 예시, 가격/크레딧 확인 방법까지 정리했습니다. - [How to Generate AI Images From the Terminal With an AI CLI Tool](https://piapi.ai/blogs/generate-ai-images-from-terminal-command-line-ai-tool): Learn how to use a command line AI tool to generate AI images from your terminal, save outputs locally, batch prompts, and automate image workflows with PiAPI CLI. - [Kling 3 vs Sora 2: Which AI Video Model Should You Use?](https://piapi.ai/blogs/kling-3-vs-sora-2): Compare Kling 3 vs Sora 2 for AI video generation, API access, pricing, prompt following, motion quality, and production workflows. Test both models in one playground. - [PiAPI CLI Quick Start: Generate AI Images and Videos from Your Terminal](https://piapi.ai/blogs/piapi-cli-quick-start): Learn how to install PiAPI CLI, authenticate with your API key, and generate AI images or videos from your terminal using PiAPI's multimodal AI models. - [Why Your AI Kiss Generator Video Fails and How to Fix It](https://piapi.ai/blogs/ai-kiss-generator-not-working): Learn why AI kiss generator videos fail, look blurry, or appear distorted. Fix input image issues and create better Kling AI Kiss videos with PiAPI. - [AI Kiss Generator Guide: How to Create Kling AI Kiss Videos](https://piapi.ai/blogs/ai-kiss-generator): Learn what an AI kiss generator is, how Kling AI Kiss turns photos into short kissing videos, and how to try the Kling kiss generator on PiAPI. - [GPT Image 2 vs Nano Banana 2.0: The Ultimate AI Image Generation Showdown](https://piapi.ai/blogs/gpt-image-2-vs-nano-banana-2-0): Compare Nano Banana 2.0 vs GPT Image 2 in image quality, prompt accuracy, realism, pricing, and API usage. See real examples, prompt tests, and performance comparisons. - [GPT Image 2 vs GPT Image 1.5 API: What's New in OpenAI Image Generation?](https://piapi.ai/blogs/gpt-image-2-vs-gpt-image-1-5-api): Compare GPT Image 2 vs GPT Image 1.5 API. Discover new features, image quality upgrades, pricing differences, prompt tips, and why GPT Image 2 is the latest choice for AI image generation. - [시댄스 2.0 vs 베오 3.1 비교: 어떤 영상 생성 AI가 더 좋을까?](https://piapi.ai/blogs/seedance-2-vs-veo-3-1-comparison): 시댄스 2.0과 베오 3.1을 비교하여 영상 품질, 가격, API 사용성을 한눈에 확인해보세요. 어떤 AI 영상 생성 모델이 나에게 적합한지 알아보세요. - [클링 AI 3.0 vs 시댄스 비교: 어떤 AI 영상 생성기가 더 좋을까? (2026)](https://piapi.ai/blogs/kling-3-0-vs-seedance-comparison): 클링 AI 3.0와 시댄스를 비교해 화질, 가격, 생성 속도, 사용성, API 지원까지 한눈에 확인하세요. 어떤 AI 영상 생성기가 더 적합한지 알아보세요. - [GPT Image 2 API Guide: Features, Prompt Tips, Pricing, and Examples](https://piapi.ai/blogs/gpt-image-2-api-guide): Learn how GPT Image 2 works, explore its key features, prompt tips, pricing, and real examples for production-ready AI image generation. - [시댄스 2.0 완벽 가이드: 사용법, 가격, API, 영상 생성 예시까지](https://piapi.ai/blogs/seedance-2-0-how-to-pricing-api-guide): 시댄스 2.0 사용법, 가격, API까지 한 번에 정리. AI 영상 제작을 위한 Seedance 기능, 생성 모드, 실제 예시와 평가까지 확인해보세요. - [Seedream 5 vs Nano Banana 2 (2026): Which AI Model Is Actually Better?](https://piapi.ai/blogs/seedream-5-vs-nano-banana-2): Tested Seedream 5 vs Nano Banana 2 across quality, speed, API, and pricing. See real examples, key differences, and which AI model you should use in 2026. - [Acestep Audio T2A Review (2026): Production-Ready AI Music or Not?](https://piapi.ai/blogs/acestep-audio-review-production-ready-ai-music): Tested Acestep Audio for AI music generation. See real examples, audio quality, and whether it can produce production-ready tracks in 2026. - [Kling AI Avatar: Full Guide with Examples (Standard vs Pro Quality)](https://piapi.ai/blogs/kling-ai-avatar-guide-standard-vs-pro): Learn how Kling AI Avatar works with real examples and a clear Standard vs Pro quality comparison. Discover features, use cases, and how to choose the right output for production. - [Kling O1 API Guide: How to Use Kling AI API for Cinematic Video Generation](https://piapi.ai/blogs/kling-o1-api-guide): Learn how to use the Kling O1 API for AI video generation. Explore Kling AI API features, pricing, documentation, and prompt examples for cinematic workflows. - [GPT Image 1.5 API Guide: Features, Pricing, and Prompt Examples](https://piapi.ai/blogs/gpt-image-1-5-api-guide): What is GPT Image 1.5? Explore features, pricing, and how to use the gpt-image-1.5 API for high-quality AI image generation. - [Hailuo vs Kling 2.6: Speed or Realism Which AI Video Model Actually Wins?](https://piapi.ai/blogs/hailuo-vs-kling-2-6): Compare Hailuo vs Kling 2.6 for AI video generation. We test motion realism, prompt adherence, and output consistency to see which model actually performs better in real-world use. - [Omnihuman 1.5 API Guide: How to Use ByteDance’s AI Human Video Model](https://piapi.ai/blogs/omnihuman-1-5-api-guide): Learn how to use the Omnihuman 1.5 API to generate realistic AI human videos. This guide covers key features, API workflow, and how to integrate ByteDance’s Omnihuman into production. - [DiffRhythm AI Guide: Music Generation API, Features, and Prompt Examples](https://piapi.ai/blogs/diffrhythm-ai-guide): Learn how DiffRhythm AI works for music generation. Explore features, prompt examples, and how to use DiffRhythm for structured audio creation. - [Hunyuan AI vs Seedance 2.0: Which Image-to-Video Model Is Better in 2026?](https://piapi.ai/blogs/hunyuan-ai-vs-seedance-2-0-image-to-video): Compare Hunyuan AI vs Seedance 2.0 for image-to-video generation. Explore differences in motion realism, consistency, and artifacts to find the best model for production use. - [Using Veo 3.1 in 2026: Complete Guide to API, Pricing, and Prompting](https://piapi.ai/blogs/veo-3-1-api-pricing-prompting-guide-2026): Learn how to use Veo 3.1 API with pricing, fast mode, and prompting guide. Generate high-quality text-to-video and image-to-video with Google Veo 3.1. - [Wan 2.6 vs Kling 2.6: Which AI Video Model Is Better for Production in 2026?](https://piapi.ai/blogs/wan-2-6-vs-kling-2-6): Compare Wan 2.6 vs Kling 2.6 for AI video generation. See differences in realism, motion consistency, prompt adherence, and which model is better for production workflows in 2026. - [FramePack API Guide: What is FramePack AI? How to Use FramePack for AI Video Extension](https://piapi.ai/blogs/framepack-ai-video-guide): Learn what FramePack AI is and how to use it for AI video extension. Includes FramePack tutorial and examples. - [Qwen AI vs Nano Banana 2: Which AI Model API Is Better in 2026?](https://piapi.ai/blogs/qwen-ai-vs-nano-banana-2-api-comparison): Compare Qwen AI vs Nano Banana 2 for image generation. Explore API features, pricing, prompt tips, and real examples to choose the best model in 2026. - [Trellis vs Trellis 2: Which 3D Generation API Should You Use?](https://piapi.ai/blogs/trellis-2-vs-trellis-3d-generation-api): Explore Trellis vs Trellis 2, including 3D generation API capabilities, mesh quality, texture consistency, and use cases like 3D product visualization. - [Dreamina Seedance 2.0 vs Sora 2: Full Comparison of Video Quality, Pricing, and Prompts](https://piapi.ai/blogs/dreamina-seedance-2-0-vs-sora-2): Dreamina Seedance 2.0 vs Sora 2 comparison covering video quality, Seedance pricing, Sora AI pricing, prompts, and performance. See which model fits your needs. - [GPT Image 1.5 vs Nano Banana 2: Which Is Better? (Full Comparison)](https://piapi.ai/blogs/gpt-image-1-5-vs-nano-banana-2-api-comparison): Compare GPT Image 1.5 vs Nano Banana 2. See key differences, output quality, and which model is better for your use case. - [Nano Banana Pro vs Nano Banana 2: Google Nano Banana API Comparison with Examples](https://piapi.ai/blogs/nano-banana-2-vs-nano-banana-pro): Nano Banana Pro vs Nano Banana 2: Google Nano Banana API Comparison and Image Quality Analysis - [Seedream 5.0 API Guide: How to Use + Quick Start Example](https://piapi.ai/blogs/seedream-5-0-api-guide): Explore the Seedream 5.0 API by ByteDance. Learn how this AI image generation model works, how to write effective prompts, and how to integrate the Seedream API for scalable image automation. - [GPT Image 1 vs 1.5 Pricing: Cost Per Image Compared (2026)](https://piapi.ai/blogs/gpt-image-1-5-vs-gpt-image-1-api-2026): Compare GPT Image 1.5 vs GPT Image 1 with insights on image quality, prompt adherence, generation stability, and API pricing. - [Nano Banana 2 API (2026): Docs, Pricing, Integration & Code Examples](https://piapi.ai/blogs/nano-banana-2-api-guide): Use Nano Banana 2 API with ready-to-use code examples, request formats, and integration steps. Quick developer guide to start building with PiAPI in minutes. - [Nano Banana vs Nano Banana Pro: Google Nano Banana API Comparison and Pricing Guide](https://piapi.ai/blogs/nano-banana-vs-nano-banana-pro): Compare Nano Banana vs Nano Banana Pro through the Google Nano Banana API. Learn about Nano Banana API pricing, prompt examples, and how to use Nano Banana Pro. - [Seedance 2.0 API Guide: ByteDance AI Video Model and Prompt Examples](https://piapi.ai/blogs/seedance-2-0-api-guide): Explore the Seedance 2.0 API by ByteDance. Learn how the Seedance AI video generator works, how to write Seedance prompts, and how to integrate the Seedance AI API. - [Sora 2 API Guide: Prompt Guide, Examples and Alternatives](https://piapi.ai/blogs/sora-2-api-text-to-video-generation): Explore the Sora 2 API for text-to-video generation. Learn its capabilities, workflow, prompts and examples for cinematic AI video generation. - [OmniHuman 1.5 vs Kling AI Avatar: Which AI Avatar Model Performs Better in 2026?](https://piapi.ai/blogs/omnihuman-1-5-vs-kling-ai-avatar): Compare OmniHuman 1.5 and Kling AI Avatar across lip-sync accuracy, facial realism, motion stability, and avatar quality through controlled testing. - [Kling 3.0 vs Kling 3.0 Omni: Which Model Is Better for AI Video Generation?](https://piapi.ai/blogs/kling-3-0-vs-kling-3-0-omni-video-quality): Compare Kling 3.0 and Kling 3.0 Omni across video quality, motion realism, and production use cases to decide which AI video model performs better. - [Kling 3.0 vs Kling 2.6: Latest Kling AI Version Comparison (2026)](https://piapi.ai/blogs/kling-3-0-api-vs-kling-2-6-api-2026): Compare Kling 3.0 and Kling 2.6 APIs in 2026 across prompt adherence, realism, resolution, artifacts, control, and generation speed. Learn which Kling AI API fits your workflow. - [Creating Educational & Infographic Visuals with Z-Image Turbo API: A Lightweight AI Image Generation Guide with Examples](https://piapi.ai/blogs/z-image-turbo-api-educational-infographic-visuals): Create educational visuals and infographic posters using Z-Image Turbo API. Learn features, use cases, and example prompts with this lightweight AI image model. - [Qwen Image API Prompting Guide: Multilingual Prompts & Best Practices](https://piapi.ai/blogs/creating-in-your-own-language-a-practical-guide-to-qwen-image-api-26-langauge-support): Explore Qwen Image AI and Qwen Image API in action. See how we use Qwen AI’s 26+ language support with PiAPI to turn multilingual prompts into production-ready visuals. - [Google Veo 3 Prompt Guide: Best Practices, Templates & Example Prompts](https://piapi.ai/blogs/veo-3-api-step-by-step-guide-to-writing-good-prompts-using-google-api-best-practices): 1. Learn how to write effective prompts for Google Veo 3 video generation. Explore best practices, structured prompt templates, and example prompts to improve AI video results. - [Wan 2.5 vs Wan 2.2: Which Wan AI API Is Better for Production in 2026?](https://piapi.ai/blogs/wan-2-5-api-vs-wan-2-2-api-choosing-the-right-wan-ai-api-for-production-grade-video-generation): Compare Wan 2.5 vs Wan 2.2 for AI video generation. See key feature differences, performance improvements, and which Wan model is recommended for production workflows in 2026. - [Nano Banana WINS over Flux Kontext: AI Image Editing Showdown](https://piapi.ai/blogs/nano-banana-flux-kontext-ai-image-editing-comparison): Compare Nano Banana API and Flux Kontext API for AI image generation. See pros, cons, pricing, and which fits your workflow. Test both instantly in PiAPI’s Free Playground — no code required. - [Nano Banana API vs Flux Kontext API [2025]: Pricing, Speed & Free Playground](https://piapi.ai/blogs/nano-banana-api-vs-flux-kontext-api-2025): Compare Nano Banana API and Flux Kontext API for AI image generation. See pros, cons, pricing, and which fits your workflow. Test both instantly in PiAPI’s Free Playground — no code required. - [The Best TTS API in 2025: f5 TTS API](https://piapi.ai/blogs/the-best-tts-api-in-2025-f5-tts-api): Discover the best TTS API in 2025 with f5-TTS. Get lifelike voice synthesis, zero-shot cloning, multi-language support, and pay-as-you-go pricing with PiAPI’s developer-friendly text to speech API. - [SkyReels API — Create AI Videos Seamlessly with PiAPI](https://piapi.ai/blogs/skyreels-api-create-ai-videos-seamlessly-with-piapi): SkyReels API brings human-centric AI video to life with cinematic quality and realistic motion. Try SkyReels API on PiAPI with free credits today. - [How to Automate Glass Fruit Cutting Videos with Make.com and Kling API](https://piapi.ai/blogs/how-to-automate-glass-fruit-cutting-videos-with-make-com-and-kling-api): Automate daily ASMR videos with Make.com and PiAPI’s Kling API. Start with the viral glass fruit cutting prompt, then scale into a self-running Shorts and Reels channel. No code needed—just prompts, automation, and PiAPI’s 20+ AI models. - [Kling API Pricing, Features, and Documentation — Everything You Need to Know (2025)](https://piapi.ai/blogs/kling-api-pricing-features-documentation): Discover Kling AI pricing, features, and API docs. Learn how PiAPI simplifies Kling API integration for creators and developers. - [Automate Midjourney & Kling Videos in n8n — Free PiAPI Workflow Template (2025)](https://piapi.ai/blogs/midjourney-n8n-integration-guide): Automate short videos using GPT-4o, Midjourney, and Kling APIs via PiAPI’s free n8n workflow. Step-by-step integration guide for developers and creators. - [FREE Nano Banana API Pricing & Key Access (2025) – Google Gemini 2.5 Flash via PiAPI](https://piapi.ai/blogs/free-nano-banana-api-pricing-and-key-access-2025-google-gemini-2-5-flash-via-piapi): Get free Nano Banana API credits via PiAPI. Simple $0.03/image pricing, free API key, and Gemini 2.5 Flash Image integration — perfect for creators and businesses. - [Nano Banana API Documentation & Pricing (2025) — Try Gemini 2.5 Flash via PiAPI](https://piapi.ai/blogs/what-is-nano-banana-google-gemini-secret-editing-model-explained): Discover Nano Banana — Google Gemini 2.5 Flash for image generation and editing. Try the free API via PiAPI to create stunning visuals instantly. - [Save 60% Off With PiAPI Luma Dream Machine API (2025) — Free vs Paid Plans Compared](https://piapi.ai/blogs/luma-dream-machine-api-pricing): Compare Luma Dream Machine pricing (2025) with free vs paid plans via PiAPI. See pricing, features, API costs, and a Fal.ai comparison to choose the best plan for your AI video projects. - [Flux AI Image Editing Capabilities (2025): Flux Playground, Kontext API & Pro Models](https://piapi.ai/blogs/flux-kontext-ai-image-editing): Discover Flux Kontext by Black Forest Labs — the most intuitive AI image generator. Edit with Flux AI Playground or scale via Flux AI image editing capabilities API. - [How to Build Fortnite-Style Game Assets with Trellis 3D API via PiAPI](https://piapi.ai/blogs/how-to-build-fortnite-style-game-assets-with-trellis-3d-api-via-piapi): Learn how to create Fortnite-style game assets using the Trellis 3D API with PiAPI. This historical guide uses a Midjourney example; PiAPI no longer provides that service. - [From Image to 3D Models: How Trellis is Changing the Game](https://piapi.ai/blogs/from-image-to-3d-models-how-trellis-is-changing-the-game): Discover how Microsoft’s Trellis, available via PiAPI, turns images into 3D models in minutes. Learn how image-to-3D technology accelerates game design, AR/VR, e-commerce, and more with fast, scalable workflows. - [Building Open-World Game Assets with Hunyuan Video AI — A GTA-Style Example](https://piapi.ai/blogs/building-open-world-game-assets-with-hunyuan-video-ai-a-gta-style-example): Discover how Hunyuan Video AI, powered by Tencent and available via PiAPI, helps developers generate consistent GTA-style game assets — from characters and vehicles to immersive city environments. - [The Future of Hailuo AI: Storytelling, Ads & E-Commerce Made Easy](https://piapi.ai/blogs/hailuo-ai-video-storytelling-ads-ecommerce): Discover how Hailuo AI empowers creators to make cinematic videos, engaging ads, and shoppable e-commerce content. Learn how to start in the playground and scale with the API. - [The Rise of Video AI: Why Models Like Hailuo AI Are Leading the Next Wave](https://piapi.ai/blogs/the-rise-of-video-ai-why-models-like-hailuo-ai-are-leading-the-next-wave): Discover Hailuo AI by Minimax — the next wave of video AI. From text-to-video to Hailuo 2’s 1080p generation, see why creators and developers are switching. - [The Best Face Swap API — PiAPI’s Fast, Reliable, and Affordable Solution](https://piapi.ai/blogs/the-best-face-swap-api-piapi-s-fast-reliable-and-affordable-solution): Discover PiAPI Faceswap API — the fast, reliable, and cost-effective AI face swap solution for images, videos, and multi-face projects. Easy integration, low latency, bulk support, and free credits to get started. - [Wanx 2.1 vs Wan 2.2: A Logo Transformation Ad Comparison](https://piapi.ai/blogs/wan-2-1-vs-wan-2-2-comparison-logo-transformation): Compare Wan 2.1 API vs Wan 2.2 API with a cinematic McDonald’s logo transformation. See how each model handles structured logo animations, fries, and Big Mac hero shots. Explore PiAPI’s Playground and try it yourself! - [Luma Dream Machine API Documentation (2025): Complete Guide to Docs, Prompts & Updates](https://piapi.ai/blogs/luma-dream-machine-api-documentation-2025-complete-guide-to-docs-prompts-and-updates): Looking for the official Luma Dream Machine API documentation in 2025? Here’s your complete guide to the docs, prompt guide, free plan details, and developer resources. - [Luma Dream Machine API Pricing (2025) — Free Plan & Features via PiAPI](https://piapi.ai/blogs/luma-ai-dream-machine-intro): Compare Luma Dream Machine’s Free, Pro, and Enterprise plans for 2025. Explore API pricing, features, and release updates — plus how to get started instantly via PiAPI. - [Flux Kontext API (2025): Features, LoRA Training & Pricing via PiAPI](https://piapi.ai/blogs/flux-ai-pro-kontext-api): Discover Flux AI Pro and Kontext API via PiAPI — fast, prompt-accurate image generation with LoRA training, Max presets, and enterprise-ready automation. - [Flux AI Generator Tutorial (2025) — Learn LoRA, Kontext & Step-by-Step Image Editing](https://piapi.ai/blogs/flux-ai-generator-step-by-step-guide): Master Flux AI Generator in 2025: step-by-step guide to LoRA training, Kontext editing, and API workflows. See examples, prompts, and use cases for marketers & developers. - [Cinematic AI at Your Fingertips — Wan 2.2 via PiAPI](https://piapi.ai/blogs/create-cinematic-ai-videos-wan-ai-piapi): Create stunning cinematic AI videos using Wan 2.2 — the open-source video generator from Alibaba. Learn how to use the Wan API via PiAPI. - [The Hype Around ChatGPT Agent Mode And What Developers Need to Know](https://piapi.ai/blogs/chatgpt-agent-mode-piapi-alternative): ChatGPT Agent Mode is trending—but what does it mean for devs? Here's a breakdown and how PiAPI users can still build custom AI workflows without it. - [Midjourney Loop Video Update (Aug 2025)](https://piapi.ai/blogs/midjourney-update-august-2025-video-loops-start-end-frames-and-api-access): Discover Midjourney’s August 2025 update: new video loop feature, start-end frame control, API access via PiAPI, and pricing updates. Full tutorial with examples. - [Kling AI Censorship Explained (2025): Why Developers Switch to PiAPI for Creative Freedom](https://piapi.ai/blogs/is-kling-too-restrictive-why-developers-prefer-piapi-s-unified-model-access-for-ai-video-gen): Frustrated by Kling’s restrictions? Discover why developers are switching to PiAPI for unrestricted AI video generation—with access to models like GPT-4o and Luma. - [Build Viral AI Videos with Kling + GPT-4o via PiAPI](https://piapi.ai/blogs/create-viral-ai-videos-with-piapi-s-kling-and-gpt-4o-apis): Access Kling, GPT-4o, and more via one API. Learn how developers use PiAPI to build custom AI video workflows—no platform limits, just full model control. - [Glass Fruit Cutting ASMR AI Prompt (How I Made a Video Without Google Veo 3)](https://piapi.ai/blogs/how-i-made-a-glass-fruit-cutting-ai-asmr-video-without-google-veo-3): Create viral ASMR glass fruit cutting videos using AI — without Google Veo 3. Here's how we used PiAPI, Kling, and MMAudio to build it step-by-step. - [Ghibli Style Image Generator using GPT 4o image generation API](https://piapi.ai/blogs/ghibli-style-image-generator-using-gpt-4o-image-generation-api): In this blog we will be using out GPT 4o image generation API to replicate the Ghibli Style, creating a Ghibli image generator! - [Creating an AI Music Video Generator using Wan 2.1](https://piapi.ai/blogs/creating-an-ai-music-video-generator-using-wan-2-1): In this blog we are going to create an AI Music Video Generator using Wan 2.1 - [Using Kling API to animate toys like Popal](https://piapi.ai/blogs/using-kling-api-to-animate-toys-like-popal): In this blog we will be using Kling API to animate toys like Popal - [Kling API vs Pika API (Kling Effects vs Pikaffects)](https://piapi.ai/blogs/kling-api-vs-pika-api): In this blog we will be comparing Kling's Kling Effects with Pika Art's Pikaffects! - [Bring Historical Figures to life using AI like Kling API](https://piapi.ai/blogs/bring-historical-figures-to-life-using-ai-like-kling-api): In this blog we use Kling API to bring historical figures to life! - [3D Trellis API through PiAPI](https://piapi.ai/blogs/3d-trellis-api-through-piapi): PiAPI has recently launched our very own 3D Trellis API! - [Kling Elements through Kling API](https://piapi.ai/blogs/kling-elements-through-kling-api): Using the Kling Elements Feature through Kling API - [Kling 1.6 Model through Kling API](https://piapi.ai/blogs/kling-1-6-model-through-kling-api): Use the Kling 1.6 API through PiAPI, in this blog we will compare the differences between Kling 1.5 and Kling 1.6 - [Sora API vs Kling API - a comparison of AI video generation Models](https://piapi.ai/blogs/sora-api-vs-kling-api-a-comparison-of-ai-video-generation-models): OpenAI's Sora just released, and PiAPI is investigating whether or not we want to launch Sora API, as part of this investigation we will be comparing Sora API to Kling API - [OpenAI's Realtime API (powering ChatGPT Advanced Voice Mode) vs Moshi API](https://piapi.ai/blogs/openai-realtime-api-vs-moshi-api): A detail comparison between OpenAI's Realtime API vs Moshi API - for conversation AI applications! - [How PiAPI's Kling AI API is better than the Official Kling AI API (cost, speed, and features)!](https://piapi.ai/blogs/how-piapi-s-kling-api-is-better-than-the-official-kling-api-cost-speed-and-additional-features): In this blog we discuss on how PiAPI's Kling AI API is better than the Official Kling AI API in terms of cost, speed, and features! - [Crush it Melt it through Pika API , Kling API & Luma API](https://piapi.ai/blogs/crush-it-melt-it-through-pika-api-kling-api-and-luma-api): In this blog we will be exploring the new Crush It Melt It trend and see if Luma API and Kling API can do as well as Pika API! - [The Motion Brush Feature through Kling API](https://piapi.ai/blogs/the-motion-brush-feature-through-kling-api): Interested about the newly released Kling Motion Brush Feature? Want to use Motion Brush through Kling API? Check out our comparison blog for more! - [Using Kling API & Midjourney API to Create an Animated Custom Wallpaper](https://piapi.ai/blogs/using-kling-api-and-midjourney-api-to-create-an-animated-custom-wallpaper): Using Kling API and Midjourney API to create visually stunning wallpapers! For example an anime wallpaper, a wallpaper of the character Yoshii Toragana from Shogun, and a Warhammer 40k Wallpaper. - [Kling 1.5 vs 1.0 - A Comparison through Kling API](https://piapi.ai/blogs/kling-1-5-vs-1-0-a-comparison-through-kling-api): A detailed comparison between Kling 1.5 and it's predecessor Kling 1.0, using Kling API to generate the videos compared. - [Luma API & Midjourney API - Exploring the Marvel Multiverse](https://piapi.ai/blogs/luma-api-and-midjourney-api-exploring-the-marvel-multiverse): Using Dream Machine API and Midjourney API we explored what famous marvel characters would look like if they were played by different actors. Such as a Tom Cruise Iron Man, a Henry Cavill Wolverine, and finally RDJ Doom! - [Midjourney API's Auto-CAPTCHA-Solver](https://piapi.ai/blogs/midjourney-api-auto-captcha-solver): An article introducing PiAPI's Auto-CAPTCHA-Solver to streamline workflow and improve efficiency - [Luma's Dream Machine 1.6 - Testing Camera Motion Feature through Luma API!](https://piapi.ai/blogs/luma-dream-machine-1-6-testing-camera-motion-feature-through-luma-api): Introducing the new Camera Motion Feature from Luma's Dream Machine 1.6 ! - [Luma Dream Machine vs Kling - A brief comparsion using APIs](https://piapi.ai/blogs/luma-dream-machine-vs-kling-a-brief-comparsion-using-apis): Comparing the same-prompt-outputs of Luma's Dream Machine and Kuaishou's Kling, results are generated using their respective APIs - [Luma Dream Machine 1.5 vs 1.0 - Comparison through Luma API](https://piapi.ai/blogs/luma-dream-machine-1-5-vs-1-0-comparison-through-luma-api): A detailed comparison between Luma's Dream Machine version 1.5 compared to previous version, using the Luma API to generate videos compared - [Midjourey V6.1 through Midjourney API](https://piapi.ai/blogs/midjourey-v6-1-through-midjourney-api): An intro to the new Midjourney V6.1 model, acccessing it through Midjourney API, and the comparison results against previous models. - [How to integrate Midjourney API into Zapier](https://piapi.ai/blogs/how-to-integrate-midjourney-api-into-zapier): Want to add Midjourney's image generation capability into your automated Zapier workflow? Check out this tutorial on how to do so using the Midjourney API! - [How to use Faceswap API from PiAPI with Postman](https://piapi.ai/blogs/how-to-use-faceswap-api-from-piapi-with-postman): Interested in using Faceswap API but don't know how to do it? Check out this tutorial on how to use Faceswap API with Postman and create your favourite face swapped images! ## Resources - [API documentation](https://piapi.ai/docs): API reference and integration documentation. - [Pricing](https://piapi.ai/pricing): Current pay-as-you-go plans and rates. - [CLI](https://piapi.ai/cli): Install the PiAPI command-line client. # Full product and guide details # Ace-Step API via PiAPI > ACE Step is the most innovative AI model designed to transform music generation. Bridging the gap between speed, musical coherence, and control, ACE-Step sets a new standard in music AI, creating music that empowers artists and creators to explore new realms of music gneration. Landing page: https://piapi.ai/ace-step Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/ace-step-api/text-to-audio ## Product details - Category: audio - Vendor: piapi ### Related entities - Ace-Step API - PiAPI - PiAPI ## FAQ ### What types of music can ACE Step generate? ACEstep can generate a wide range of instrumental and vocal music across different genres and styles. It excels in creating realistic instrumental tracks with diverse timbres and expressions, maintaining musical coherence in complex arrangements. Additionally, ACE-Step is capable of producing vocal samples tailored to various genres, offering users the flexibility to explore music production in styles ranging from classical and jazz to pop, rock, electronic, and rap. ### How is ACE-Step different to other music AI models? ACE-Step adopts diffusion-based generation with architectural components such as DCAE and linear transformers, providing fast synthesis while maintaining musical coherence. ### How can I control specific features of the music generated by ACE Step? ACE-Step offers several control mechanisms include voice cloning, lyric editing, and remixing. Additionally, its training-free applications allow for easy modifications and variations of generated content. ### Does ACE Step support multilingual lyric generation? Yes, ACE Step supports lyric generation in 19 languages, with the highest performance in 10 widely-used languages. However, less common languages may experience performance inconsistencies. ### Can I use Ace-step to generate songs commercially? Yes, ACE-Step is an open-source model that can be used for commercial music generation, providing artists and producers the tools they need to create and monetize music projects. However, it's important to check the licensing terms and ensure compliance with any applicable regulations or restrictions regarding its use. ### Do you offer refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you are given free credits to try our ACE Step Model (specifically the "Pay-as-you-go" service) before making payments! # AI Hug Generator via PiAPI > Use our hug AI generator to turn any photo of two people into a hugging video in minutes. Try the free AI hug video generator playground or scale with the hugging AI API via PiAPI. Landing page: https://piapi.ai/ai-hug Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/ai-hug-api/create-task ## Product details - Category: video - Vendor: piapi ### Related entities - AI Hug Generator - piapi - PiAPI # Claude Fable 5 API via PiAPI > Claude Fable 5 API - Advanced AI with breakthrough autonomous execution, coding, and vision capabilities. Work for hours without intervention. Pay-as-you-go. Landing page: https://piapi.ai/claude-fable-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: llm - Vendor: piapi ### Facts - Long-Horizon Autonomous Execution.: Fable 5 can work for hours or days without human intervention, planning its approach, checking progress against goals, and refining as it goes. Hand off entire projects and review the finished work. - Advanced Software Engineering.: State-of-the-art coding capabilities with fewer tool calls and lower token consumption than previous Opus-class models. Excels in agentic workflows like Claude Code. - Breakthrough Vision Capabilities.: Extract precise data from complex scientific charts, rebuild web apps from screenshots, and understand nested diagrams in documents. Ideal for finance, legal, and analytics workflows. - Superior Reasoning.: Enhanced analytical reasoning for complex problem-solving, research, and knowledge work across domains. - Visual Self-Verification.: Uses vision to evaluate its own coding output, checking results against original designs or target screenshots for higher accuracy. - Improved Efficiency.: Completes equivalent work with fewer tool calls and lower token consumption compared to previous models, reducing costs for complex workflows. - Multilingual Support.: Create content in multiple languages while maintaining cultural nuance and idiomatic expressions. - Easy Integration.: Simple REST API with OpenAI-compatible format. Works seamlessly with Claude Code for autonomous AI-assisted development. ### Related entities - Claude Fable 5 API - piapi - PiAPI ## FAQ ### What is Claude Fable 5? Claude Fable 5 is a cutting-edge AI model excelling in autonomous execution, software engineering, vision, and complex reasoning. It can work for hours or days without human intervention, making it ideal for large-scale coding projects and knowledge work. ### How is Fable 5 different from other models? Claude Fable 5 delivers breakthrough autonomous execution (working for days without intervention), state-of-the-art vision (rebuilding apps from screenshots), and improved efficiency with fewer tool calls and lower token consumption. ### How do I access Fable 5 API? Sign up for a PiAPI account to get free credits and your API key. You can then call the Fable 5 API using simple HTTP requests with your API key in the header. ### What is the pricing for Fable 5 API? Fable 5 is available through PiAPI's pay-as-you-go system. Check the PiAPI workspace for current pricing. New users receive free credits to test the API before paying. ### What are the best use cases for Fable 5? Fable 5 excels at: autonomous coding projects via Claude Code, complex document analysis (finance, legal, analytics), rebuilding UIs from screenshots, long-running research and knowledge work, and any task requiring extended autonomous execution. ### What does 'autonomous execution' mean? Fable 5 can work independently for hours or days on complex tasks. It plans its approach, checks progress against goals, and refines its work — all without human intervention. You can hand off entire projects and review the finished results. ### What can Fable 5 do with vision? Fable 5 has breakthrough vision capabilities: extracting precise data from complex scientific charts, rebuilding web applications from screenshots, and understanding nested diagrams in PDFs. It also uses vision to verify its own coding output against design specs. ### Are there rate limits? PiAPI queues requests when concurrent usage exceeds thresholds. There's no hard limit on total requests — you can make as many as your credit balance allows. ### What other AI models does PiAPI offer? PiAPI provides access to multiple AI models including DeepSeek R1 (reasoning), GPT models, Claude Code for development, and various image/video generation models like Kling, Seedance, and more. # Deep Research API from ChatGPT via PiAPI > Autonomous AI agent that synthesizes hundreds of online sources into expert reports—saving hours on complex research tasks Landing page: https://piapi.ai/deep-research Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Facts - Advanced Multi-Step Reasoning: Deep Research leverages a sophisticated reasoning engine to autonomously plan and execute complex browsing tasks. By dynamically pivoting in response to new information, it achieves what could take hours in mere minutes. - Robust Information Synthesis: Utilizing the upcoming OpenAI o3 model optimized for web browsing and data analysis, Deep Research aggregates and synthesizes data from a multitude of sources, offering comprehensive reports with high-caliber accuracy. - Comprehensive Documentation and Citations: Every output is meticulously documented, complete with citations and a transparent summary of the research path, ensuring verifiable and usable insights for professional or personal applications. - Seamless Integration with ChatGPT: Select 'Deep Research' in ChatGPT's message composer, specify your query, and integrate files for context. The agent will produce analytic reports enriched with images and data visualizations—perfect for detailed domain-specific inquiries. - State-of-the-Art Performance Evaluations: Achieving record scores on Humanity's Last Exam and GAIA benchmarks, Deep Research exhibits AI capabilities rivaling human expert-level precision across diverse subjects—from chemistry to social sciences. - Responsive Tool Utilization: The API incorporates Pytool functionality to plot and iterate on data, embedding visual content and iterating with browsing tools for multi-modal fluency in responses. - Iterative Deployment & Continuous Improvement: Deep Research evolves through iterative deployment, constantly refining its algorithms and parameters to address known limitations and ensure optimum performance and reliability. ### Related entities - Deep Research API from ChatGPT - piapi - PiAPI # DeepSeek API via PiAPI > Try the most cost effective Large Language Model DeepSeek R1 API that is open source and fully licensed to empower your applications! Landing page: https://piapi.ai/deepseek Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: llm - Vendor: deepseek ### Facts - Cost Effective.: With superior inference infrastructure, we are able to provide extremely cost competitive API service for DeepSeek R1 model! - Adequate Token Throughput.: We work hard to keep our output token per seconds (TPS) level of 6 words per second for our users and their applications! - Reinforcement Learning.: R1-Zero demonstrates chain-of-thought (CoT) purely through reinforcement learning (RL), without relying on supervised fine-tuning (SFT). - Multi-Stage Training with Cold Start Data.: DeepSeek incorporates cold-start data to fine-tune the base model, with reasoning-oriented RL, rejection sampling, and supervised fine-tuning, achieving performance comparable to OpenAI-o1-1217. - Distillation for Smaller Models.: DeepSeek enables the distillation of reasoning capabilities into smaller dense models. By fine-tuning open source models like Qwen and Llama with the 800k samples from R1. - Strong Performance across Diverse Tasks.: R1 scores 79.8% Pass@1 on AIME 2024, and 97.3% on MATH - 500, on par with OpenAI-o1-1217. It also excels in coding-related tasks, achieving a high rating on Codeforces, and in other tasks such as creative writing, general question answering, and long - context understanding - Easy Setup and Integration.: Integrating our API is a hassle-free setup process with simple HTTPs calls, making it easy to integrate within any tech stack. - Webhook.: We will notify you of a completed task through our webhook feature, to avoid repeated fetch calls made through the network. ### Related entities - DeepSeek API - deepseek - PiAPI ## FAQ ### What is PiPAI's DeepSeek API? Our DeepSeek API is provided on PiAPI's custom AI inference framework and infrastructure, which allows developers to integrate the advanced, cost-effective LLM capability Chat and Reasoning from R1 and V3 into their own apps or platforms! ### Who is the intended user for the DeepSeek API? The DeepSeek API is created for developers who want to incorporate state of the art language and reasoning capabilities into their generative AI applications. This feature is ideal for any AI powered coding assistants, literature review, documentation summary, translation applications, marketing and advertising related applications. ### How to get started with integrating the API? After registering for an account on PiAPI, you will get some free credits to try the API. Using your own API-KEY you can start making HTTPs calls to the API! ### How do I make calls to the API? You can call our API using HTTPS Post and Get methods from within your application. A wide range of programming languages that support HTTP methods (ex. Python, JavaScript, Ruby, Java, etc.) can be used to make the call! ### Is there a limit to the total number of requests or concurrent number of requests I can make? We will queue your concurrent jobs if the number of your concurrent jobs exceeds a certain threshold. In terms of total number of requests, you can make as many requests as your credit amount allows. ### How does the API handle errors? Our API returns error codes and messages in the HTTP response to help identify the issue. Please refer to our documentation for more details. ### Can you provide dedicated deployment for this API? Yes, absolutely! We provide custom solutions for clients with specialized requirements (ex. low latency, higher concurrency, fine-tuned DeepSeek models, etc), and we do provide cost-effective and performance-enhanced solutions for these LLM usecases! ### What is the license for the DeepSeek model? The DeepSeek Models have a custom open-source license and it is permissible for commercial use for any lawful purpose. Developers do not need to register or apply with DeepSeek before using the open-source models. Developer can also develop derivative models and product applications based on the Models. ### What is the pricing options for the API? We offer the API through a pay-as-you-use system, you can purchase credits on our Workspace and monitor the remaining credits. The per-use cost of the API is reflected on upper portion this page. Please note that the credits purchased do expire in 180 days after purchase. ### How do I pay for this API? We have integrated Stripe in our payment system, which will allow payments to be made from most major credit card providers. # DiffRhythm API via PiAPI > Discover DiffRhythm, the first open-sourced diffusion-based AI music generation model that creates full-length songs with vocals and accompaniment in just 10 seconds! Landing page: https://piapi.ai/diffrhythm Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/diffrhythm-api/create-task ## Product details - Category: audio - Vendor: piapi ### Related entities - DiffRhythm API - piapi - PiAPI # Dream Machine API via PiAPI > Want to allow your users to create breathtaking videos from a simple text prompt or an image? Checkout out PiAPI's Dream Machine API - start generating high-quality videos today! Landing page: https://piapi.ai/dream-machine-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/dream-machine/create-task ## Product details - Category: video - Vendor: luma ### Related entities - Dream Machine API - luma - PiAPI # F5-TTS API via PiAPI > Try the F5-TTS zeroshot text-to-speech API in an interactive playground, then integrate it via PiAPI to clone voices and generate high-quality speech audio from text. Landing page: https://piapi.ai/f5-tts-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/tts-api/f5-tts ## Product details - Category: audio - Vendor: piapi ### Facts - Seamless Integration: Call the F5 TTS AI API with a simple POST request, using standard JSON fields for model and input, and plug it into your existing backend or workflows in minutes. - Batch Processing: Process many text snippets in parallel by queuing multiple F5-TTS tasks, ideal for audiobooks, support responses, localization, or any high-volume text-to-speech workloads. - High-Quality Results: Zero-shot voice cloning with F5 text API produces natural, expressive speech that closely matches your reference voice sample for production-ready audio. ### Related entities - F5-TTS API - piapi - PiAPI # Face Swap API via PiAPI > Experience the power of AI with our FaceSwap API. Embed face-swapping feature into your applications and offer your users the ability to swap faces on their images! Landing page: https://piapi.ai/faceswap-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/faceswap-api/create-task ## Product details - Category: image - Vendor: piapi ### Facts - Cost Effective.: With our affordable pay-as-you-use pricing, we deliver excellent value for money while providing top-notch face-swapping capabilities! - Low Latency.: We work hard to keep latencies low for our API, turning back images result faster for our users! - High Concurrency Supported.: Built to handle high concurrency, our API efficiently manages concurrent requests, ensuring service even during peak loads. - Easy Setup and Integration.: Integrating our API is a hassle-free setup process with simple HTTPs calls, making it easy to integrate within any tech stack. - Asynchronous.: Our API works asynchronously, enhancing your workflow efficiency, and allowing smoother integration into your existing systems. - Automatic face detection.: Our API comes with robust automatic face detection capability, intelligently identifying and swapping faces in your input photo. - Bulk generation.: We have the backend infrastructure to handle your custom high-volume needs, be it for batch operations or large-scale projects or one-time project, we've got you covered. - Standard RESTful API.: Conforming to the constraints of REST architectural style, our API ensures interoperability and compatibility with a variety of systems - Webhook.: We will notify you of a completed task through our webhook feature, to avoid repeated fetch calls made through the network. ### Related entities - Face Swap API - piapi - PiAPI # Flux API via PiAPI > Want to bring the best open source visual generation model from Black Forest Labs to your users? Try Flux API from PiAPI to get started! Landing page: https://piapi.ai/flux-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/flux-api/text-to-image ## Product details - Category: image - Vendor: flux ### Related entities - Flux API - flux - PiAPI ## FAQ ### What is the FLUX API? The FLUX API are offered by PiAPI based on the FLUX.1-dev model and FLUX.1-schnell model. Given our accelerated hardware infrastructure and custom-developed inference framework, PiAPI is able provide APIs for these two models at very market competitive costs while maintaining performance and minimizing latency! ### What type of images can I generate with FLUX.1 API? With the recent ELO score information for the FLUX-dev and FLUX-schnell model both exceeding than that of Midjourney, the possibility of using API for image generation is quite endless. It is expected that it would be significantly impacting industries ranging from entertainment, education, product design all the way to scientific visualization. ### Can I use images generated from the FLUX-dev API for commercial purposes? Given the Apache2.0 license of the FLUX.1-schnell model, all images generated from our API using that model will be available for commercial use as needed by the developers or the end-users. However, images generated from the API for FLUX.1-dev model will not be available for commercial use since BFL has limited that model to non-commercial use only. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # Flux Kontext API via PiAPI > Flux Kontext is an advanced image-to-image editing model that comprehends both your visuals and creative vision. Created by Black Forest Labs and optimized for production use, this powerful Kontext model enables you to transform, enhance, and reimagine images through natural language commands. From adjusting design elements to completely changing artistic styles, the Kontext API makes complex editing feel effortless. Landing page: https://piapi.ai/flux-kontext Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/flux-api/kontext ## Product details - Category: image - Vendor: flux ### Related entities - Flux Kontext API - flux - PiAPI ## FAQ ### What is Flux Kontext API? Flux Kontext API is a powerful image-to-image editing service that allows you to transform images using natural language prompts. It offers multiple model variants (Pro, Max, and Dev) with different speed and quality trade-offs. The API supports various editing tasks including style transfer, object modification, background replacement, text editing, and more. Built by Black Forest Labs, it provides production-ready AI image editing capabilities through simple REST API calls. ### Can I use Flux Kontext for commercial projects? Yes! All Kontext models come with full commercial use rights. You can integrate them into your products, services, and client work without any additional licensing fees or restrictions. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### What image formats are supported? Kontext supports common image formats including JPEG, PNG, and WebP. Input images should be under 10MB for optimal performance. Output format matches your input format by default, but can be specified in your API request. ### Is there a rate limit? Rate limits depend on your plan and usage patterns. Free tier users have lower limits, while paid users get higher throughput. For high-volume applications, contact us about enterprise plans with custom rate limits and dedicated infrastructure. # Flux Kontext Dev via PiAPI > FLUX Kontext Dev is an open-weight, developer-oriented AI image editing model with ultra-fast inference, batch support, and multi-image capabilities. Landing page: https://piapi.ai/flux-kontext/flux-kontext-dev Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/flux-api/kontext ## Product details - Category: image - Vendor: flux ### Facts - Fast, flexible, and developer friendly: FLUX Kontext Dev is an open-weight, developer-oriented AI image editing model with ultra-fast inference, batch support, and multi-image capabilities. ### Related entities - Flux Kontext Dev - flux - PiAPI ## FAQ ### What is Flux Kontext API? Flux Kontext API is a powerful image-to-image editing service that allows you to transform images using natural language prompts. It offers multiple model variants (Pro, Max, and Dev) with different speed and quality trade-offs. The API supports various editing tasks including style transfer, object modification, background replacement, text editing, and more. Built by Black Forest Labs, it provides production-ready AI image editing capabilities through simple REST API calls. ### Can I use Flux Kontext for commercial projects? Yes! All Kontext models come with full commercial use rights. You can integrate them into your products, services, and client work without any additional licensing fees or restrictions. ### How do webhooks work? When you submit an image editing request, you can provide a webhook URL. Once processing is complete, we'll send a POST request to your endpoint with the results. This allows for seamless integration into your applications without polling for status updates. ### What image formats are supported? Kontext supports common image formats including JPEG, PNG, and WebP. Input images should be under 10MB for optimal performance. Output format matches your input format by default, but can be specified in your API request. ### Is there a rate limit? Rate limits depend on your plan and usage patterns. Free tier users have lower limits, while paid users get higher throughput. For high-volume applications, contact us about enterprise plans with custom rate limits and dedicated infrastructure. ### What happens if my API call fails? Failed API calls are not charged. We provide detailed error messages to help you troubleshoot issues. Common failures include invalid image formats, oversized files, or inappropriate content. Our status page shows real-time API health. ### Do you offer API documentation and code examples? Yes! We provide comprehensive API documentation with detailed examples in multiple programming languages including Python, Node.js, PHP, and cURL. Our interactive API explorer lets you test endpoints directly in your browser with real-time examples and response previews. # Flux Kontext Max via PiAPI > FLUX Kontext Max offers maximum precision and control for AI image editing with improved prompt adherence, typography, and premium consistency. Landing page: https://piapi.ai/flux-kontext/flux-kontext-max Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/flux-api/kontext ## Product details - Category: image - Vendor: flux ### Facts - Maximum precision and control: Flux Kontext Max delivers maximum precision and control for professional AI image editing. ### Related entities - Flux Kontext Max - flux - PiAPI # Flux Kontext Pro via PiAPI > FLUX Kontext Pro delivers professional-grade image editing with context-aware AI. Fast iterative editing while maintaining character consistency across scenes. Landing page: https://piapi.ai/flux-kontext/flux-kontext-pro Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/flux-api/kontext ## Product details - Category: image - Vendor: flux ### Facts - Professional image editing: Flux Kontext Pro provides professional AI image editing workflows. ### Related entities - Flux Kontext Pro - flux - PiAPI # Framepack API via PiAPI > Use Framepack API via PiAPI to generate long-form AI videos from static images. Extend videos with image-to-video generation, flexible duration, API docs, pricing and free credits. Landing page: https://piapi.ai/framepack Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/framepack-api/create-task ## Product details - Category: video - Vendor: piapi ### Facts - Multimodal Input: Framepack video API supports multimodal input, turning your image and text prompt into high quality videos. - Long Video Generation: Framepack AI API supports long video generation and flexible duration generation options. Choose generation from 10 to 30 seconds at 30 FPS. - Advanced Training Method: Framepack AI is robustly trained on advanced methods for anti-drifting and anti-forgetting to ensure that video results maintain consistency and quality throughout their duration. - Next Frame Prediction: Framepack AI video utilizes next frame prediction for I2V generations to autoregressively generate video ensuring efficiency and quality. ### Related entities - Framepack API - piapi - PiAPI # GPT Image 1 API via PiAPI > Bring gpt-image-1 image generation capability through API to your users! Landing page: https://piapi.ai/gpt-image-1 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gpt-image/gpt-image-api ## Product details - Category: image - Vendor: openai ### Related entities - GPT Image 1 API - openai - PiAPI ## FAQ ### Can I use images generated from the GPT Image 1 API for commercial purposes? GPT-generated content is not copyrighted, so users can use generated images for their intended purposes. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # GPT Image 1.5 API via PiAPI > Access GPT Image 1.5 via PiAPI for image generation and editing. Supports JPG/PNG/WEBP, high-resolution outputs, code samples, pricing, and free credits to start fast. Landing page: https://piapi.ai/gpt-image-1-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gpt-image/gpt-image-api ## Product details - Category: image - Vendor: openai ### Related entities - GPT Image 1.5 API - openai - PiAPI ## FAQ ### What image styles does GPT Image 1.5 API support? GPT-Image-1.5 API supports diverse artistic styles, from photorealistic scenes to artistic renderings, enabling creative freedom across various visual domains. ### Can ChatGPT Image API handle complex prompts? Absolutely! ChatGPT Image API is designed for advanced prompt understanding, including complex instructions, multiple objects, detailed scenes, and stylistic requests, making it suitable for professional use cases. ### What image formats does GPT-Image-1.5 API support? GPT-Image-1.5 API supports standard image formats including JPG, PNG, WEBP and JPEG, with high-resolution output capabilities. # GPT Image 2.5 API via PiAPI > PiAPI provides GPT Image 2.5 Flare and Sunburst for image generation and editing. Both models share the same USD rates per 1M tokens. Landing page: https://piapi.ai/gpt-image-2-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gpt-image/gpt-image-api ## Product details - Category: image - Vendor: openai ### Facts - PiAPI model: gpt-image-2.5-flare is the fastest PiAPI option for everyday, high-quality image generation. - PiAPI model: gpt-image-2.5-sunburst is the strongest PiAPI option for image generation and editing precision. - PiAPI pricing: Per 1M tokens, both models cost $3.75 text input, $6.00 image input, and $22.50 image output. - Quality: The quality parameter accepts auto, low, medium, and high. The API rejects xhigh, max, and standard with HTTP 400. ### Related entities - GPT Image 2.5 API - OpenAI - PiAPI ## FAQ ### Which GPT Image 2.5 models are available on PiAPI? The API supports gpt-image-2.5-flare and gpt-image-2.5-sunburst for image generation and editing. ### How much does GPT Image 2.5 API cost? Per 1M tokens, both models cost $3.75 text input, $6.00 image input, and $22.50 image output. ### Which quality values does the API accept? The quality parameter accepts auto, low, medium, and high. The API rejects xhigh, max, and standard with HTTP 400. # GPT Image 2 API via PiAPI > Explore the GPT Image 2 API on PiAPI for high-quality image generation, examples, pricing, and playground access. Build with GPT Image 2 and OpenAI Image 2 workflows. Landing page: https://piapi.ai/gpt-images-2-0 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gpt-image/gpt-image-api ## Product details - Category: image - Vendor: openai ### Related entities - GPT Image 2 API - openai - PiAPI ## FAQ ### What is GPT Image 2? GPT Image 2 is the image-generation model featured on this page. PiAPI packages it into a dedicated GPT Image 2 API experience with a hero section, examples, playground access, pricing context, and FAQ guidance in one place. ### Is GPT Image 2 API available on PiAPI? Yes. This page is built as a GPT Image 2 API landing page on PiAPI, with direct playground access, model examples, pricing context, and links into the broader platform workflow. ### Can I generate images directly from the GPT Image 2 API playground? Yes. Once you are signed in with an API-enabled account, you can submit prompts directly from the playground on this page and test the GPT Image 2 API flow from the browser. ### Does this Images 2.0 AI API page support editing as well as text-to-image? The page positioning covers both text-to-image generation and image editing workflows. The current examples lean into polished visual generation first, while the playground remains aligned with the broader GPT image workflow on PiAPI. ### What output options are available for gpt-image-2 right now? The current playground supports prompt, number of images, image size, quality tier, and output format. Those controls are inherited from the shared GPT image playground system used for the gpt-image-2 page experience. ### How much does Images 2.0 API cost? The pricing block on this page reflects the current setup: the gpt-image-2 model is shown at $0.1 per generation. If PiAPI updates the model or pricing tier later, this section should be refreshed to match. # Hailuo API via PiAPI > Use Hailuo API via PiAPI for text-to-video, image-to-video, and subject-reference videos. View docs, Hailuo 2.3 pricing, free credits, and start integrating. Landing page: https://piapi.ai/hailuo Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/hailuo-api/generate-video ## Product details - Category: video - Vendor: hailuo ### Facts - Text to Video (T2V-01): Simply enter your text prompts and stunning videos will be generated by Hailuo AI! Try through our Playground or through the Hailuo API! - Image to Video (I2V): Want to generate videos based on your images? Drag and drop your image along with a textual prompt and see I2V-01 (and I2V-01-live) in action! - Subject Reference (S2V): Upload a reference image and describe your scene, and get ready to be amazed with revolutionizing character consistency! - Recreate Function Supported: Like a specific clip generated and want to try again with a different spin? We support Hailuo's recreate feature through the API endpoint. - Bulk Generation Available: Looking to perform a large amount of text-to-video generation tasks for your own site or your own database? Contact us on Discord with your prompts and we will get back to you as soon as possible! - Dual API Service Modes: Host-your-own-account mode and Pay-as-you-go mode are both supported for Hailuo API. Choose the best approach for your development needs or even mix and them both for stability and cost. - Asynchronous API Calls: Our asynchronous functionality allows users to submit tasks while their program continues to run without interruption until the callback function is triggered. - Watermark Removal: By subscribing to PiAPI's Premium Subscription Plan, users will be able to remove the "Hailuo" watermark on the bottom right corner of the generated video. - Priority Generations: With PiAPI's Premium Subscription Plan, users benefit from top-priority generations, making it perfect for peak load and high demand periods! - Commercial Use: Subscribers to the Premium Plan are allowed to use the generated video outputs for commercial purposes to suit their needs. ### Related entities - Hailuo API - hailuo - PiAPI ## FAQ ### What is Hailuo AI Video Generator? Hailuo Video AI is developed by Minimax, an AI video model swiftly converts text prompts into 6-second high-quality videos, offering 720p resolution at 25 FPS. It's an efficient solution for creating engaging video content. ### What is the Hailuo API? Hailuo Video API is provided by PiAPI to enable developers to integrate the video generation capability of Hailuo into their own APP! ### What type of videos can I make with the Hailuo AI? You can effortlessly create all sorts of stunning videos with the Hailuo AI Video Generator, transforming your ideas into captivating visuals in just minutes! ### What are the current limitations with regarding to video generated? As good as the current model is now, there are still some limitations. For example, in complex, action-packed scene such as a car drifting on snow-covered roads, object morphing can occur, Occasionally, objects that are supposed to be moving remain static. Accurate text displaying also requires further improvement. ### Can you offer the video preview feature during the generation process? Currently Hailuo does not offer this feature as part of the web application, but when they do, PiAPI will integrate this feature into our supported features. ### Can I use videos generated from the Hailuo API for commercial purposes? PiAPI does not put any restrictions on the usage of the generated video. Please also refer to terms of use from Hailuo for more details. # Hailuo 2 API via PiAPI > Discover MiniMax Hailuo 2's groundbreaking video generation capabilities with state-of-the-art instruction following and extreme physics mastery—all delivered at world-class cost efficiency. Landing page: https://piapi.ai/hailuo-02 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/hailuo-api/generate-video ## Product details - Category: video - Vendor: hailuo ### Facts - Text-to-video and image-to-video: Create video from a prompt or use an image as the visual starting point for a generated clip. - Hailuo 2.3 model choices: Choose Hailuo v2.3 for quality or v2.3-fast for faster turnaround. - 768p and 1080p output: Use 768p for 6- or 10-second generations; 1080p is available for 6-second output. ### Related entities - Hailuo 2 API - hailuo - PiAPI ## FAQ ### What is Hailuo 2 API? Hailuo 2 API gives developers access to MiniMax Hailuo video generation through PiAPI, including text-to-video and image-to-video workflows. ### Which Hailuo 2 models can I use? The Hailuo 2 playground supports Hailuo v2.3 and Hailuo v2.3-fast. Available durations depend on the selected output resolution. ### How do I get started with Hailuo 2? Create a PiAPI API key, submit a video_generation task with your prompt and settings, then poll the task until the video result is complete. # Hunyuan API via PiAPI > Use the Hunyuan Video API to generate text-to-video and image-to-video with simple pricing and a free playground. View API docs and integrate in minutes. Landing page: https://piapi.ai/hunyuan Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/hunyuan-api-doc ## Product details - Category: video - Vendor: hunyuan ### Facts - Industry Leading Video Model: Hunyuan Video surpasses commercial models in the industry with cinematic videos like never before. - Ultra Large Parameters Model: Built on 13 billion parameters, the Hunyuan Video model is richly architected to generate videos across a wide variety of content, styles, and ideas. - Rich Semantic Expression: Our Hunyuan API excels in generating smooth videos with complete continuous actions in one go. - High Physical Accuracy: Hunyuan Video API complies with physical law, creating high physical dynamics videos that mimic the natural world. - Artistic Shots: Hunyuan API delivers seamless integration of direction-level camera work, producing smooth special effects combined with native cuts for a smoother video experience. - Concept Generalization: Hunyuan AI API supports rich combinations of ideas, including Chinese-style creation and broader Eastern culture themes. - Video Dubbing: Hunyuan Video supports video dubbing and sound generation within AI generated videos, enabling richer storytelling with synchronized audio. ### Related entities - Hunyuan API - hunyuan - PiAPI ## FAQ ### What is Hunyuan Video? Hunyuan Video is a video-generation model from Tencent's Hunyuan family designed for high-quality text-to-video and image-to-video synthesis. Through PiAPI , you can integrate Hunyuan Video API into your products and workflows. ### How do I access Hunyuan Video via PiAPI? You can access Hunyuan Video by creating an account in the PiAPI workspace and generating an API key. Then follow our Hunyuan Video API docs to create tasks and retrieve generated videos. ### What generations does Hunyuan Video support? Hunyuan Video supports both I2V and T2V generation. You can start from a purely textual prompt or supply a reference frame to guide motion, style, and layout. ### How is Hunyuan Video priced on PiAPI? Pricing is per generation and varies by model type (txt2video, fast-txt2video, image-to-video, and LoRA variants). See the Hunyuan pricing table above on this page or our main pricing page for the latest details. # AI Image Background Remover via PiAPI > Remove image backgrounds instantly with PiAPI's AI image background remover tool. API Docs, Pricing and Free credits available. Start with PiAPI today! Landing page: https://piapi.ai/image-remove-background Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/image-editing-api/remove-background-api ## Product details - Category: image - Vendor: piapi ### Related entities - AI Image Background Remover - piapi - PiAPI # AI Image Segmentation Tool via PiAPI > Segment any region in your images using the Segmentation API. Generate clean masks or foreground cutouts in seconds for ecommerce, creative tools, and automated workflows. Landing page: https://piapi.ai/image-segmentation-tool Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/image-editing-api/segment-with-prompt-api ## Product details - Category: image - Vendor: piapi ### Related entities - AI Image Segmentation Tool - piapi - PiAPI # AI Image Upscaler via PiAPI > Upscale and enhance images instantly with PiAPI's AI Image Upscaler API. Improve resolution and sharpen details so your images look great everywhere. Landing page: https://piapi.ai/image-upscaler-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/image-editing-api/super-resolution-api ## Product details - Category: image - Vendor: piapi ### Related entities - AI Image Upscaler - piapi - PiAPI # Kling 2.5 API via PiAPI > Access Kling 2.5 via PiAPI. Use Image-to-Video and Text-to-Video with Turbo mode, view pricing, explore API docs, and start generating videos with a developer-ready endpoint. Landing page: https://piapi.ai/kling-2-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Facts - Enhanced Prompt Adherence & Temporal Control: Kling 2.5 parses complex, multi-step instructions with stronger understanding of causal relationships, enabling accurate character interactions and smooth scene transitions. Improved temporal logic ensures coherent story flow, richer narratives, and consistent creative expression across the entire sequence. - Fluid Motion & Stable Dynamic Scenes: Kling 2.5 API handles a wider range of movements with stability not achievable in earlier versions such as Kling 2.1. Kling AI 2.5 ensures that motion stays fluid and natural, minimizing distortions or breakdowns even in fast-changing environments. - Advanced Training Methods: Kling 2.5 Turbo is built using reinforcement learning, high-intensity image conditioning, and large-scale training over massive amounts of high-quality video data. These techniques result in stronger visual fidelity, better motion reasoning, and more robust generation performance for Kling AI 2.5 API. - Reference-Image-Driven Consistency: Kling AI 2.5 API supports reference images as input, preserving visual identity with high accuracy. Colors, lighting, textures, and the overall atmosphere remain consistent throughout the generated video, maintaining stylistic fidelity. - Asynchronous API Calls: Our API is designed to be called asynchronously with callback function, so that users can submit tasks and be notified upon their completion, avoiding program interruptions. ### Related entities - Kling 2.5 API - kling - PiAPI ## FAQ ### What is Kling 2.5? Kling 2.5 is the latest version of the Kling AI video generation model developed by Kuaishou. It features improved video quality, better temporal consistency, and enhanced visual performance compared to previous versions. ### How do I access Kling 2.5 via API? Use PiAPI's Kling 2.5 API endpoints to submit prompts and receive generated videos. The API supports text-to-video and image-to-video. You can also explore more video editing capabilities with Kling AI API . ### What makes Kling 2.5 different from other Kling versions? Kling 2.5 offers enhanced video quality with improved temporal consistency, sharper visuals, and more realistic motion. It provides better prompt adherence and overall video generation performance. ### Can I control duration and resolution? Yes, you can control the duration (5s or 10s) for our Kling 2.5 API along with various aspect ratios for different use cases. ### Is Kling 2.5 suitable for production use? Yes, Kling 2.5 Turbo is designed for production use. Many teams use it for content creation, social media, advertising, and creative projects. The API supports asynchronous calls for scalable workflows. ### Can I generate NSFW content using Kling 2.5 API? Please refer to our NSFW policy for more information and follow the guidelines. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you are given free credits to try our playground and our API (specifically the Pay-as-you-go service) before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # Kling 2.6 API via PiAPI > Use Kling 2.6 API via PiAPI to generate image-to-video and text-to-video with native audio. View API docs, pricing, free credits, and start integrating in minutes. Landing page: https://piapi.ai/kling-2-6 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Facts - Native Audio Capability: By aligning semantics of sounds and dynamic visuals, Kling 2.6 API supports natural voice generation in Chinese and English, action sound effects and environmental ambience sounds. - Enhanced Semantic Understanding: Kling 2.6 API features an enhanced semantic understanding of textual descriptions, spoken languages, and intricate storylines. - Audio-visual Synchronization: Kling Video 2.6 API provides frame-level alignment between visual motion and audio rhythms for consistent audiovisual coherence. - Precise generation and control of generated video: Kling 2.6 AI API supports configurable video duration and aspect ratio, with the ability to generate up to 4 videos in a single request. - T2V and I2V Generation: Kling 2.6 API supports both text-to-video (T2V) and image-to-video (I2V) generation for flexible video creation workflows. - Asynchronous API Calls: Our API is designed to be called asynchronously with callback function, so that users can submit tasks and be notified upon their completion, avoiding program interruptions. ### Related entities - Kling 2.6 API - kling - PiAPI ## FAQ ### What is Kling 2.6? Kling Video 2.6 is the latest version of the Kling AI video generation model developed by Kuaishou. It features native audio generation, improved video quality, better temporal consistency, and enhanced visual performance compared to previous versions. ### How do I access Kling 2.6 via API? Use PiAPI's Kling 2.6 AI API endpoints to submit prompts and receive generated videos. The API supports text-to-video and image-to-video. You can also explore more video editing capabilities with Kling AI API . ### What makes Kling 2.6 different from other Kling versions? Kling Video 2.6 AI offers native audio generation that previous Kling models did not, along with enhanced video quality featuring improved temporal consistency, sharper visuals, more realistic motion, and better prompt adherence for overall video generation performance. ### Can I control duration and resolution? Yes, you can control the duration (5s or 10s) for our Kling 2.6 API along with various aspect ratios for different use cases. ### Is Kling 2.6 free? You will receive free credits upon sign up to use in our PiAPI workspace . ### How much does Kling 2.6 API cost? Our Kling 2.6 API costs $0.20 for a 5 seconds video under standard mode and $0.33 for a 5 seconds video under professional mode. ### Is Kling 2.6 suitable for production use? Yes, Kling 2.6 API is designed for production use. Many teams use it for content creation, social media, advertising, and creative projects. The API supports asynchronous calls for scalable workflows. ### Does Kling 2.6 support motion control? Yes, we do support Kling 2.6 motion control . Come experience motion control AI with Kling 2.6 today! ### Can I generate NSFW content using Kling 2.6 API? Please refer to our NSFW policy for more information and follow the guidelines. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you are given free credits to try our playground and our API (specifically the Pay-as-you-go service) before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # Kling 2.6 Dance Generator via PiAPI > Try the Kling AI Dance Generator with a free 5-second demo. Upload an image and short video to preview AI-generated dance motion before using Kling 2.6 via PiAPI. Landing page: https://piapi.ai/kling-2-6/dance-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-motion-control-api ## Product details - Category: video - Vendor: piapi ### Related entities - Kling 2.6 Dance Generator - kling - PiAPI # Kling 2.6 Motion Control via PiAPI > Try Kling 2.6 Motion Control in a live playground. Upload reference videos, control motion strength, test features with free credits, then scale with the Kling 2.6 API via PiAPI. Landing page: https://piapi.ai/kling-2-6/motion-control Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-motion-control-api ## Product details - Category: video - Vendor: kling ### Related entities - Kling 2.6 Motion Control - kling - PiAPI # Kling 2.6 Motion Poster via PiAPI > Try the Kling AI Motion Poster Playground to turn images or references videos into dynamic motion posters. Free demo. Upload your own assets, then integrate Kling 2.6 via PiAPI. Landing page: https://piapi.ai/kling-2-6/motion-poster Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: piapi ### Related entities - Kling 2.6 Motion Poster - kling - PiAPI # Kling 3.0 API via PiAPI > Use Kling 3.0 API via PiAPI to generate text-to-video and image-to-video with multi-shot control and native audio, built on Kling 2.6. View docs, pricing, free credits, and start integrating in minutes. Landing page: https://piapi.ai/kling-3-0 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-3-api ## Product details - Category: video - Vendor: kling ### Facts - Multi-shot AI Director: Kling 3 AI acts as an AI director, understanding scene coverage and shots, intelligently adjusting camera angles and compositions, and applying cinematic language with precision for film-like results. - Enhanced Subject Consistency: Kling 3 delivers industry-leading subject consistency in image-to-video, supporting multi-image references and multi-character coreference. - Element Reference: Kling 3 supports element references for subject consistency and output stability. - Upgraded Native Audio In 5 Languages: Kling 3 AI API is capable of generating native-quality audio in Chinese, English, Japanese, Korean, and Spanish, handling dialects, accents, multilingual code-switching dialogues, and syncing natural lip movements and facial expressions. - Native-level Text Rendering: Kling 3.0 API produces crisp, readable text directly in video frames with well-structured layouts, supporting high-fidelity use cases such as ecommerce and performance-driven advertising. - 15-second Generation and Duration Control: Kling video 3.0 supports video generation up to 15 seconds to accommodate more complex action sequences. Flexible duration options are also available for creators who need more flexibility. - Custom Reference Frames: Kling 3.0 supports custom start and end frames for I2V video generations to enable smoother transitions, tighter edits, and easier integration into longer narrative timelines. ### Related entities - Kling 3.0 API - kling - PiAPI ## FAQ ### What is Kling 3.0? Kling Video 3.0 is a powerful Kling AI video generation model developed by Kuaishou, offering advanced video and audio generation capabilities. It builds on Kling 2.6 with improved temporal consistency, better motion, and richer creative control, designed for production-ready workflows from social content to high-end advertising. Start using Kling 3.0 API with PiAPI today! ### How do I access Kling 3.0 via API? Use PiAPI's Kling 3.0 AI API endpoints to submit prompts and receive generated videos. The API supports T2V and I2V generations. You can also explore more video generation capabilities with Kling AI API . ### What makes Kling 3.0 different from other Kling versions? Kling Video 3.0 AI offers upgraded quality and control compared to earlier Kling models , including improved temporal consistency, sharper visuals, more realistic motion, and enhanced audio generation. ### What is the difference between Kling 3.0 Omni and Kling 3.0? Kling 3.0 Omni is built on Kling O1 while Kling 3.0 is built on Kling 2.6. Both models are similar in that they support T2V, I2V, and features like multi-shot. However, Kling 3.0 Omni gives you more control by allowing developers to generate using reference from videos. ### Can I control duration and resolution? Yes, you can control the duration and aspect ratios for Kling 3.0 API videos for different use cases. ### Is Kling 3.0 free? You will receive free credits upon sign up to use in our PiAPI workspace , so you can experiment with Kling 3.0 generations, explore different settings, and test integrations before committing to a paid plan. ### Is Kling 3.0 suitable for production use? Yes, Kling 3.0 API is designed for production use. Many teams use Kling API services for content creation, social media, advertising, and creative projects. Our Kling video 3 API supports asynchronous calls for scalable workflows and large productions. ### Can I generate NSFW content using Kling 3.0 API? Please refer to our NSFW policy for more information and follow the guidelines. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you are given free credits to try our playground and our API (specifically the "Pay-as-you-go" service) before making payments! ### How can I get in touch with your team? Please email support@piapi.ai if you have questions about Kling 3.0 API, pricing or integrations. The team is happy to help you design and launch your video workflows. # Kling 3.0 Omni API via PiAPI > Use Kling 3.0 Omni API for multi-shot text-to-video and image-to-video with native audio. Compare $0.10-$0.20/sec pricing, docs, and free credits. Landing page: https://piapi.ai/kling-3-omni Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-3-omni-api ## Product details - Category: video - Vendor: kling ### Facts - Multi-shot AI Director: Kling 3 AI acts as an AI director, understanding scene coverage and shots, intelligently adjusting camera angles and compositions, and applying cinematic language with precision for film-like results. - Enhanced Subject Consistency: Kling 3 delivers industry-leading subject consistency in image-to-video, supporting multi-image references, multi-character coreference (3+ characters), and video references for stable, coherent motion. - Element Reference: Kling 3 Omni supports element references for subject consistency and output stability. - Upgraded Native Audio In 5 Languages: Kling 3 AI API is capable of generating native-quality audio in Chinese, English, Japanese, Korean, and Spanish, handling dialects, accents, multilingual code-switching dialogues, and syncing natural lip movements and facial expressions. - Native-level Text Rendering: Kling video 3 omni API produces crisp, readable text directly in video frames with well-structured layouts, supporting high-fidelity use cases such as ecommerce and performance-driven advertising. - 15-second Generation and Duration Control: Kling video 3.0 supports video generation up to 15 seconds to accommodate more complex action sequences. Flexible duration options are also available for creators who need more flexibility. - Custom Reference Frames: Kling 3.0 supports custom start and end frames for I2V video generations to enable smoother transitions, tighter edits, and easier integration into longer narrative timelines. ### Related entities - Kling 3.0 Omni API - kling - PiAPI ## FAQ ### What is Kling 3.0 Omni? Kling Video 3.0 Omni is the latest version of the Kling AI video generation model developed by Kuaishou, offering advanced video and audio generation capabilities. It is a major upgrade and built on Kling O1 , with superior performance, more cinematic control, richer storytelling tools, and greater consistency across complex scenes, designed for production-ready workflows from social content to high-end advertising. Start creating with our Kling 3 API today! ### How do I access Kling 3.0 via API? Use PiAPI's Kling 3.0 AI API endpoints to submit prompts and receive generated videos. The API supports T2V and I2V generations. You can also explore more video generation capabilities with Kling AI API . ### What makes Kling 3.0 different from other Kling versions? Kling Video 3.0 AI offers upgraded quality and control compared to earlier Kling models , including improved temporal consistency, sharper visuals, more realistic motion, and enhanced audio generation. ### What is the difference between Kling 3.0 Omni and Kling 3.0? Kling 3.0 Omni is built on Kling O1 while Kling 3.0 is built on Kling 2.6. Both models are similar in that they support T2V, I2V, and features like multi-shot. However, Kling 3.0 Omni gives you more control by allowing developers to generate using reference from videos. ### Can I control duration and resolution? Yes, you can control the duration and aspect ratios for Kling 3.0 API videos for different use cases. ### Is Kling 3.0 free? You will receive free credits upon sign up to use in our PiAPI workspace , so you can experiment with Kling 3.0 generations, explore different settings, and test integrations before committing to a paid plan. ### Is Kling 3.0 suitable for production use? Yes, Kling 3.0 API is designed for production use. Many teams use Kling API services for content creation, social media, advertising, and creative projects. Our Kling video 3.0 API supports asynchronous calls for scalable workflows and large productions. ### Can I generate NSFW content using Kling 3.0 API? Please refer to our NSFW policy for more information and follow the guidelines. # Kling AI Avatar API via PiAPI > Create AI avatars with Kling: facial animation, consistent characters, voice-driven motion, and avatar video generation. API docs, avatar features & model capabilities via PiAPI. Landing page: https://piapi.ai/kling-ai-avatar Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-avatar-api ## Product details - Category: video - Vendor: kling ### Facts - Multimodal Input Support: Kling Avatar API accepts image, audio, and text prompts as input, enabling flexible control over avatar behavior, appearance, and narrative design. Kling AI Avatar's multimodal pipeline allows precise alignment between visual identity, speech timing, and instruction-based AI avatar generation. - High-Quality Video Output: Kling Avatar API generates videos at 1080p resolution and 48 FPS, delivering crisp visuals and smooth motion. It supports up to 1-minute video generation, making it ideal for explainers, news-style reporting, and long presentations. - Advanced Generation Capabilities: Kling AI Avatar provides state-of-the-art lip-sync accuracy across singing, multilingual speech, and fast dialogue. It also offers expressive emotion control, supports realistic human and animal characters, and maintains visual and motion stability in long-duration extrapolated videos. - Multilingual Speech & Performance: The Kling Avatar model supports English, Japanese, Korean, and Chinese, enabling natural and expressive AI avatar generation, suitable for content across multiple regions. - Advanced Training Architecture: Kling AI Avatar leverages multimodal large language models (MLLM) for unified global planning, keyframe-controlled architecture, cross-attention mechanisms, enhanced lip-sync strategies, and optimized data processing for more robust results. ### Related entities - Kling AI Avatar API - kling - PiAPI ## FAQ ### What is Kling AI Avatar? Kling AI Avatar is an AI technology by Kuaishou that allows you to generate AI avatars from your imagination. With PiAPI , you can now create AI avatars with our Kling AI Avatar API today! ### How do I generate avatars with the Kling API? You can generate avatars using PiAPI's Kling endpoint. Documentation includes full request examples and configuration options. ### Can I use Kling AI Avatar outputs commercially? Yes. Kling avatar outputs can be used for commercial purposes such as content creation, marketing, virtual influencers, or presentations. ### What is the Kling AI Avatar API? Kling AI Avatar API provides advanced AI-driven avatar generation capabilities, supporting high-fidelity photorealistic and stylized outputs for virtual identity and creative production pipelines. ### Does Kling AI Avatar support audio-driven full-body generation? Yes, Kling AI Avatar can generate full-body avatar videos directly from text, image and audio inputs, resulting in synchronizing lip movement and body motion. Our AI avatar API is suitable for developers and content creators. ### Can I control scenes or emotions via prompts? Yes, Kling AI Avatar API allows fine control through prompts, you can modify background scenes and specify emotional tone while preserving identity consistency. # Kling AI Kiss Generator via PiAPI > Create short AI kiss videos from photos using Kling Effects. Upload an image, generate realistic romantic kiss animations, and try the free demo before integrating via PiAPI. Landing page: https://piapi.ai/kling-ai-kiss Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: piapi ### Facts - Fixed Kling 2.6 kiss workflow: The mini app uses a focused romantic image-to-video template rather than an open-ended video setup. - Two-subject reference image: For the most stable kiss motion, upload one clear image with two visible subjects and readable faces. - Developer-ready task identity: The quickstart creates a kling video_generation task using Kling 2.6. ### Related entities - Kling AI Kiss Generator - piapi - PiAPI # Proposal Video Generator via PiAPI > Create romantic AI videos from photos using Kling Effects. Generate proposal-style, cinematic, or affectionate image-to-video animations in a free playground, then integrate via PiAPI. Landing page: https://piapi.ai/kling-ai-proposal Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - Proposal Video Generator - piapi - PiAPI # AI Squish Generator via PiAPI > Create squishy, elastic animation videos from photos using Kling AI. Upload an image, generate playful squash-and-stretch effects, and try the free demo on PiAPI. Landing page: https://piapi.ai/kling-ai-squish Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-effects-api ## Product details - Category: video - Vendor: piapi ### Facts - Focused squish effect: This mini app fixes the Kling effect to squish so creators can start from one image without configuring the full effects catalog. - Image upload and preview: Upload a reference image, review the preview, and generate a short elastic photo-to-video animation. - Effects API quickstart: Use model kling and task_type effects with effect set to squish in your own application. ### Related entities - AI Squish Generator - piapi - PiAPI # Kling API via PiAPI > Want to bring your users the Kling 1.0, 1.5 or 2.0 model from Kuaishou? Check out our Kling API to generate video content from texts, static images, or existing videos! Landing page: https://piapi.ai/kling-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Facts - Text-to-video and image-to-video: Generate a clip from a prompt alone or add a source image for image-guided animation. - Multiple Kling generations: The playground exposes classic Kling 1.6, 2.5, 2.6, and 3.0 video generation, plus Kling 3.0 Omni and Kling O1 with their own tier, duration, resolution, and audio options. - Asynchronous task API: Submit a generation task, then poll its task ID until the video result is ready for your workflow. ### Related entities - Kling API - kling - PiAPI ## FAQ ### What is the Kling API? Kling API gives developers access to Kuaishou's Kling video-generation models through PiAPI for text-to-video and image-to-video workflows. ### Which Kling versions can I use? The Kling API playground supports Kling 1.6, 2.5, 2.6, and 3.0. Available modes, durations, and native-audio options depend on the version you select. ### How do I call the Kling API? Create an API key, submit a video_generation task to the PiAPI task endpoint, and poll the returned task ID until the result is complete. ### Can I use an image as input? Yes. Kling supports both text-to-video and image-to-video generation; add an image URL to the task input for an image-guided clip. # Kling API V2 via PiAPI > Kling 2.0 Master - Bringing your video generation to the next level! Landing page: https://piapi.ai/kling-api/v2 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Related entities - Kling API V2 - kling - PiAPI # Kling API 2.1 Master via PiAPI > Kling 2.1 Master - Unparalleled image and text to video generator with great prompt adherence Landing page: https://piapi.ai/kling-api/v2.1/master Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Related entities - Kling API 2.1 Master - kling - PiAPI # Kling API 2.1 Pro via PiAPI > Kling 2.1 Pro - superior video gneration with flexibility and precision! Landing page: https://piapi.ai/kling-api/v2.1/pro Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Related entities - Kling API 2.1 Pro - kling - PiAPI # Kling API 2.1 Standard via PiAPI > Kling 2.1 Standard - Outstanding video generation with great value! Landing page: https://piapi.ai/kling-api/v2.1/standard Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/create-task ## Product details - Category: video - Vendor: kling ### Related entities - Kling API 2.1 Standard - kling - PiAPI # Kling Effects API via PiAPI > Create AI video effects like squish, expansion, birthday, and water effects in the Kling Effects playground. Integrate the Kling Effects API via PiAPI with simple POST calls. Landing page: https://piapi.ai/kling-effects-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-effects-api ## Product details - Category: video - Vendor: kling ### Facts - Effect-first image-to-video workflow: Upload one image, select a supported Kling effect, and generate a short animated video. - 15 priced preset effects: The current schema exposes 10 standard-price presets and 5 professional-price presets. - Simple task API: Create an effects task with model kling, then poll the task ID for its completed video URL. ### Related entities - Kling Effects API - kling - PiAPI ## FAQ ### What is the Kling Effects API? Kling Effects API creates short image-to-video animations. Upload an image, select an effect such as squish or water, submit an effects task, and retrieve the finished video through PiAPI. ### How is Kling Effects priced? The authorized preset tiers are $0.20 per video for standard effects and $0.30 per video for professional effects. The selected effect determines the tier. ### What input does an effects task need? An effects task uses model kling, task_type effects, an effect name, and an image_url. Some effects may also benefit from an optional prompt. ### Can I try focused effects such as kiss or squish? Yes. The Kling AI Kiss and AI Squish Generator pages keep focused mini-app workflows for those effects, while this page exposes the broader effects selector. # Kling O1 API via PiAPI > Use Kling O1 API via PiAPI to generate high-quality short-form videos. View API docs, pricing, free credits, and start integrating in minutes. Landing page: https://piapi.ai/kling-o1 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/kling-o1-api ## Product details - Category: video - Vendor: kling ### Facts - World's First Unified Multi-modal Tool: Kling Omni is the industry's first multimodal video generation and video editing tool, supporting text, video, image and subject inputs. - Reference Based Video Generation: Kling Omni AI API supports reference image video generation for enhanced subject consistency and stable output. - Start And End Frame Generation: Kling O1 API support custom start and end frames for I2V video generations to enable smoother transitions, tighter edits, and easier integration into longer narrative timelines. - Video Editing Functions: Kling AI O1 API supports multiple editing functions include subject referecing, video content editing, and shot transitions. - Flexible Duration Options: Kling Omni 1 API enable users to control output duration to fit a diversity of use cases. - Director-like Memory: Kling O1 retains identity of main characters, props and settings for stability amidst dynamic camera movements. - Supports Multitasked Prompts: Kling Omni API excels in processing multitasked prompts, exponentially expanding creative freedom and flexibility. ### Related entities - Kling O1 API - kling - PiAPI ## FAQ ### What is Kling O1? Kling O1 is a Kling video generation model designed for high-quality, single-shot and short-form video creation. It powers cinematic AI video outputs with strong motion and temporal consistency, and serves as a predecessor to the more advanced Kling variant, Kling 3.0 Omni . Start creating with PiAPI today! ### How do I access Kling O1 via API? Use PiAPI's Kling O1 API endpoints to submit prompts and receive generated videos. You can find full request/response examples in the Kling O1 API docs . ### What workflows does Kling O1 support? Kling Omni one supports core video generation workflows like text-to-video and image-to-video. Our Kling AI API is well suited for social clips, marketing assets, short-form storytelling, and experimentation with video prompts. ### How is Kling O1 different from Kling 3.0 Omni? Kling O1 focuses on strong single-shot video generations, while Kling 3.0 Omni introduces advanced capabilities such as multi-shot cinematic control, longer durations, and richer multi-character scenes. Many teams use Kling O1 for fast experimentation, then upgrade to Kling 3.0 Omni for complex, multi-shot sequences. # MiniMax H3 API via PiAPI > MiniMax H3 API generates MP4 video with native stereo audio from text prompts or a first-frame image through PiAPI. Landing page: https://piapi.ai/minimax-h3 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/minimax-api/generate-video ## Product details - Category: video - Vendor: minimax ### Facts - Text-to-video: Create a video from a text prompt with the MiniMax H3 API. - Image-to-video: Provide a first-frame image and prompt to guide the generated video. - Native stereo audio: Completed MP4 outputs include a generated audio track muxed with the video. - 480p, 768p, and 1080p: Choose 480p for the lower-cost option, 768p for a balance, or 1080p for the highest-resolution output. - 5–15 second clips: Set the requested duration between 5 and 15 seconds. - Async task workflow: Submit a task, poll its status, and retrieve the completed video URL. - Managed MiniMax H3 API: Use MiniMax H3 through PiAPI without downloading model weights or operating GPU provisioning, inference, scaling, monitoring, and upgrades. - Content policy: The PiAPI endpoint follows applicable model moderation and PiAPI's NSFW policy; local open-weight behavior does not guarantee hosted API behavior. - Community variants and LoRAs: PiAPI will follow the MiniMax H3 open-source community and evaluate useful model variants and LoRAs for future managed API access. ### Related entities - MiniMax H3 API - MiniMax AI video generator - MiniMax H3 open weights - MiniMax H3 LoRA - MiniMax H3 community variants - self-hosted AI video generator - ComfyUI - Seedance alternative - Kling AI - PiAPI ## FAQ ### What is MiniMax H3 API? MiniMax H3 API is a video generation API that creates MP4 video with native stereo audio from text prompts or a first-frame image. ### Does MiniMax H3 support image-to-video? Yes. For image-to-video, provide a public JPG or PNG image as the first frame together with a prompt. ### What resolution and duration does MiniMax H3 support? MiniMax H3 supports 480p, 768p, and 1080p output with requested durations from 5 to 15 seconds. ### How do I call MiniMax H3 through PiAPI? Create a PiAPI API key, submit a txt2video or img2video task, poll the task status, and read the completed video URL from the response output. ### Is MiniMax H3 a Seedance alternative? MiniMax H3 is a practical Seedance alternative for developers who need text-to-video, first-frame image-to-video, and native stereo audio through a managed AI video API. Seedance is a better fit when a workflow specifically needs PiAPI's broader multi-image, video, or audio reference modes. ### How does MiniMax H3 compare with Kling AI? MiniMax H3 is a cost-conscious Kling AI alternative for text-to-video and first-frame image-to-video with native stereo audio. Kling offers a broader model family and additional reference and generation controls. PiAPI lets you test both API workflows before choosing a model for production. ### Why use the MiniMax H3 API on PiAPI instead of self-hosting? PiAPI provides a managed MiniMax H3 API without the GPU capacity planning, model downloads, ComfyUI setup, storage, scaling, monitoring, upgrades, and security work required by local deployment. Start in the PiAPI Playground, then use the same asynchronous API workflow in production. ### Is MiniMax H3 an uncensored AI video generator? Some community users describe local MiniMax H3 open-weight workflows as uncensored, but results and moderation vary by checkpoint, runtime, and hosting provider. PiAPI does not promise an unfiltered endpoint: requests must follow applicable model moderation and PiAPI's NSFW content policy. ### Will PiAPI support MiniMax H3 community variants and LoRAs? PiAPI will continue following the MiniMax H3 open-source community and evaluating useful community model variants and LoRAs. Any future managed API availability will depend on compatibility, output quality, licensing, safety, stability, and production demand. # MMAudio API via PiAPI > Try the MMAudio video-to-audio API in an interactive playground, then integrate it via PiAPI to transform silent videos into immersive audio experiences with perfectly matched, professional soundtracks. Landing page: https://piapi.ai/mmaudio-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/mmaudio-api/create-task ## Product details - Category: audio - Vendor: piapi ### Facts - Seamless Integration: Call the MMAudio AI API with a simple POST request, using standard JSON fields for model and input, and plug it into your existing backend or workflows in minutes. - Batch Processing: Process many videos in parallel by queuing multiple MMAudio tasks, ideal for content creation, video editing workflows, or any high-volume video-to-audio workloads. - Multimodal Support: Our MMAudio AI API supports text and video input modalities for effective data scaling and cross modal semantic alignment. - Synchronization Module: MMAudio is built with a synchronization module that aligns audio with video frames for tightly coupled, on-beat audio generation. - Multimodal Joint Training: MMAudio is trained on a wide range of audio-visual and audio-text datasets for more robust and comprehensive results across diverse content types. ### Related entities - MMAudio API - piapi - PiAPI # Moshi API via PiAPI > Looking to integrate Moshi into your application? Checkout out PiAPI's Moshi API! Landing page: https://piapi.ai/moshi-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: llm - Vendor: piapi ### Facts - Cost Effective Solutions: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! - Low Latency: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! - High Concurrent Workloads: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! - Dedicated Service: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! - Added Data Security: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! - Fine-tuning Available: We provide custom deployment service that allows for seamless integration, optimized performance, and robust support. Enjoy enhanced security and customization options with peak performance. Email us today to get started! ### Related entities - Moshi API - piapi - PiAPI # Nano Banana 2 API via PiAPI > Nano Banana 2 API via PiAPI provides high-quality AI image generation and image editing with flexible aspect ratios, resolutions, and output formats. Landing page: https://piapi.ai/nano-banana-2 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gemini-api/nano-banana-2 ## Product details - Category: image - Vendor: google ### Related entities - Nano Banana 2 API - google - PiAPI ## FAQ ### What is Nano Banana 2 API? Released by Google, Nano Banana 2 is the successor to Nano Banana and Nano Banana Pro. The Nano Banana 2 API provides image generation and editing capabilities via PiAPI, including text-to-image and image-to-image workflows with configurable formats, aspect ratios, and resolutions. ### Can I edit existing images with Nano Banana 2? Yes. Upload one or more reference images and describe the change you want. Nano Banana 2 supports controlled transformations while keeping key elements consistent. ### Is there free access to try Nano Banana 2? When you first sign up in PiAPI's Workspace, you can use free credits to try Nano Banana 2 in the playground. ### Do you support webhooks? Yes, webhook notifications are available for asynchronous workflows. ### How do I integrate the API into my application? Integration is straightforward with our REST API. We provide request details and examples in the Nano Banana 2 API docs. # Nano Banana Pro API via PiAPI > Use the Nano Banana Pro API for high-quality image generation and editing. Transparent pricing from $0.105 per image, full API docs, and commercial usage via PiAPI. Landing page: https://piapi.ai/nano-banana-pro Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gemini-api/nano-banana-pro ## Product details - Category: image - Vendor: google ### Facts - Lightning-Fast Generation: Powered by Google's optimized Gemini 2.5 Flash model, delivering high-quality images in seconds. Perfect for real-time applications and rapid prototyping. - Cost-Effective Pricing: At just $0.105 per image for 1K and 2K outputs, get premium AI-generated visuals without breaking the budget. - Advanced AI Technology: Leverage Google's cutting-edge Gemini 2.5 Flash model with state-of-the-art image generation capabilities and superior prompt understanding. - Multiple Output Formats: Choose between JPEG and PNG formats to suit your specific needs. Optimized file sizes for web use and high-quality outputs for print. - No-Code Workspace: Test and generate images directly through our intuitive web interface before integrating into your applications. No coding required to start. - Developer-Friendly API: Clean, well-documented REST API with comprehensive examples. Easy integration with any programming language or platform. - Reliable Infrastructure: Built on robust cloud infrastructure with 99.9% uptime guarantee. Automatic scaling and load balancing for consistent performance. - Webhook Support: Get real-time notifications when your image generation tasks are complete. Perfect for asynchronous workflows and automation. - Commercial Usage: All generated images are cleared for commercial use. Build products, create content, and monetize your applications with confidence. ### Related entities - Nano Banana Pro API - google - PiAPI ## FAQ ### What is Gemini 2.5 Flash Image API? Gemini 2.5 Flash is Google's latest AI image generation model, optimized for speed and quality. Our API provides easy access to this technology, allowing you to generate high-quality images from text prompts in seconds. ### How much does it cost to generate images? Our pricing is simple and transparent: it starts at $0.105 per image for 1K and 2K outputs, with no subscription fees or hidden costs. ### What image formats are supported? We support both JPEG and PNG output formats. You can specify your preferred format in the API request. ### How fast is the image generation? Most images are generated in under 10 seconds, making Nano Banana a strong fit for real-time applications and user-facing products. ### Can I use generated images commercially? Yes. All images generated through our API come with commercial usage rights for products, marketing, websites, and other business use cases. ### What is the no-code workspace? Our no-code workspace at piapi.ai/workspace/nano-banana allows you to test the API without writing code. You can enter prompts, adjust settings, and generate images directly in your browser. ### Do you support webhooks? Yes, we provide webhook support for asynchronous processing. You can specify a webhook endpoint and receive a notification when your images are ready. ### How do I integrate the API into my application? Integration is straightforward with our REST API. We provide request details and examples in our API docs . ### What kind of prompts work best? Detailed, descriptive prompts work best. Include information about style, composition, lighting, colors, and mood for stronger results. ### Is there a limit on image generation? There are no hard limits on total image generation, but you can generate up to 4 images per API request. ### How can I get in touch with your team? Please email us at support@piapi.ai and we would be happy to help. # OmniAvatar API via PiAPI > Generate realistic AI avatars & full-body videos with OmniAvatar API. Audio-driven, high-quality results. API docs, free trial & commercial licensing via PiAPI. Landing page: https://piapi.ai/omniavatar Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: video - Vendor: alibaba ### Facts - Audio-driven full body video generation: Our OmniAvatar API generates full-body videos directly from audio inputs, enabling synchronized and expressive motion outputs. - Realistic Lip-Sync and Fluid Body Movements: OmniAvatar AI has improved realistic and lip-sync accuracy, while also display natural and fluid body movements. - Pixel-wise multi-hierarchical audio embedding: OmniAvatar captures richer and more detailed audio features through a multi-level pixel-wise embedding architecture. - Enhanced Controllability: Our OmniAvatar API allows fine control over generation - users can modify background scenes and emotional tone directly through prompts. - SOTA Performance: OmniAvatar achieved superb performance on HDTF, AVSpeech and cropped-face evaluation benchmarks, demonstrating state-of-the-art capability. - Seamless Integration: Employs LoRA-based training to achieve smooth fusion between audio and text features, improving overall coherence. ### Related entities - OmniAvatar API - alibaba - PiAPI ## FAQ ### What is OmniAvatar? OmniAvatar is an AI technology by Alibaba that allows you to generate AI avatars from your imagination. With PiAPI , you can now create AI avatars with our OmniAvatar API today! ### What is the OmniAvatar API? OmniAvatar API provides advanced AI-driven avatar generation capabilities, supporting high-fidelity photorealistic and stylized outputs for virtual identity and creative production pipelines. ### How do I use the OmniAvatar API? PiAPI offers API endpoints for avatar generation. Documentation includes full API references, request examples, and usage guides. ### Can I use OmniAvatar for commercial projects? Yes, OmniAvatar-generated avatars can be used for commercial applications such as content creation, virtual influencers, brand assets, advertising, and interactive experiences. ### Does OmniAvatar support NSFW or restricted content? No, OmniAvatar follows strict content safety guidelines and does not allow NSFW, explicit, or harmful avatar generation. All inputs and outputs are filtered to prevent disallowed content. ### Does OmniAvatar support audio-driven full-body generation? Yes, OmniAvatar AI can generate full-body avatar videos directly from text and audio inputs, resulting in synchronizing lip movement and body motion. Our AI avatar API is suitable for developers and content creators. ### Can I control scenes or emotions via prompts? Yes, OmniAvatar AI API allows fine control through prompts, you can modify background scenes and specify emotional tone while preserving identity consistency. # OmniHuman 1.5 API via PiAPI > Create realistic AI human avatars and talking-head videos with OmniHuman 1.5. Audio-driven lipsync, full-body avatars, and API access via PiAPI. Try the playground or integrate via API. Landing page: https://piapi.ai/omnihuman-1-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/omni-human-api/omni-human-1-5 ## Product details - Category: video - Vendor: bytedance ### Facts - Audio Semantics-Driven Expressive Motion: OmniHuman 1.5 excels in interpreting speech content, timing, and prosody to generate natural gestures, pauses, and body movement beyond basic lip synchronization. - Text-Guided Scene & Action Control: Our OmniHuman API allows users to explicitly direct camera motion, character actions, timing and scene elements through text instructions for AI avatar generation. - Multi-Character & Multi-Audio Scene Generation: OmniHuman 1.5 AI API animates multiple characters within a single scene, each driven by independent audio tracks and coordinated interactions. - Long-Horizon Avatar Generation: OmniHuman 1.5 AI maintains motion coherence, expressiveness, and temporal consistency in video sequences exceeding one minute. - Diverse Character Styles & Appearance: OmniHuman supports a wide range of character styles and visual identities while preserving realism and expressiveness. - Temporal Identity Preservation: With pseudo last frame identity preservation technique, OmniHuman 1.5 prevents appearance drift across frames. - Multimodal Fusion Pipeline: Our OmniHuman 1.5 API jointly processes text, audio and visual inputs through shared attention mechanisms so each modality contributes optimally to the avatar generation. - Context-Aware Emotional Performance: OmniHuman API delivers emotionally rich animation by aligning motion, expression, and timing with semantic and contextual cues from audio and text. - SOTA Performance: OmniHuman-1.5 achieves superior results over leading academic baselines by leveraging a cognitive dual-system architecture. ### Related entities - OmniHuman 1.5 API - bytedance - PiAPI ## FAQ ### What is OmniHuman 1.5? OmniHuman-1.5 is a powerful avatar generation model from the OmniHuman framework from Bytedance As an upgrade from OmniHuman-1 , Bytedance OmniHuman 1.5 achieves better avatar quality for your imagination. Try OmniHuman 1.5 API with PiAPI today! ### What is the OmniHuman 1.5 API? OmniHuman 1.5 API provides advanced AI-driven avatar generation capabilities, supporting high-fidelity photorealistic and stylized outputs for virtual identity and creative production pipelines. ### How can I use OmniHuman? PiAPI offers API endpoints for avatar generation. Documentation includes full API references, request examples, and usage guides. Also check out our OmniHuman pricing for more details on Omni Human 1.5 API credit usage. ### How does OmniHuman compare to other AI video generation tools? OmniHuman is an avatar generation model that specializes in AI avatar generation, as compared to other general-purpose video generation tools. Check out our blog comparison between OmniHuman 1.5 vs Kling AI Avatar to find out more! ### Does OmniHuman 1.5 support NSFW or restricted content? Please refer to our NSFW policy for more information and follow the guidelines. ### Is OmniHuman available? Yes, PiAPI provides OmniHuman 1.5 API and an Omni Human 1.5 playground. Go to our OmniHuman AI video generator to experiment with audio-driven virtual human AI avatar. ### Can I control scenes or emotions via prompts? Yes, OmniHuman 1.5 AI API allows fine control through prompts, ideal for image to lipsync tasks, while also modifying background scenes and specifying emotional tone, giving you full control over your generation. Try OmniHuman now! # Pixal3D API via PiAPI > PiAPI is preparing Pixal3D API access for teams that want image-to-3D generation in apps, tools, and production pipelines. Landing page: https://piapi.ai/pixal3d-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/pixal-3d-api/create-task ## Product details - Category: 3d - Vendor: hunyuan ### Facts - Image-to-3D workflow: Prepare an image input, create an asynchronous generation task, and retrieve the resulting 3D asset when support launches. - Coming soon: Output formats and final integration details will be confirmed when Pixal3D support launches. # Qwen Image API via PiAPI > Qwen Image API provides multilingual text rendering (26+ languages) and precise image editing via PiAPI. Landing page: https://piapi.ai/qwen-image Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/qwen-image-api/text-to-image ## Product details - Category: image - Vendor: alibaba ### Related entities - Qwen Image API - alibaba - PiAPI ## FAQ ### What is Qwen Image? Developed by Alibaba, Qwen Image is a powerful T2I model capable of image generation and editing. At PiAPI, we provide Qwen AI API, perfect for developers and content creators. ### What languages does Qwen Image API support for text rendering? Qwen Image supports both alphabetic languages like English and logographic scripts such as Chinese, ensuring accurate typographic detail across diverse language systems. ### Can Qwen Image handle image editing for professional applications? Absolutely. Qwen Image is designed for high-precision image editing tasks, including style transfer, object insertion and removal, detail enhancement, and text editing within images, making it suitable for professional use cases. ### What artistic styles can Qwen Image generate? Qwen Image can create a wide range of artistic styles, from photorealistic scenes to impressionist paintings and vibrant anime aesthetics. ### Is Qwen Image API suitable for creating virtual avatars? Yes, with its advanced semantic and appearance editing capabilities, Qwen Image API can transform input images into diverse styles, perfect for virtual avatar creation. # Seed Audio 1.0 API via PiAPI > Create speech with BytePlus Seed Audio 1.0 via PiAPI, using permitted voice references plus speed, pitch, loudness, format, and sample-rate controls. Landing page: https://piapi.ai/seed-audio-1-0-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/byteaudio-api/seed-audio ## Product details - Category: audio - Vendor: bytedance ### Facts - Voice-reference guidance: Use permitted reference audio to guide speech generation. - Developer controls: Adjust speech rate, pitch, loudness, format, and sample rate for your workflow. ### Related entities - Seed Audio 1.0 API - BytePlus - PiAPI ## FAQ ### What is Seed Audio 1.0 API? Seed Audio 1.0 is a text-to-speech API available through PiAPI with voice-reference and output controls. ### How is Seed Audio usage billed? Seed Audio 1.0 usage is billed per second of generated audio output. # Seedance 2.0 API via PiAPI > Use Seedance 2.0 API for text-to-video, first/last-frame, and omni-reference video generation. Compare $0.07-$0.50/sec pricing, docs, demo, and free credits. Landing page: https://piapi.ai/seedance-2-0 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/seedance-api/seedance-2 ## Product details - Category: video - Vendor: bytedance ### Related entities - Seedance 2.0 API - bytedance - PiAPI ## FAQ ### What is Seedance 2.0 API? Released in February 2026, Bytedance Seedance 2.0 AI is an advanced video generation model designed for cinematic, ultra realistic and immersive experiences. Aligning with industry standards, Bytedance Seedance 2.0 brings video generation to the next level, suitable for high fidelity and cinematic videos. Additionally, our Seedance AI API supports watermark removal. Start using Seedance 2.0 API with PiAPI today! ### How is Seedance 2.0 different from Seedance 1.0 Pro and Lite? Seedance 2.0 is the successor of Seedance 1.0 Pro and Seedance 1.0 Lite, offering improved scene consistency, upgraded motion quality and more robust prompt following. It is better suited for production-grade storytelling and complex scenes. ### How do I get access to Seedance 2.0? To start using Seedance 2.0 API, sign up at PiAPI's workspace, obtain your API key and follow the Seedance API documentation. Please read our content moderation system for safe usage of our Seedance AI API. ### Does Seedance 2.0 have an API? Yes. You can integrate Seedance 2.0 via PiAPI using a simple REST API. Use your X-API-Key to create tasks (text-to-video or image-to-video), then poll the task endpoint to retrieve the output video URL. ### How do I get a Seedance 2.0 API Key? Create a PiAPI account and sign in to the Workspace. Then go to the API Key page to copy your X-API-Key (you can use free credits to test Seedance 2.0 before topping up). ### What is Seedance 2.0 pricing? Seedance 2.0 is priced per second of generated video. The two main tiers are seedance-2-preview (Quality) and seedance-2-fast-preview (Fast). Watermark removal is priced separately. See the pricing section on this page for the latest rates. ### What is seedance-2-mini? Seedance-2-mini is the most affordable option in the Seedance 2.0 family. It generates roughly 2x faster than seedance-2-fast at similar quality, making it ideal for rapid prototyping and high-volume test generations. Available at 720p resolution. ### Does Seedance support image-to-video? Yes. Seedance supports image-to-video. Provide one or more input image URLs along with your prompt to generate a video conditioned on the images. ### How do I use face / real-person references? Face references require the Private Asset Library. Upload your images via the Asset API, wait for status=Active (verification passed), then use asset:// in seedance-2-less-restriction or seedance-2-fast-less-restriction. This ensures compliance and consistent character identity across shots. ### Is there free access for Seedance 2.0 API? Yes. When you first sign up for a PiAPI workspace, you receive free credits that can be used to try Seedance 2.0 API along with other models. This lets you test Seedance 2.0 generations, tune prompts and validate your integration before paying. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # Seedance 2.5 API via PiAPI > Seedance 2.5 is a production video-generation model available through PiAPI with 480p, 720p, and 1080p output. Landing page: https://piapi.ai/seedance-2-5 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/seedance-api/seedance-25 ## Product details - Category: video - Vendor: bytedance ### Facts - Available resolutions: 480p, 720p, and 1080p - API pricing: $0.15/s at 480p; $0.35/s at 720p; $0.80/s at 1080p ### Related entities - Seedance 2.5 API - bytedance - PiAPI # Seedream 5 Lite API via PiAPI > Powered by Bytedance, Seedream 5.0 Lite is a powerful image generation model that delivers high image quality up to 3K. Get started with the Seedream 5.0 API today! Landing page: https://piapi.ai/seedream-5-lite Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/seedream-api/seedream-5 ## Product details - Category: image - Vendor: bytedance ### Related entities - Seedream 5 Lite API - bytedance - PiAPI ## FAQ ### What is Seedream 5.0 Lite? Released by Bytedance, Seedream 5.0 Lite is a unified image generation model that supports high-quality text-to-image output and image editing using reference images. As the successor of Seedream 4.0, the model has improved capabilities. Through PiAPI, you can generate results in both 2K and 3K resolutions. Start today! ### How do I provide reference images for editing? The Seedream 5.0 AI allows users to include up to 10 reference image URLs. The prompt should describe the edit you want to apply. ### What aspect ratios are supported? Seedream 5 AI API supports a diverse format output, 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 4:5, and 5:4. ### What output formats are available? Seedream 5 Lite supports PNG and JPEG output formats. If you do not specify an output format, PNG is used by default. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # Seedream 5 Pro API via PiAPI > Generate premium 1K/2K images with reference image support via a single API call — no subscription, pay per image. Landing page: https://piapi.ai/seedream-5-pro Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/seedream-api/seedream-5 ## Product details - Category: image - Vendor: bytedance ### Related entities - Seedream 5 Pro API - bytedance - PiAPI ## FAQ ### What is the difference between Seedream 5 Pro and Seedream 5 Lite? Seedream 5 Pro offers higher quality output with 1K and 2K resolutions, while Lite supports 2K and 3K. Pro has reference image support with the first image free, while Lite has flat pricing with no reference image charges. Pro is optimized for professional workflows requiring premium quality. ### How does reference image pricing work? The first reference image is included free with each generation. Any additional reference images cost $0.003 each. This allows you to guide the generation with visual references at minimal cost. ### What resolutions are available? Seedream 5 Pro supports 1K (default) and 2K resolutions. 2K renders at roughly twice the pixel dimensions of 1K. Note that 3K is only available on the Lite model. ### What is the less-restriction model? The less-restriction variant supports generation with face assets that have passed Private Asset Library review. Upload the face asset, wait until its status is Active, then reference the verified asset in the request. ### Can I get a refund if I'm not satisfied? Yes, we offer refunds for unused credits. Please contact our support team for assistance with refund requests. ### How do I get started? Sign up at piapi.ai, add credits to your account, and you can start generating images immediately via the API or our web workspace. # Skin Tokens API via PiAPI > Skin Tokens is an automatic rigging API. Submit a GLB character mesh and receive a rigged GLB containing the original mesh, a fitted skeleton, and per-vertex skinning weights. Landing page: https://piapi.ai/skin-tokens-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/tools/skin-tokens-api ## Product details - Category: 3d - Vendor: qubico ### Facts - Rigging without a rigger: Rigging a character by hand is slow specialist work. Skin Tokens fits a skeleton and solves per-vertex skinning weights automatically, so a mesh becomes animatable without a rigging pass. - GLB in, GLB out: Input is a valid glTF 2.0 binary (GLB) of up to 80,000 triangles, given as a publicly accessible URL or a base64 data URI. Output is a rigged GLB at output.model_file. - Flat $0.30 per task: Every rig task costs $0.30 regardless of mesh complexity, so per-asset cost is predictable when rigging a whole character library. - Asynchronous by design: Create a task, then poll the fetch endpoint until it reaches a terminal state. Long meshes never block your request thread. - Webhook notifications: Supply config.webhook_config.endpoint and an optional secret to get a callback when a task completes or fails. De-duplicate callbacks by task_id and fetch the task to confirm the final state. - Semantic bone naming: Choose original for compatibility, mixamo for mixamorig:* names, or ue5 for UE5 mannequin names. Naming is topology-based and tolerates variable joint counts. ### Related entities - Skin Tokens API - qubico - PiAPI ## FAQ ### What does the Skin Tokens API do? It automatically rigs a 3D character mesh. You submit a GLB mesh and receive a rigged GLB containing the original mesh, a fitted skeleton, and per-vertex skinning weights. ### What mesh can I send? A valid glTF 2.0 binary (GLB) of no more than 80,000 triangles, provided either as a publicly accessible URL or as a base64 data URI. A clean humanoid mesh in a neutral pose generally produces the most reliable rig. ### How much does it cost? $0.30 per rig task. ### Can I choose bone names? Yes. bone_names defaults to original for compatibility. Set it to mixamo for mixamorig:* names or ue5 for UE5 mannequin names. The mapping follows armature topology rather than fixed joint indexes. ### Is the API synchronous? No. Rigging is an asynchronous task: create the task, then poll the fetch endpoint until it reaches a terminal state. You can also register a webhook to be notified when the task completes or fails. # SkyReels API via PiAPI > Generate human centric videos with 33 distinct facial expressions and 400 natural movement combinations, reflecting true human emotions in the output videos Landing page: https://piapi.ai/skyreels Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/skyreels-api/create-task ## Product details - Category: video - Vendor: piapi ### Facts - Human-Centric Precision: SkyReels V1 focuses on capturing authentic human emotions with its ability to generate 33 distinct facial expressions coupled with over 400 movement combinations. - Text-to-Video & Image-to-Video: Utilize our video diffusion transformer to effortlessly convert text and images into vivid video stories tailored to your vision. - Cinematic Aesthetics and Lighting: Each frame generated mirrors Hollywood-level cinematic quality, ensuring professional-grade video content through immaculate composition and actor positioning. - Unparalleled Training: Crafted through fine-tuning on the HunyuanVideo dataset, inclusive of 10 million high-quality film and TV clips, guaranteeing superior output grounded in rich cinematic history. - State-of-the-Art Performance: SkyReels V1 stands shoulder to shoulder with leading proprietary models like Kling and Hailuo, offering competitive open-source quality in text-to-video conversion. - Versatile Application: Ideal for filmmakers, content creators, and marketers seeking to enhance engagement with visually stunning narratives that effectively communicate and resonate with audiences. - User-Friendly API Integration: Seamlessly integrate the SkyReels V1 API into your existing workflow to empower your team with tools designed for creativity and efficiency. - Collaborative Innovation by Kunlun: Developed by the esteemed innovators at Kunlun, where cutting-edge technology meets creative empowerment. - Commercial Use: Subscribers to the Premium Plan are allowed to use the generated video outputs for commercial purposes to suit their needs. ### Related entities - SkyReels API - piapi - PiAPI ## FAQ ### What is SkyReels Video Generator? SkyReels V1 is a human-centric video generation AI model developed by Kunlun . It is the first and most advanced open-source human-centric video foundation model, fine-tuned from HunyuanVideo on tens of millions of high-quality film and television clips. ### What is the SkyReels API? The SkyReels API is provided by PiAPI , designed to give developers access to the SkyReel V1 model. PiAPI uses its inference stack and hardware to deliver efficient, scalable video generation. With the SkyReels API, you can integrate SkyReel V1 human video generation into your apps and platforms. ### What input types does the SkyReel V1 model support? SkyReels supports texts and reference images, unlocking a wide range of creative opportunities. ### What makes SkyReels V1 different from other video creation models? SkyReels V1 excels with its human-centric design, offering precise facial expressions, movement combinations, and cinematic aesthetics from extensive training on high-quality film data. ### Can I use videos generated from the SkyReel API for commercial purposes? PiAPI does not restrict lawful use of the generator and its API; you may use generated video for lawful commercial purposes within the limits of the original model license. ### Are there refunds? No, we do not offer refunds. When you first sign up for PiAPI Workspace, you receive free credits to try SkyReels V1 or the SkyReel API (Pay-as-you-go) before paying. # Sora 2 API via PiAPI > Experience OpenAI's Sora 2, the next generation of video generation with unprecedented realism, physics accuracy, and creative control. Access Sora 2 through PiAPI's powerful API. Landing page: https://piapi.ai/sora-2 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/sora2-api/text-to-video ## Product details - Category: video - Vendor: openai ### Facts - Cinematic Quality at Scale: Generate photorealistic videos in seconds. Sora Video AI captures lifelike details, natural lighting, and realistic textures. Deliver studio-grade content without the production overhead. - Real-World Physics Simulation: Experience authentic motion and interaction. AI Sora understands how objects move, collide, and behave in physical space. Every scene feels grounded and believable. - Precise Prompt Control: Describe your vision and get exactly what you need. From character actions to scene composition, Sora 2 interprets complex instructions with exceptional accuracy so you can iterate faster. - Developer-First API: Integrate in minutes with clean, well-documented endpoints. Built on open source AI principles, our API offers flexible parameters and reliable performance. Ship video features without the complexity. - Advanced Camera Direction: Control every shot with precision. OpenAI Sora delivers professional camera movements, angles, and transitions. Get broadcast-quality cinematography with studio-grade control. ### Related entities - Sora 2 API - openai - PiAPI ## FAQ ### What is Sora 2? Sora 2 is OpenAI's advanced video generation model that creates realistic and imaginative videos from text descriptions or image inputs. It represents a significant leap in AI-powered video synthesis with improved quality, physics accuracy, and creative control. ### How can I access the Sora 2 API? Sora 2 API is provided by PiAPI to enable developers to integrate OpenAI's Sora 2 video generation capabilities into their own applications with simple API calls! ### What video resolutions and durations does Sora 2 support? Our Sora 2 API supports video generation up to 1080p resolution for pro mode with variable durations ranging 4 to 12 seconds. ### What makes Sora 2 different from other video generation models? Sora 2 stands out with its exceptional understanding of physics, complex prompt adherence, multi-shot consistency, and photorealistic quality. It maintains character and scene coherence while offering advanced camera control and cinematographic capabilities. ### Can I use Sora 2 for commercial projects? Yes, videos generated through PiAPI's Sora 2 API can be used for commercial purposes. Please review our terms of service and OpenAI's usage policies for specific guidelines and limitations. ### What is the typical generation time for Sora 2 videos? Generation time varies based on video length and complexity, but typically ranges from 2-5 minutes for standard requests. Longer videos and more complex prompts may take additional time to ensure quality output. # Trellis 2 API via PiAPI > Generate 3D assets from text or images with the Trellis 2 API. View examples, pricing, and developer-ready API docs on PiAPI. Landing page: https://piapi.ai/trellis-2-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/trellis2-api/create-task ## Product details - Category: 3d - Vendor: microsoft ### Related entities - Trellis 2 API - microsoft - PiAPI ## FAQ ### What type of scenarios can Trellis 2 API be used for? Trellis 3D API can be used in a variety of use cases. If you are interested in 3D character creation, our API could be used to create 3D characters from images. Or if you have a website for 3D assets trading, this tool could be used to populate the website with AI generated 3D models. ### Can I use 3D models generated from the Trellis 2 API for commercial purposes? Given the commercial license of the Trellis.2 API model, all 3D models generated from our API using that model will be available for commercial use as needed by 3D artists or creators! ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits, which can be used for free AI 3D model generation and API calls! # Trellis 2 Playground via PiAPI > Experiment with Trellis 2 API via PiAPI's universal playground. Upload an image and generate high-quality 3D assets using Trellis.2. Landing page: https://piapi.ai/trellis-2-playground Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/trellis2-api/create-task ## Product details - Category: 3d - Vendor: microsoft ### Facts - Image to 3D Generation!: PiAPI will be the first to offer image to 3D Trellis 3D and Trellis API - simplifying 3D asset generation workflow of 3D artists around the world! - Text to 3D Generation: PiAPI will continually observe the latest research on 3D generation and deploy any model that shows any promising result in text-to-3D generation. - Asynchronous API Calls: The API will be designed to allow for asynchronous calls, allowing developers to "submit and forget" tasks, and fetch for the task when completed. - Accelerated Hardware Infrastructure: Our accelerated hardware infrastructure is specifically optimized for latent diffusion model inference work, ensuring workloads to be executed with maximum efficiency and minimum latency. - Custom Inference Frameworks: Our custom inference framwork used for latent diffusion model workloads focus on optimized runtime performance and resource management, preventing bottlenecks from traditional framework. - Bulk Generation Available: The perfect generation option for developers to populate their 3D projects with large quantity of custom 3D models from custom prompts and/or images. Contact us on Discord with your prompts and we will get back to you with a pricing and a timeline! - Simple and Flexibe Payment Plans: Our Pay-as-you-go option allows developers to test the API with free credits, and purchase credits for consumption, and manually or automatically top-up their credits as their usage varies. - Commercial Use: Given the commercial license of the Trellis model, all images generated from our API using that model will be available for commercial use as needed by the creators or 3D artists. ### Related entities - Trellis 2 Playground - microsoft - PiAPI ## FAQ ### What is the Trellis 3D Model? The Trellis 3D Generation Model is developed by Microsoft's Trellis Research Team It is the most advanced 3D generation AI model in the market, dedicated to simplify creator's 3D workflow with the power of generative AI! ### What is the Trellis 3D API? The Trellis API are offered by PiAPI based on the Trellis model. Given our accelerated hardware infrastructure and custom-developed inference framework, PiAPI is able provide APIs for these two models at very market competitive costs while maintaining performance and minimizing latency! ### What type of 3D model can I generate with the text to 3d model? With the significant quality, users can use the picture to 3d model with various kinds using textual prompts or image prompts in their workflow! ### What type of scenarios can Trellis be used for? The photo to 3D model can be used in a variety of usecases. For example, if you are using Object AI to create your 2D images, you can use the AI tool to generate the corresponding 3D model; our 2D to 3D model AI generator can produce high quality 3D asset. Or if you are interested in 3D character creation, our AI generator could be used to create made up 3D characters. Or if you have a website for 3d assets trading, this tool could be used to populate the website with AI generated 3d models. ### Can I use 3D models generated from the Trellis API for commercial purposes? Given the Apache2.0 license of the Trellis 3D, all images generated from our API using that model will be available for commercial use as needed by 3D artists or creators! # Trellis 3D API via PiAPI > Want to use the best open source 3D generation model from Microsoft Research? Try Trellis 3D and Trellis API from PiAPI! Landing page: https://piapi.ai/trellis-3d-api Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/trellis-api/create-task ## Product details - Category: 3d - Vendor: microsoft ### Facts - Image to 3D Generation!: PiAPI will be the first to offer image to 3D Trellis 3D and Trellis API - simplifying 3D asset generation workflow of 3D artists around the world! - Text to 3D Generation: PiAPI will continually observe the latest research on 3D generation and deploy any model that shows any promising result in text-to-3D generation. - Asynchronous API Calls: The API will be designed to allow for asynchronous calls, allowing developers to "submit and forget" tasks, and fetch for the task when completed. - Accelerated Hardware Infrastructure: Our accelerated hardware infrastructure is specifically optimized for latent diffusion model inference work, ensuring workloads to be executed with maximum efficiency and minimum latency. - Custom Inference Frameworks: Our custom inference framwork used for latent diffusion model workloads focus on optimized runtime performance and resource management, preventing bottlenecks from traditional framework. - Bulk Generation Available: The perfect generation option for developers to populate their 3D projects with large quantity of custom 3D models from custom prompts and/or images. Contact us on Discord with your prompts and we will get back to you with a pricing and a timeline! - Simple and Flexibe Payment Plans: Our Pay-as-you-go option allows developers to test the API with free credits, and purchase credits for consumption, and manually or automatically top-up their credits as their usage varies. - Commercial Use: Given the commercial license of the Trellis model, all images generated from our API using that model will be available for commercial use as needed by the creators or 3D artists. ### Related entities - Trellis 3D API - microsoft - PiAPI ## FAQ ### What is the Trellis 3D Model? The Trellis 3D Generation Model is developed by Microsoft's Trellis Research Team It is the most advanced 3D generation AI model in the market, dedicated to simplify creator's 3D workflow with the power of generative AI! ### What is the Trellis 3D API? The Trellis API are offered by PiAPI based on the Trellis model. Given our accelerated hardware infrastructure and custom-developed inference framework, PiAPI is able provide APIs for these two models at very market competitive costs while maintaining performance and minimizing latency! ### What type of 3D model can I generate with the text to 3d model? With the significant quality, users can use the picture to 3d model with various kinds using textual prompts or image prompts in their workflow! ### What type of scenarios can Trellis be used for? The photo to 3D model can be used in a variety of usecases. For example, if you are using Object AI to create your 2D images, you can use the AI tool to generate the corresponding 3D model; our 2D to 3D model AI generator can produce high quality 3D asset. Or if you are interested in 3D character creation, our AI generator could be used to create made up 3D characters. Or if you have a website for 3d assets trading, this tool could be used to populate the website with AI generated 3d models. ### Can I use 3D models generated from the Trellis API for commercial purposes? Given the Apache2.0 license of the Trellis 3D, all images generated from our API using that model will be available for commercial use as needed by 3D artists or creators! # Veo 3 API via PiAPI > Access Google Veo 3 via API on PiAPI. Generate cinematic videos with native audio and realistic motion. Docs, pricing, and examples for developers. Landing page: https://piapi.ai/veo-3 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/veo3-api/text-to-video ## Product details - Category: video - Vendor: google ### Facts - Configurable Display Output: Generate videos in landscape or portrait, at 720p or 1080p, for social and cinematic delivery formats. - Major Audio Upgrade: Veo 3 adds integrated audio generation, enabling dialogue and ambient sound in a single workflow. - Best-in-Class Quality: Veo 3 improves realism, physics, and prompt adherence for cinematic results. - Veo 3 Fast Variant: Fast mode supports rapid T2V and I2V generation for experimentation, ads, and scalable production. - Advanced Creative Control: Keep stronger visual and audio coherence while steering style, pacing, and output composition. - Content Safety & Compliance: Outputs are checked for memorized content risks to reduce copyright, privacy, and bias concerns. - Asynchronous Execution: Submit Veo 3 tasks in the background and retrieve results later with callback-friendly workflows. - Frame Control: Fine-tune first and last frame behavior for smoother transitions and more precise creative direction. ### Related entities - Veo 3 API - google - PiAPI ## FAQ ### What is Google Veo 3? Google Veo 3 is an advanced Veo 3 AI model for cinematic video generation. ### How do I access Veo 3 via API? Use PiAPI's Veo AI API endpoints to submit prompts and receive generated videos. ### What makes Veo 3 different from other AI video models? Veo 3 stands out with native audio generation, temporal consistency, and more realistic motion. ### Can I control duration and resolution? Yes, you can control duration and resolution when calling the Veo AI API. ### Is Veo 3 suitable for production use? Yes. Many teams use Veo-based video generation for ads, trailers, and concept visualization. ### Do you support Veo 3.1? Yes, PiAPI supports Veo 3.1 API. Visit the Veo 3.1 page and start incorporating the Veo AI architecture into your workflow today! ### Does PiAPI support the fast mode? Yes, PiAPI offers Veo 3 API in Veo 3 fast mode for rapid workflows. # Veo 3.1 API via PiAPI > Create cinematic videos with Veo 3.1 from Google, the latest Veo AI API built on the Veo 3 architecture. Experience sharper realism, smoother motion, and richer audio fidelity - or revisit Veo 3 for the earlier model. Landing page: https://piapi.ai/veo-3-1 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/veo31-api/text-to-video ## Product details - Category: video - Vendor: google ### Facts - Enhanced Realism & Fidelity: The Veo 3.1 API captures true-to-life textures, lighting, and physics for production-quality storyboarding and direction-driven storytelling powered by Veo AI. - Improved Prompt Understanding: Built on Veo 3, Veo 3.1 delivers better prompt adherence, accurately translating complex text and visual cues into context-aligned scenes for T2V and I2V generations. - Richer Native Audio & Dialogue: With Veo AI API, creators can generate natural speech, ambient layers, and multi-speaker dialogue synchronized perfectly with visual output. - Ingredients to Video: The Veo 3.1 API supports up to three reference images to guide generation, giving developers precise control over characters, objects, and visual style across scenes. - Scene Extension: Extend clips to one minute or longer using the Veo 3.1 API, maintaining visual continuity and background audio, ideal for cinematic storytelling and long-form creative output. - First and Last Frame Control: Define exact start and end frames with the Veo AI API, ensuring smoother transitions and stronger narrative direction in every generated video. - Object Insertion: Easily integrate new objects into scenes using the Veo AI API, while maintaining lighting, perspective, and photorealistic consistency. - Veo 3.1 Fast Variant: An optimized version of Veo 3.1, the Veo 3.1 Fast API allows developers to create videos with sound while maintaining high quality and optimizing for speed, ideal for scalable, rapid creative workflows. ### Related entities - Veo 3.1 API - google - PiAPI ## FAQ ### What is Google Veo 3.1? Google Veo 3.1 is an advanced Veo AI model for cinematic video generation, offering physics-aware motion, realistic lighting, and native audio, accessible through the Veo 3.1 API. At PiAPI , we offer a wide range of API services for creators and developers. ### How do I access Veo 3.1 via API? Use PiAPI's Google Veo API endpoints to submit prompts and receive generated videos. See Veo API Docs for details. ### What makes Veo 3.1 different from other AI video models? Veo 3.1 improves temporal consistency, cinematic lighting control, and physics-accurate motion, delivering lifelike realism and smoother frame coherence compared to earlier Veo AI models. ### Can I control duration and resolution? Yes, the duration and resolution parameters are configurable when calling the Veo 3.1 API for flexible generation control. ### Is Veo 3.1 suitable for production use? Yes, many teams use Veo AI for ads, trailers, and concept visualization. The Veo 3.1 API is optimized for scalable production workflows. ### How does pricing work for Veo 3.1 on PiAPI? For Veo 3.1, we offer $0.24 per second for generations with audio and $0.12 per second for generations without audio. For Veo 3.1 Fast, we offer $0.09 per second for generations with audio and $0.06 per second for generations without audio. ### Does Veo 3.1 support NSFW content? Please refer to our NSFW policy for more information and follow the guidelines. # AI Video Background Remover via PiAPI > Remove video backgrounds instantly with PiAPI's AI background remover. Studio-quality cutouts without green screens or manual masking, plus a Background Remover API for automation. Landing page: https://piapi.ai/video-remove-background Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/tools/video-remove-background-api ## Product details - Category: video - Vendor: piapi ### Facts - AI-Powered Detection: Our Video Background Remover API utilizes frame-accurate segmentation, removing video background cleanly. - High-Quality Results: Our Video Background Remove API produces studio-quality cutouts with crisp edges and stable motion. - Batch Processing: Process multiple videos simultaneously with our efficient batch processing capabilities, saving time and resources for large-scale video background removal tasks. - Background Remover API: Easy-to-use REST API for seamless integration into your applications, workflows, or content management systems. ### Related entities - AI Video Background Remover - piapi - PiAPI # AI Video Watermark Remover via PiAPI > Remove watermarks from videos with PiAPI's AI watermark remover. Fast, accurate results and a developer-ready API for automation. Landing page: https://piapi.ai/video-remove-watermark Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - AI Video Watermark Remover - piapi - PiAPI # AI Video Upscale Tool via PiAPI > Upscale and enhance videos instantly with PiAPI's Video Upscale API. Improve resolution and sharpen details so your videos look great everywhere. Landing page: https://piapi.ai/video-upscale-tool Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/tools/video-upscale-api ## Product details - Category: video - Vendor: piapi ### Facts - High-Quality Video Upscaling: Use our Video Upscale API to sharpen details and enhance resolution so your videos look crisp on any screen. - Production-Ready Performance: Process large batches of videos with predictable pricing and latency, ideal for editors, platforms, and automation pipelines. - Optimized for Real-World Content: Designed for user-generated and commercial footage, with support for common resolutions and frame ranges used in production. ### Related entities - AI Video Upscale Tool - piapi - PiAPI # Kling Virtual Try-On via PiAPI > Build an AI-powered virtual fitting room for your ecommerce store. Use PiAPI’s Virtual Try-On API to place garments onto model images and create realistic outfit previews with simple integration and pricing. Landing page: https://piapi.ai/virtual-try-on Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/kling-api/virtual-try-on-api ## Product details - Category: video - Vendor: kling ### Related entities - Virtual Try-On - piapi - PiAPI # Wan 2.2 API via PiAPI > Wan2.2 by Alibaba is a gamer in the Open Source Video Generation Space! Landing page: https://piapi.ai/wan/wan-2-2 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/wanx-api/create-task ## Product details - Category: video - Vendor: alibaba ### Facts - Mixture-of-Experts (MoE) Architecture: Wan 2.2 introduces an advanced MoE architecture to its video diffusion models. By dispersing the denoising process across specialized expert models for different timesteps, it enhances overall model capacity without increasing computational costs. - Cinematic-Level Aesthetics: Achieve professional narrative control with Wan 2.2's in-depth shot language capabilities. Fine-tune elements like lighting, color, and composition for detailed and versatile video styles. - Complex Motion Generation: With a dataset expansion of 65.6% more images and 83.2% more videos compared to its predecessor, Wan2.2 delivers fluid, complex motion rendering that matches and exceeds top-tier models globally. - High-Definition Hybrid TI2V: Experience fast, high-quality video generation at 720P and 24fps, thanks to Wan2.2's sophisticated Wan2.2-VAE. Compatible with consumer-grade GPUs, it supports diverse applications from academia to industry. - Versatile Generation Methods: Wan 2.2 supports text-to-video and image-to-video generation, providing flexibility whether you're driven by creative storytelling or precise instructional content. ### Related entities - Wan 2.2 API - alibaba - PiAPI ## FAQ ### What makes Wan 2.2 stand out from previous versions like Wan 2.1? Compared to Wan2.1, Wan2.2 significantly upgrades generation quality through a larger dataset and the innovative Mixture-of-Experts (MoE) architecture, which allows for advanced control over video attributes without increasing computational load. ### Can Wan2.2 be used on standard consumer-grade graphics cards? Yes, the model is optimized to run efficiently on GPUs like the NVIDIA 4090, making high-definition video generation accessible to a wider range of users. ### What kind of control does Wan2.2 offer over video aesthetics? It provides granular control over aspects such as lighting, color, and composition, allowing for customizable and precise cinematic style generations. ### Is Wan2.2 open-source? Absolutely, Wan2.2 is fully open-source, enabling the community to access and leverage its powerful capabilities for various projects. ### What is Wan 2.2 API? The Wan API is provided by PiAPI, designed to facilitate developers' access to the Wan 2.2 model. With Wan API, developers can easily integrate the Wan2.2 model's exceptional video generation capability. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you are given free credits to try our playground and our API (specifically the "Pay-as-you-go" service) before making payments! # Wan 2.6 API via PiAPI > Access Wan 2.6 API for cinematic I2V and T2V video generation. See pricing, claim free credits, read docs, and start generating high-quality videos on PiAPI. Landing page: https://piapi.ai/wan/wan-2-6 Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/wan-api/wan26-text-to-video ## Product details - Category: video - Vendor: alibaba ### Facts - Seamless Audio-Visual Sync: Wan 2.6 natively supports high-fidelity video generation with synchronized audio, aligning visuals, voices, and ambient sound. - Multimodal Input: Use text, image, audio, and video as inputs to build richer generation workflows through the Wan 2.6 API. - Cinematic Aesthetics: Wan 2.6 supports professional storytelling with stronger motion dynamics, visual stability, and up to 15-second 1080p generations. - Asynchronous API Calls: Submit tasks with callbacks and receive results when jobs finish, without blocking your app flow. - Multi-Image Control: Support reference-driven generation and editing with commercial-grade character and scene consistency. - Precision, Motion, and Visual Fidelity: Wan 2.6 improves instruction following, motion smoothness, and output quality across both T2V and I2V workflows. - Adjustable Aspect Ratios: Generate output in multiple aspect ratios for social, entertainment, product, and broader content workflows. - World-Level Understanding: Wan 2.6 combines real-world understanding and structured generation for more coherent, cinematic visual narratives. ### Related entities - Wan 2.6 API - alibaba - PiAPI ## FAQ ### What is Wan 2.6? Wan 2.6 is an advanced AI model, a successor of Wan 2.5 developed by Alibaba . It supports multimodal input, synchronized audio, and professional-grade 1080p video generation for both T2V and I2V workflows. ### How much does Wan 2.6 API cost? Wan 2.6 costs $0.08/second for T2V and I2V 720P generation, and $0.12/second for T2V and I2V 1080P generation. ### How do I access Wan AI via API? Use PiAPI's Wan 2.6 API endpoints to submit prompts and retrieve generated videos programmatically. ### Can I control duration and resolution? Yes. You can configure both duration and resolution when generating Wan 2.6 videos through the API. ### Does Wan 2.6 API support I2V and T2V generations? Yes. Wan 2.6 supports both I2V and T2V workflows, making it suitable for both creators and developers. ### Can I generate NSFW content using Wan 2.6 API? Please refer to our NSFW policy for more information and follow the guidelines. # Wanx API via PiAPI > Wan 2.1 by Alibaba generating high-quality videos with significant leap forward in AI-driven visual content creation! Landing page: https://piapi.ai/wanx Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/wanx-api/create-task ## Product details - Category: video - Vendor: alibaba ### Facts - SOTA Performance: Wan 2.1 consistently outperforms existing open-source models and commercial solutions, setting a new benchmark in video generation. - Text-to-Video & Image-to-Video: Wanx 2.1 excels across multiple tasks, including Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, enabling versatility and broad applications in the video generation field. - Fast Video Generation: Generate a 5-second 480P video in approximately 4 minutes, even without optimization techniques like quantization, making us the most efficient video generation tools available! - Realistic Motion Handling: Wan 2.1 excels at simulating complex bodily movements, maintaining accurate spatial-temporal relationships for seamless, natural motion sequences in video creation. - Multilingual Support: Wanx 2.1 supports text prompts in both Chinese and English, making it accessible to a wide range of users globally, from developers to content creators. - Top Performance Leader: Ranked among the top three video generative models on the VBench leaderboard with an impressive score of 84.7%, Wanx leads the field in motion handling and multi-object interaction. - Customizable Artistic Styles: With over 100 artistic style templates available, including oil painting and cyberpunk, users can create unique visual content in various genres to match their specific needs. - Wide Industry Applications: Wanx 2.1 can be used in advertising, short video production, digital restoration of historical footage, and immersive teaching materials, making it a versatile tool for various industries. - Commercial Use: Subscribers to the Premium Plan are allowed to use the generated video outputs for commercial purposes to suit their needs. ### Related entities - Wanx API - alibaba - PiAPI # Z-Image API via PiAPI > Powered by Alibaba's Tongyi-MAI, Z-Image Turbo API delivers ultra high-speed, high-fidelity AI image generation. Get started with Z-Image Turbo API now with PiAPI! Landing page: https://piapi.ai/z-image-turbo Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/z-image-api/text-to-image ## Product details - Category: image - Vendor: alibaba ### Related entities - Z-Image Turbo API - alibaba - PiAPI ## FAQ ### What is Z Image Turbo API? By Alibaba, Z-Image Turbo is the core image generation model under the Z-Image model. It is designed specifically for generating high-quality images via API. Image editing is supported separately through the Z-Image Editing variant. Start creating with Z Image Turbo API today! ### What is the difference between Z-Image Turbo and Z-Image Editing? Z-Image Turbo focuses on image generation from prompts, while Z-Image Editing is designed for modifying existing images. Both models are variants of the Z-Image model and serve different use cases. ### How do I generate images with Z Image Turbo API? You can generate images using PiAPI's Z Image API endpoint by sending a text prompt and configuration parameters. Documentation includes full request examples and integration guides. ### Does Z Image Turbo support image editing? Z-Image Edit is still in the process of being released. If you require image editing workflows, please explore other PiAPI image APIs such as Qwen Image API or Nano Banana Pro API that support edit operations. ### Is there free access for Z Image Turbo API? When you first sign up with us on PiAPI's Workspace, you will be given free credits to try selected APIs, including Z Image Turbo API if it is enabled for your account. ### Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI's Workspace, you will be given free credits to try our API before making payments! ### How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! # AI Advertisement Video Generator via PiAPI > Upload a product, add an optional actor or logo, write a script, and generate a campaign-ready advertisement with Seedance 2.5 or Kling 3.0. Landing page: https://piapi.ai/ai-advertisement-video-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/seedance-api/seedance-25 ## Product details - Category: video - Vendor: piapi ### Related entities - AI Advertisement Video Generator - Seedance 2.5 - Kling 3.0 - PiAPI # AI Age Filter via PiAPI > AI Age Filter is a portrait image-editing workflow that creates an older or younger version while preserving recognizable identity. Landing page: https://piapi.ai/ai-age-filter Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/gpt-image/gpt-image-api ## Product details - Category: tool - Vendor: piapi ### Related entities - AI Age Filter - AI age progression - AI age regression - PiAPI # AI Face Rater via PiAPI > AI Face Rater is a selfie-to-dashboard workflow powered by GPT Image 2 image editing. Landing page: https://piapi.ai/ai-face-rater Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - AI Face Rater - GPT Image 2 - PiAPI # AI Headshot Generator via PiAPI > AI Headshot Generator turns an uploaded portrait into a professional LinkedIn-ready headshot using GPT Image 2 image-to-image editing with outfit and backdrop presets. Landing page: https://piapi.ai/ai-headshot-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - AI Headshot Generator - GPT Image 2 - PiAPI # AI Sports Video Generator via PiAPI > AI Sports Video Generator turns an uploaded photo into a Korean Baseball or FIFA World Cup fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Landing page: https://piapi.ai/ai-sports-video-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - AI Sports Video Generator - GPT Image 2 - Seedance 2.0 - PiAPI # Claymation AI Generator via PiAPI > Claymation AI Generator creates clay-style images and clay AI videos from text, images, or uploaded video using Seedream 5 Lite, Kling 3.0, and Seedance 2.0 Fast. Landing page: https://piapi.ai/claymation-ai-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - Claymation AI Generator - Seedream 5 Lite - Kling 3.0 - Seedance 2.0 - PiAPI # FIFA World Cup AI Video Generator via PiAPI > FIFA World Cup AI Video Generator turns an uploaded portrait into a World Cup-inspired football fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Landing page: https://piapi.ai/fifa-world-cup-ai-video-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - FIFA World Cup AI Video Generator - GPT Image 2 - Seedance 2.0 - PiAPI # Ghibli Style AI Generator via PiAPI > Ghibli Style AI Generator converts an uploaded photo into a Studio Ghibli-inspired anime illustration with Qwen Image Edit, then animates the result into a short cinematic scene with Kling 3 Omni. Landing page: https://piapi.ai/ghibli-style-ai-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - Ghibli Style AI Generator - Qwen Image Edit - Kling 3 Omni - PiAPI # Korean Baseball AI Video Generator via PiAPI > Korean Baseball AI Video Generator turns an uploaded portrait into a Korean baseball-inspired stadium fan-cam image with GPT Image 2, then animates the approved image into a short video with Seedance 2.0 Fast. Landing page: https://piapi.ai/korean-baseball-ai-video-generator Base URL: https://api.piapi.ai Authentication: use the API key from your PiAPI workspace. Documentation: https://piapi.ai/docs/overview ## Product details - Category: tool - Vendor: piapi ### Related entities - Korean Baseball AI Video Generator - GPT Image 2 - Seedance 2.0 - PiAPI ## Seedance 2.5 API: Fewer Restrictions, More Creative Freedom Learn what Seedance 2.5 Less Restriction changes, how reviewed face references fit the workflow, and how to start a policy-qualified API request. Seedance 2.5 Less Restriction is a distinct PiAPI task type for permitted projects that need more room for reference-guided creative direction. Its documented product distinction is support for verified face assets after Private Asset Library review, alongside the same Seedance 2.5 video workflow. It is a product option, not a promise that every request will be accepted. PiAPI content policy, applicable law, and rights and consent requirements still apply. Start with the Seedance 2.5 API , then use the current Seedance 2.5 API documentation for the live request contract. ## Standard and Less Restriction at a glance The two options use the same hosted model family and request shape. Choose the task type that matches the project and keep the rest of the request grounded in the current schema. Dimension Standard Less Restriction Task type seedance-2.5 seedance-2.5-less-restriction Best fit General text and reference-guided video Permitted workflows that need the Less Restriction path and reviewed face references Face references Follow the standard supported-reference rules Upload the face asset to the Private Asset Library and wait for review before use Other references Image, video, and audio references supported by the current schema Image, video, and audio references supported by the current schema Output 4-30 second integer duration; 480p, 720p, or 1080p 4-30 second integer duration; 480p, 720p, or 1080p Policy PiAPI content policy applies PiAPI content policy applies "Less Restriction" describes the selected PiAPI task type. It does not promise universal acceptance, and all requests remain subject to review. Use the label to choose a workflow, then make the request specific, rights-cleared, and easy to inspect. ## When this option is useful The option is most useful when identity, references, and creative direction need to stay consistent across a planned scene. Examples include: - a character-led short film using a rights-cleared face asset that has passed review; - an editorial or fashion concept with mature themes that remains within policy; - a branded or narrative scene where one character must keep the same identity across references; - a multimodal storyboard that combines an image, a short video reference, an audio cue, and a precise prompt. The controllable parts of the workflow matter more than a label: use material you have the right to use, describe each reference's role, choose a sensible duration and aspect ratio, and review the returned video before treating it as final. ## Prepare access and references Keep the setup in a short, repeatable sequence: - Create a PiAPI account and an API key. - Decide whether the project needs seedance-2.5 or seedance-2.5-less-restriction . - If the request uses a face reference, upload it through the Private Asset Library . - Wait until the asset status is Active , then retain the verified asset_id or asset:// reference required by the current guide. - Use rights-cleared material and keep the request within the PiAPI content policy . The library handles an asynchronous lifecycle: an upload starts in Processing , then becomes Active or Failed . Managed assets are useful for a recurring cast or product subject. For a one-off reference, the current guide also documents ephemeral URL handling; read that guide for retention and quota details rather than copying an older limit into a new integration. ## Use the playground, then the API The Seedance 2.5 playground is a practical place to check a prompt, task type, resolution, and reference order. Once the inputs are clear, carry the same values into an asynchronous task request. This is a request example using a reviewed face asset. It is a schema example, not a claim that this article ran a live generation. ``` curl --request POST "https://api.piapi.ai/api/v1/task" \ --header "X-API-Key: $PIAPI_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "seedance", "task_type": "seedance-2.5-less-restriction", "input": { "prompt": "The fictional lead in @image1 walks through a rain-lit gallery, pauses beside a blue sculpture, and turns toward the camera in one continuous cinematic shot.", "duration": 8, "resolution": "720p", "aspect_ratio": "16:9", "image_urls": ["asset://asset-REVIEWED-CHARACTER"] } }' ``` The top-level model is seedance , while task_type selects the 2.5 variant. The prompt names @image1 because the first supplied image is the character reference. The current documentation also supports @video1 and @audio1 when the corresponding arrays are supplied. A placeholder that has no matching reference is rejected, so keep the numbering aligned. The response contains a task_id and a status such as Pending , Processing , Completed , or Failed . Poll GET /api/v1/task/{task_id} until the task reaches a terminal status, then retrieve the returned video URL promptly. For webhooks, use the current unified webhook documentation rather than inventing a callback field. ## A prompt template for a controlled scene Use a small reference pack and give every asset one job: ``` Create one continuous 8-second cinematic scene. @image1 is the fictional lead character; preserve the face, dark jacket, and silver bag. The setting is a quiet gallery after rain. Begin with a medium tracking shot, move to a gentle front-facing close shot, and keep the lighting cool and consistent. Show the character crossing the room, stopping beside the blue sculpture, and looking toward the camera. No logos, captions, duplicate subjects, outfit changes, or random text. ``` Specificity makes the result easier to review. Name the subject, action, camera, lighting, and exclusions in that order. If the image reference has a different shape from the requested output, remember that the reference image's aspect ratio takes precedence under the current 2.5 contract. ## Duration, resolution, and pricing The current 2.5 API reference accepts any integer duration from 4 to 30 seconds, with 5 seconds as the default when omitted. It lists 480p, 720p, and 1080p output; 4K is not available. The supported aspect ratios are 21:9 , 16:9 , 9:16 , 4:3 , 1:1 , and 3:4 . The Less Restriction task is billed per generated second. The following rates were read from the live 2.5 pricing section on September 8, 2026; check the current pricing page before budgeting a production. Resolution Less Restriction rate 480p $0.165 per second 720p $0.385 per second 1080p $0.88 per second For example, an 8-second 720p output has a base generation charge of 8 x $0.385 = $3.08 before any input-reference component. The pricing section describes video-reference input at half the selected unit rate, so a request with video references depends on both the output duration and the input video duration. Read the live contract for the exact calculation and current limits before sending a long reference. ## How 2.5 differs from 2.0 Seedance 2.5 is the current subject here, with the dedicated seedance-2.5 and seedance-2.5-less-restriction task types, 4-30 second output, and 480p, 720p, or 1080p resolution. Seedance 2.0 remains available for existing integrations with its own task variants and limits. Task names, reference behavior, duration, and pricing are version-specific, so use the Seedance 2.0 API guide when maintaining a 2.0 integration instead of copying a 2.0 request into a 2.5 workflow. ## Content policy and responsible use Less Restriction gives a permitted project more creative room while retaining the rules that govern the hosted service. Before you send a request: - use only references for which you have the necessary rights and consent; - keep prompts and source media within the PiAPI content policy ; - describe the intended subject and action clearly so the result can be reviewed; - treat a rejected request as a signal to revisit the brief and inputs rather than repeatedly resubmitting the same material. The policy page owns the full list of restrictions and enforcement details. This article explains the workflow and task selection; it is not a replacement for that policy or for the live API contract. ## FAQ ## What is Seedance 2.5 Less Restriction? It is the seedance-2.5-less-restriction PiAPI task type. Its documented distinction is support for generation with face assets after they pass Private Asset Library review. It still uses the Seedance 2.5 request schema and PiAPI content policy. ## Does Less Restriction mean every prompt is accepted? No. The task type provides more room for permitted reference-guided work, but each request and its references still need to satisfy PiAPI policy, applicable law, and rights requirements. ## Do I need a reviewed face asset for every request? You need a reviewed face asset when you use a face reference in this workflow. Text-only requests and other supported references follow the current 2.5 schema. Upload a face asset, wait for Active , and use the verified reference documented by the Private Asset Library guide. ## Which task type should I send? Use seedance-2.5 for the standard workflow or seedance-2.5-less-restriction when a permitted project needs that task type's broader creative path and reviewed face-reference support. Keep the model value as seedance . ## What resolutions and durations are available? The current 2.5 reference accepts integer durations from 4 through 30 seconds and lists 480p, 720p, and 1080p. It also lists six aspect ratios: 21:9 , 16:9 , 9:16 , 4:3 , 1:1 , and 3:4 . ## How is Seedance 2.5 billed? Billing is per generated second, based on task type and resolution. Video references add an input-duration component described in the live pricing section. See current PiAPI pricing before estimating a production run. ## Where can I read the current content policy? Read the PiAPI content policy . It is the source for policy scope and enforcement; the API documentation is the source for request fields, limits, and status behavior. ## Start with a reviewed reference workflow Choose a permitted scene, prepare a clear reference, and test the request in the Seedance 2.5 playground . When the inputs are ready, read the Seedance 2.5 API reference and send the same task type, prompt, references, duration, resolution, and aspect ratio through your application. Source note: product fields, limits, task types, and rates were checked against the Seedance 2.5 configuration and live API reference on September 8, 2026. The draft does not claim a live generation, conversion lift, or universal acceptance rate. ## Bring Your Own Kling Account: API Access via PiAPI Bring your existing Kling account into an API workflow with PiAPI Host-your-account. Explore seat pricing, account credits, and availability in the dashboard. Your team already has a Kling account, a subscription, and a growing list of video ideas. The next requirement is an API workflow around that account. PiAPI's Host-your-account (HYA) option brings those two parts together: API access through PiAPI, with generation work processed by the Kling account you operate. For developers, studios, and automation teams, the appeal is continuity. An existing account can remain part of the workflow as video generation becomes part of an application, an internal tool, or a content production process. HYA adds a PiAPI service seat around that account; the account's credits and entitlements remain relevant. ## Your Kling account, an API workflow Bring your own account, sometimes shortened to BYOA, describes the arrangement. You operate the Kling account, while PiAPI provides the HYA seat and API access. Submitted work is routed through the connected account, and PiAPI returns the results when processing completes. Account connection and service activation are completed in the dashboard. An existing Kling account or subscription can therefore be used through PiAPI's API via HYA. This does not turn a consumer subscription into an official Kling developer API key, transfer its credits to PiAPI, or change its underlying entitlements. The value is an API workflow that uses the account you already operate. ## What the HYA seat covers The current PiAPI Kling API page lists Host-your-account at $10 per seat per month . That is a monthly service-seat price, separate from the cost of your Kling account and any PiAPI subscription plan. The current billing terms and available options are shown in the dashboard before purchase. PiAPI's published HYA explanation describes generations as using the connected Kling account's credits, with no additional PiAPI generation charge beyond the seat under that description. It also distinguishes the seat charge from PiAPI plan fees. The dashboard's current billing terms take precedence when you make a purchase decision. For budgeting, keep the account cost, HYA seat cost, and any optional PiAPI plan cost separate. A seat is not a bundle of unlimited video generations. The number of jobs an account can process still depends on its available credits, service state, and applicable limits, so a monthly seat price alone does not establish a fixed cost per finished video. ## Credits and capacity stay with your account The published HYA description assigns concurrent-job limits and watermark entitlements to the connected Kling account. A PiAPI public-pool plan limit should not be read as an HYA guarantee. Likewise, upgrading a PiAPI plan should not be assumed to change the entitlements of an independently operated Kling account. Account availability also matters. A seat does not make an unavailable account ready to process work, and available credits do not guarantee an instant result. Current account and service status are available in the dashboard. ## A service for one account or several The Kling product page advertises multiple-account hosting and load balancing . This is relevant to a studio or team that already operates several accounts and wants them within a PiAPI API workflow. Each account's credits, subscription state, and capacity remain part of the overall picture. More accounts do not establish a guaranteed linear increase in throughput. The practical value of an additional seat depends on the account behind it and the workload. Seat availability and purchasing are handled in the dashboard, where the current service options can be evaluated against your team's needs. ## Who is this option for? HYA is designed for people who already operate a Kling account and want that account to be part of an API-based application or production workflow. Examples include a studio with an existing subscription, a developer building an internal video tool, or a team evaluating automation around its own account credits. PiAPI also offers shared pay-as-you-go access. That is a separate service option with its own billing and capacity terms. HYA's specific proposition is access through your own account. The Kling API product page presents the available options, and activation or purchase is completed in the dashboard. ## Common questions ### Can I use an existing Kling subscription through PiAPI's API? Yes. Host-your-account is intended for API work through a Kling account you operate. The account needs usable credits and an available service state. Current HYA availability and activation are shown in the dashboard. ### Does the $10 seat replace my Kling subscription? No. The published $10-per-seat monthly fee is for the PiAPI HYA service seat. Your Kling account costs and entitlements remain separate, as do any PiAPI plan fees. Review current billing terms in the dashboard. ### Are concurrent jobs and watermark removal included automatically? The published HYA explanation ties those entitlements to your own Kling account. It does not establish a single numeric concurrency limit or a watermark-removal guarantee for every account. ### Where can I activate or purchase HYA? Visit the PiAPI dashboard to activate or purchase the service. Account connection and service management are completed in the dashboard. ## Bring your account to PiAPI Keep your existing Kling account at the center of the workflow, with PiAPI providing the HYA API service around it. Visit the PiAPI dashboard to activate or purchase Host-your-account and review current availability and billing. Pricing and product references checked September 7, 2026. The $10 monthly seat listing is separate from account costs and PiAPI plan fees. Current availability and purchase terms are shown in the dashboard. ## MiniMax H3 Explained: Open-Weight Video Model Learn what MiniMax H3 is, why its open-weight ecosystem matters, what PiAPI supports, how much it costs, and where its video quality fits. MiniMax H3 is a video-generation model from MiniMax that combines multimodal input understanding with generated video and native audio. The upstream release makes model resources publicly available under the MiniMax H3 Community License Agreement, so open-weight is more precise than simply "open-source." Through PiAPI, developers can use a managed endpoint for short text-to-video and first-frame image-to-video generation without setting up a local model environment. The short version: MiniMax H3 is worth trying when low-cost iteration and native audio matter. Its results are promising for short-form concepts and early creative exploration, but exact text, complex scenes, and brand-sensitive compositions still need human review. ## What is MiniMax H3? MiniMax H3 is MiniMax's video-and-audio generation model. The upstream H3 release is broader than any single hosted API integration may be: MiniMax's materials discuss multimodal understanding, native stereo audio, and higher-end output capabilities. Those claims should not automatically be treated as features of every third-party endpoint. PiAPI provides one practical access path through an asynchronous task API. Its current documented scope is narrower: txt2video for prompt-based generation and img2video for animating a supplied first frame, with 512p or 768p output, requested durations from 5 to 15 seconds, three aspect ratios, and native stereo audio in the returned MP4. For request bodies, polling, and implementation code, see the MiniMax H3 API guide . ## Why are developers and creators paying attention? MiniMax H3 sits at an interesting intersection. The public-weight ecosystem attracts developers who want to understand or experiment with the model beyond a closed web interface. Native audio makes the output useful for quick concept work because a clip can arrive with video and an audio stream in one file. PiAPI adds a managed route for teams that want to test prompts and workflows without first operating local inference infrastructure. The cost also makes iteration practical. At the currently documented PiAPI rates, a five-second generation costs $0.15 at 512p or $0.25 at 768p. That does not make H3 automatically the cheapest or best model for every use case, but it lowers the cost of exploring several short concepts before a more expensive production pass. ## Is MiniMax H3 open source? Open-weight is the safer term. The downloadable H3 release is governed by the MiniMax H3 Community License Agreement , not an unrestricted open-source license. The agreement includes territorial, acceptable-use, distribution, attribution, and commercial-use conditions. Its default Applicable Territory excludes the United States, European Union, United Kingdom, and South Korea. In practical terms, public weights do not automatically mean that anyone can self-host, redistribute, modify, or commercially deploy MiniMax H3 everywhere. Check the current license for your jurisdiction and use case. PiAPI is a separate hosted service, so the downloadable-model license should not be treated as a description of PiAPI’s own access terms or capabilities. ## What does MiniMax H3 support through PiAPI? PiAPI's current MiniMax H3 documentation exposes: - Text-to-video through txt2video . - First-frame image-to-video through img2video . - 512p and 768p output. - Requested durations from 5 to 15 seconds. - 16:9, 9:16, and 1:1 aspect ratios. - Native stereo audio and asynchronous task creation and polling. Broader upstream references to 2K output, multi-shot generation, video-to-video, or additional reference modes should not be presented as PiAPI features unless the current MiniMax Generate Video API documentation confirms them. ## How much does MiniMax H3 cost through PiAPI? Based on PiAPI rates verified on August 13, 2026, a successful five-second task costs $0.15 at 512p or $0.25 at 768p. The two new tests generated for this article used those settings and consumed 1,500,000 and 2,500,000 PiAPI points respectively, for an expected $0.40 total. Prices can change, so check the current documentation before budgeting a larger batch. ## How well does MiniMax H3 perform? This article uses two new PiAPI outputs generated specifically for this review. The 512p terrarium text-to-video task completed successfully in 129.93 seconds. The terrarium remains the clear central subject and the restrained push-in is easy to follow, but the requested fern-unfurling detail is subtle at this short duration. That makes it a useful focused composition example, though not evidence that H3 will reproduce every small natural movement precisely. The 768p paper-bird image-to-video task completed successfully in 150.41 seconds. The supplied bird-and-bowl composition remains recognizable while the model adds gentle motion, but the simple graphic source is interpreted with some visual softness and should be reviewed frame by frame before being treated as a controlled animation. Together, the fresh outputs show a practical value proposition: H3 can produce inspectable short clips for focused scene exploration and first-frame animation at a low per-attempt cost. They also reinforce the need for review. Both files include video and audio streams, but stream presence alone does not establish audio quality, synchronization, or prompt adherence. This two-generation set is practical review evidence, not a benchmark. ## Who should try MiniMax H3 through PiAPI? MiniMax H3 is a sensible candidate for developers prototyping an asynchronous video workflow, creators iterating on short-form concepts, and product or lifestyle teams exploring motion directions before investing in final production assets. It is also useful for teams that want video and an audio stream returned together. It is a weaker fit when typography must be exact, a brand asset must remain unchanged, a complex action must work on the first attempt, or a clip must publish without human approval. For a matched model-selection discussion, see the MiniMax H3 vs Seedance 2.0 comparison . ## Conclusion MiniMax H3 is compelling because it combines an open-weight ecosystem, native audio, and an inexpensive managed API path. Through PiAPI, developers can explore short text-to-video and image-to-video workflows at a relatively low per-generation cost. The tradeoff is that promising footage is not guaranteed footage: exact text, complex scenes, and brand-sensitive outputs still require review. Ready to explore the model? Try MiniMax H3 through PiAPI , or read the MiniMax H3 API guide for the complete request and polling workflow. ## Frequently asked questions ### What is MiniMax H3? MiniMax H3 is a MiniMax video-generation model with multimodal understanding and native audio. PiAPI currently exposes a managed subset for text-to-video and first-frame image-to-video. ### Is MiniMax H3 open source? Open-weight is the more precise term. The public release is governed by the MiniMax H3 Community License Agreement, which includes territorial and usage restrictions. ### Does PiAPI support MiniMax H3 audio? PiAPI documents native stereo audio in the returned MP4. Audio presence does not by itself prove that every requested sound or synchronization cue was followed accurately. ### How much does a five-second MiniMax H3 generation cost? At the rates verified on August 13, 2026, a successful five-second task costs $0.15 at 512p or $0.25 at 768p through PiAPI. ### Can MiniMax H3 generate video from an image through PiAPI? Yes. PiAPI documents first-frame image-to-video through the img2video task type. The current API guide covers the request format and polling flow. ## Seedance 2.5 for AI Short Films: A 30-Second Storytelling Workflow See three Seedance 2.5 short films, then learn a simple creative workflow for scripting dialogue, guiding characters, and shaping a complete 30-second scene. Can a single AI-generated video feel like a scene from a film, with characters who speak, react, and reach a real ending in 30 seconds? We tried three approaches with Seedance 2.5 through PiAPI: a family drama made from text alone, a thriller guided by character and location images, and a science-fiction scene shaped with images and sound. Each one has five spoken lines and a complete story turn. The result: Seedance 2.5 can carry a compact dramatic scene when the idea is simple and the direction is precise. The best results came from treating the prompt like a miniature screenplay rather than a list of visual effects. The simplest workflow is to choose one emotional turn, write five short lines, divide the scene into four timed beats, and give every reference image one clear job. ## Watch Three Seedance 2.5 Short Films These are the original 30-second outputs. They have not been rebuilt from selected shots, so you can judge the dialogue, pacing, character consistency, and endings as they came back from the model. ## Example 1: The Watch — Text-Only Emotional Drama Two siblings meet in their late father's workshop. Eli has kept a stopped pocket watch because winding it feels like accepting that his father is gone. Mara sees the same action differently: starting the watch means their own lives can continue. The script ``` Mara: "You kept Dad's watch." Eli: "I kept the hour he left." Mara: "Then let it move." Eli: "If I wind it, he's really gone." Mara: "No. It means we're still here." ``` The warm workshop, rain-blue window, two adults, and pocket watch stay coherent throughout the scene. All five lines are clear, the characters remain easy to distinguish, and the lip sync works well. The blocking becomes quiet after the opening, while the final winding action is more subtle than requested. Even so, it plays as a complete emotional moment and would need only a modest trim. Creative lesson: text alone can establish mood and performance, but the story should revolve around one readable object and one emotional decision. ## Example 2: Last Platform — Image-Controlled Thriller Detective Noa Vale and courier Samir Hale enter a station that closed ten years ago. The lights come on. An announcement promises a final train. Then something approaches from the darkness. The script ``` Noa: "This station closed ten years ago." Samir: "Then who keeps turning on the lights?" Station announcement: "Last train arriving." Noa: "There are no tracks." Samir: "Noa... look behind you." ``` The character references do useful work here. Noa keeps her navy coat and flashlight; Samir keeps his olive jacket and messenger bag. Wet tiles, amber lights, and the empty tunnel give the scene a consistent visual identity. The dialogue and lip sync are clear, and the slow move toward a tense two-shot creates a strong ending. One requested beat does not land literally: Noa says there are no tracks, but rails remain visible. That mismatch is a good reminder that a spoken line does not guarantee the matching visual detail. Creative lesson: references improve identity and production design, but important reveals still need to be visually simple and easy for the model to show. ## Example 3: Tomorrow Is Approaching — Multimodal Sci-Fi Pilot Aya Venn and engineer Ren Kade stare through an observation window at a fractured blue anomaly. Their navigation system is not showing where Earth went. It is showing something that has not happened yet. The script ``` Aya: "Navigation says Earth is gone." Ren: "Navigation is reading tomorrow." Ship voice: "Correction. Tomorrow is approaching." Aya: "Can we turn away?" Ren: "We already did. That's why it found us." ``` This was the most controlled of the three scenes. Aya and Ren remain recognizable in their white-orange and silver costumes, while the circular observation deck and damaged planet hold together from beginning to end. All five lines are understandable, lip sync is good, and the warning pulse with reactor hum is clearly audible without overpowering the voices. The threat brightens more than it advances, so the final image is calmer than the script suggests. It still works as an original movie-style scene and has the strongest sense of a preplanned world. Creative lesson: a small, purposeful reference pack can make an ambitious setting feel designed rather than improvised. ## What the Three Films Taught Us The input method changed how much control we had, but more control did not remove the need for a simple story. Approach What it did well What still needed judgment Text only Mood, dialogue, and a prop-led emotional scene Blocking and the final hand action Character and location images Faces, wardrobe, props, and atmosphere Literal delivery of the missing-tracks reveal Images plus sound World-building, visual continuity, and sonic tone Strength of the final camera and threat movement Human playback found all five lines clear in every film. Characters stayed distinguishable, lip sync was good, and none of the three had an audio problem. That makes each output usable, but not perfect. The important distinction is whether an edit can improve the pacing or whether the central story beat would need a new generation. ## Start With One Story Turn Thirty seconds is enough for a scene, but not for a complicated plot. Give the audience one question and one change in meaning. In The Watch , grief becomes permission to continue. In Last Platform , confidence becomes fear. In Tomorrow Is Approaching , a navigation error becomes a glimpse of the future. A useful four-beat structure is: Time Story job What to show 0–7s Set up One place, the characters, and a readable problem 7–15s Change A reply that makes the opening mean something new 15–22s Escalate One discovery, announcement, or irreversible action 22–30s Resolve A final line followed by an image that can breathe Five short lines were enough for all three films. Leaving gaps between them gave the characters time to look, move, and react. If every second is filled with dialogue, the scene may feel like a rushed voice demo instead of a film. ## Choose How Much Control the Scene Needs You do not need references for every idea. A contained drama can work from text when the exact faces and room design are not essential. Add references when identity, wardrobe, props, or the world must carry across the full shot. For the thriller, we used one image for each character and one for the empty station. The thriller pack contained Detective Noa Vale in a navy raincoat with a brass flashlight, Samir Hale in an olive jacket with a canvas bag, and an abandoned tiled platform. The science-fiction pack followed the same pattern: Pilot Aya Venn, Engineer Ren Kade, and the observation deck. One original sound reference supplied the warning pulse and reactor hum. Each asset had one job, which made the prompt easier to understand and the result easier to evaluate. For more short-form input patterns, see these Seedance 2.5 prompt and reference examples . Use the current Seedance 2.5 API documentation for today's request limits and pricing. ## Write the Prompt Like a Director A production prompt should tell the model what changes and what must stay stable. Use this order: - Name the characters and repeat their defining visual details. - Keep the action in one location with one lighting setup. - Describe a few motivated camera moves, not a long shot list. - Write the dialogue in exact order with speaker names. - Block unwanted extras, subtitles, logos, identity swaps, and costume changes. Here is the core of the thriller prompt: ``` Create one continuous 30-second cinematic thriller scene using the supplied references. @image1 defines Detective Noa Vale; preserve her face, short black hair, navy raincoat, and brass flashlight. @image2 defines Samir Hale; preserve his face, olive jacket, canvas messenger bag, and build. @image3 defines the abandoned platform. Start behind them, track alongside as Noa raises the flashlight, then move into a tense two-shot. Maintain faces, clothes, props, spatial positions, and screen direction. Spoken dialogue, in this exact order with no narrator and no extra words: Noa: "This station closed ten years ago." Samir: "Then who keeps turning on the lights?" Station announcement: "Last train arriving." Noa: "There are no tracks." Samir: "Noa... look behind you." No music, subtitles, captions, logos, extra passengers, face swaps, outfit changes, duplicate characters, random text, or visible train impact. ``` The continuity instructions are as important as the action. When a character's coat, prop, or position matters to the story, say so directly. ## Review the Result Like an Editor Watch the original from beginning to end before deciding what to regenerate. Listen for every line, check who appears to be speaking, and look for face, wardrobe, prop, and location changes. Ordinary edits can trim dead frames, balance dialogue and ambience, or hold the final image a little longer. Regenerate when the central action is missing, a character changes identity, the dialogue becomes unusable, or the ending tells a different story. For a longer AI short film, make several scenes with the same cleared reference pack. Use the last composition of one scene to plan the opening of the next, and keep screen direction and lighting consistent. ## Technical Notes: Reproduce the Workflow Through PiAPI PiAPI uses an asynchronous task workflow: create the task, save its task ID, poll until it finishes, and download the output promptly. ``` { "model": "seedance", "task_type": "seedance-2.5-less-restriction", "input": { "prompt": "YOUR RECORDED PRODUCTION PROMPT", "duration": 30, "resolution": "720p", "aspect_ratio": "16:9", "auto_upload_assets": true, "image_urls": [ "https://example.com/character-a.png", "https://example.com/character-b.png", "https://example.com/location.png" ] } } ``` As verified on August 12, 2026, PiAPI accepted whole-number Seedance 2.5 durations from 4 to 30 seconds at 480p or 720p, and rejected 1080p. Rechecked on August 19, 2026: 1080p is now supported and priced at $0.80 per second; 4K is still rejected. The current contract supports up to nine image, three video, and three audio references, subject to its combination rules. Check the live request schema before integrating. Our three returned files were 30.08 seconds, 1280×720, H.264 at 24 fps, with stereo AAC audio. The successful videos used seedance-2.5-less-restriction and cost $11.55 each at the observed $0.385 per-second rate. Including six generated reference images and one hosted warning-sound task, the confirmed test total was $34.90 , or about $11.63 per usable scene . Check current PiAPI pricing before budgeting a production. Two earlier standard-task attempts were rejected before generation and consumed no points after restoration. One original text prompt triggered a generic copyright classifier; one fictional portrait was classified as a possible real person. We kept those failures in the task record and used one controlled rerun for each. The less-restriction route does not remove the need for cleared assets or safety review. The Seedance private-asset workflow explains the broader asset process, though its older task examples do not replace the current 2.5 contract. All characters, locations, dialogue, and sound references in this test were original. We used no celebrity likenesses, franchises, logos, licensed footage, samples, or copyrighted dialogue. Task IDs, normalized inputs, charges, media probes, playback notes, and hashes were preserved separately from temporary output URLs. ## Seedance 2.5 Short-Film FAQ ### Can PiAPI generate a 30-second Seedance 2.5 video? Yes. The current PiAPI contract accepts whole-number durations from 4 through 30 seconds. Each of our three returned files probed at 30.08 seconds. ### Does Seedance 2.5 support speech in a short film? The generated files include audio, and dialogue can be written in the prompt. Listen to every original to verify the wording, speaker assignment, voice consistency, and lip sync; an audio track alone does not prove those details are correct. ### How do you improve character consistency? Use one clean reference per character, give each person distinctive hair, clothing, and props, repeat those details in the prompt, and keep the scene in one location. Reuse the same cleared reference pack across connected scenes. ### How many references should a scene use? Use the fewest that have a clear purpose. Our referenced scenes used two character images and one environment image. The API may accept more, but the maximum is not automatically the best creative choice. ### Is editing still necessary? Usually. Trimming, sound balancing, and pacing are normal editorial work. Regenerate when a central story beat, identity, action, or spoken line is missing. ### What should I do if a reference is rejected? Check whether it was a moderation rejection or a failed generation, then review the error, consumed points, and refund state. Use only cleared assets and follow the current less-restriction or asset-upload guidance. Do not repeatedly resubmit disallowed material. ### Is this a Seedance 2.5 benchmark? No. It is a documented three-scene production test. It shows what happened with text-only, image-referenced, and image-plus-audio workflows, not a universal success rate or comparison with other models. ## Make Your First 30-Second Scene Begin with one room, two characters, five short lines, and one change in meaning. Decide what must remain visually consistent, then generate your Seedance 2.5 short-film scene and judge the original before expanding the story. If your next project is commercial rather than narrative, use the Seedance 2.5 product-ad workflow for product references, vertical prompts, and cost-per-usable-clip planning. Evidence note: generation records, visual reviews, media probes, hashes, charges, and human playback were completed on August 12, 2026. Playback confirmed clear dialogue, distinguishable characters, good lip sync, and no audio problems in all three originals; the science-fiction scene also retained its warning pulse and reactor hum. ## MiniMax H3 vs Seedance 2.0: Is Seedance Worth the Higher Price? Compare MiniMax H3's low-cost, open-weight approach with Seedance 2.0's premium video quality using matched PiAPI tests, pricing, and real outputs. Seedance 2.0 through PiAPI is the stronger choice when output quality and control matter most. MiniMax H3 through PiAPI is the more economical choice when you need to iterate cheaply or generate at volume. That is the practical decision behind this MiniMax H3 vs Seedance 2.0 comparison for developers, creative teams, and technical creators. We tested both models through PiAPI on August 11, 2026, using the same prompt or input in three scenarios. Seedance won the main visible decision criterion in each pair, although MiniMax H3 still made attractive footage at one quarter of the tested price. This is a six-output practical test, not a universal benchmark. Direct answer: MiniMax H3 is MiniMax's open-weight video model, while Seedance 2.0 is ByteDance Seed's multimodal video model. Through PiAPI, both generate video with stereo audio; H3 is the lower-cost option, while Seedance offers broader controls and stronger results in our tests. Quick verdict: Seedance 2.0 delivered cleaner dialogue staging, better product-identity preservation, and more convincing complex motion. MiniMax H3 cost 75% less per tested output and remained useful for low-cost concepts and photorealistic restyling. Seedance is the performance choice; H3 is the value choice. This verdict comes from six PiAPI generations run on August 11, 2026. ## Key takeaways - Seedance 2.0 won the main visible decision criterion in all three matched pairs. - MiniMax H3 cost $0.25 per five-second output , compared with $1.00 for Seedance 2.0 Quality. - Both models completed all three tasks, giving each a 3/3 technically usable rate in this small evidence set. - H3's median processing time was 178.86 seconds ; Seedance's was 136.57 seconds , although Seedance had one 432.30-second outlier. - H3 was especially good at turning a stylized product input into a polished photorealistic concept, but it changed the product design. - Seedance handled rain, action progression, physical interaction, and product identity more reliably in the tested outputs. - This comparison evaluates visual performance only; it does not rank speech, sound design, or lip sync. ## Table of contents - MiniMax H3 vs Seedance 2.0 at a glance - How we tested - Test 1: native-audio dialogue - Test 2: product image-to-video - Test 3: complex motion and physics - What the three pairs showed - API feature differences - Pricing, latency, and real cost - Moderation and “uncensored” searches - Which model should you choose? - Limitations - FAQ ## MiniMax H3 vs Seedance 2.0 at a glance The capability rows below describe what PiAPI exposed on the test date. The result rows describe only our six original outputs. Decision point MiniMax H3 through PiAPI Seedance 2.0 through PiAPI Shared generation tasks Text-to-video; first-frame image-to-video Text-to-video; first/last-frame image-to-video Additional exposed modes None used in this comparison Omni-reference with image, video, and audio inputs Exposed duration 5–15 seconds 4–15 seconds Exposed resolution 512p or 768p Quality tier: 480p, 720p, or 1080p Tested setting 768p, 5 seconds, 16:9 Quality 720p, 5 seconds, 16:9 Generated audio Stereo audio documented; stereo AAC present in all tested MP4s Stereo audio documented; stereo AAC present in all tested MP4s Tested rate $0.05 per second $0.20 per second Tested five-second charge $0.25 $1.00 Median processing time 178.86 seconds 136.57 seconds Technically usable outputs 3/3 3/3 Cost per technically usable output $0.25 $1.00 Best fit from these tests Low-cost concepts and photorealistic restyling Product fidelity, complex motion, and polished final footage The price comparison uses H3 768p and the premium seedance-2 Quality task at 720p. Seedance also offers Fast and Mini tiers, but substituting one of those would answer a different price-versus-performance question. ## How we tested MiniMax H3 and Seedance 2.0 We asked one question: does Seedance 2.0's visible performance justify paying four times the H3 rate for a five-second PiAPI generation? We used one output per model in three scenarios: dialogue, product image-to-video, and complex motion. There was no hidden pool of extra attempts. How to read the evidence: - Documented capability means a feature or limit exposed in current PiAPI product configuration or API documentation. - Original observation means something visible in one of the six retained outputs or recorded in its PiAPI task data. - Interpretation means our workflow recommendation based on those facts and observations, not a universal model ranking. Both models received identical submitted prompts within each pair. We matched the five-second duration and 16:9 aspect ratio, then selected the closest quality-oriented resolutions available: H3 at 768p and Seedance Quality at 720p. This resolution mismatch is unavoidable and disclosed; the test does not prove that resolution caused any result. We considered an output technically usable when it completed, downloaded as a valid MP4, had the requested general duration and dimensions, and could be inspected as comparison evidence. “Production approved” is stricter and depends on the requirements of a real creative brief, so this article reports only the technically usable rate. Processing time runs from PiAPI task start to task completion. End-to-end time runs from creation to completion. A failed, moderated, timed-out, or charged rejected task would have remained in the denominator and cost total. In this run, all six tasks completed with no retries, failures, moderation events, or refunds. Visual evaluation considered prompt adherence, visual quality, detail, artifacts, motion and physical interaction, identity consistency, and likely commercial usefulness. The examples below use short comparison paragraphs instead of a numerical scorecard because one generation cannot support precise model-wide scores. ## Test 1: Native-audio dialogue The intended line was “Build faster, create more.” A Windows command-line quoting issue shortened the prompt before submission. Both APIs received the same shortened prompt, so the pair remains matched, but this example tests only a single requested word. We did not buy replacement outputs or conceal the mistake. Exact prompt received by both models ``` Cinematic medium close-up of an adult woman hosting a late-night radio show in a warm studio. She looks into the camera and clearly says, Build ``` Settings: text-to-video; five seconds; 16:9; H3 768p; Seedance Quality 720p; one output per model. MiniMax H3 output from the matched radio-studio dialogue prompt. Seedance 2.0 output from the matched radio-studio dialogue prompt. Seedance creates the more coherent radio-studio scene: its wider composition, console, microphone arm, lighting, and background remain stable without visible text. H3 supplies a sharp, energetic close-up and a consistent presenter, but it adds prominent unsolicited pseudo-lettering that makes the shot harder to use. Seedance wins the visual comparison on composition and production polish. ## Test 2: Product image-to-video This test asked each model to animate the same publication-safe perfume image while retaining its design. The source is a stylized portrait image, while the requested output is landscape. The pair therefore measures aspect-ratio adaptation and identity retention, not perfect first-frame preservation. Shared product input: a stylized teal bottle with a dark square cap, highlight line, circular bottle detail, and marble surface. Original PiAPI-owned evidence asset. Exact prompt ``` Premium fragrance commercial. Preserve the exact bottle shape, teal glass, cap, label placement, and marble surface from the first frame. The camera makes a slow smooth push-in while a pale silk ribbon moves gently behind the bottle and tiny water droplets catch the light. Keep the product centered and unchanged. Realistic materials, restrained motion, no new objects, no rewritten text, no logo changes. ``` Settings: first-frame image-to-video; five seconds; 16:9; H3 768p; Seedance Quality 720p; one output per model. MiniMax H3 product animation generated from the shared reference image. Seedance 2.0 product animation generated from the shared reference image. H3 transforms the flat illustration into an appealing photorealistic perfume advertisement with convincing teal glass, marble, lighting, and restrained fabric movement. It also substantially redesigns the bottle, cap, label, and graphic style. Seedance retains more of the original silhouette, dark cap, highlight, and circular detail while adding a controlled silk backdrop and pedestal. Seedance is better when product identity matters; H3 is attractive for fictional concept development or deliberate restyling. ## Test 3: Complex motion and physics The final prompt combined two subjects, timed actions, rain, clothing movement, a puddle interaction, camera tracking, and generated sound. Five seconds is a demanding limit, so we evaluated whether the key action remained readable rather than expecting every phrase to appear perfectly. Exact prompt ``` Cinematic rainy-night fencing scene in a stone courtyard. Two adult fencers circle once, exchange three fast blade strikes, then the fencer in red parries and steps through a shallow puddle, sending a realistic splash sideways. Their coats react naturally to movement and rain. The camera tracks smoothly from left to right. Metallic blade impacts, rainfall, and footsteps are synchronized. No dialogue, no slow motion, no cuts, no text. ``` Settings: text-to-video; five seconds; 16:9; H3 768p; Seedance Quality 720p; one output per model. MiniMax H3 output from the matched rainy fencing prompt. Seedance 2.0 output from the matched rainy fencing prompt. Seedance gives the stronger action result. The rain is visible, the red and dark-clad fencers remain readable, the exchange progresses across the courtyard, and the final puddle splash connects to the movement. H3 keeps two fighters and a stable environment, but the scene looks flatter, with weaker rain and coat response and less convincing blade-to-hand interaction. Seedance wins on visual motion clarity, atmosphere, physical interaction, and cinematic finish. ## Video quality, prompt adherence, and motion results Test result: Across six PiAPI generations on August 11, 2026, Seedance won the main visible decision criterion in all three matched pairs. H3 cost $0.25 per five-second output; Seedance Quality cost $1.00. Across these three one-shot pairs, Seedance was more dependable at preserving the visible details that mattered. Its dialogue shot contained no pseudo-text, its product shot retained more identity during a difficult reframing task, and its fencing sequence showed clearer environmental and interaction cues. Those benefits matter more when a team needs a final asset than when it needs a rough concept. H3's strongest moment was the product output. Its version did not follow the identity-preservation brief as closely, but the photorealistic restyling looked polished enough to inspire a campaign direction. At $0.25 per attempt, teams could explore four H3 generations for the price of one tested Seedance Quality generation—although four attempts do not guarantee one will match Seedance. Audio performance is outside the scope of this comparison. Technical inspection confirmed that every MP4 contains a stereo AAC track, but the model rankings and workflow recommendations in this article are based on visible output only. ## MiniMax H3 API vs Seedance 2.0 API features Both APIs cover the shared foundation needed for this article: text-to-video and image-conditioned video generation with generated stereo audio. The MiniMax H3 API documentation exposes a simpler surface: txt2video and first-frame img2video , 512p or 768p, 5–15 seconds, and 16:9, 9:16, or 1:1 output. The step-by-step MiniMax H3 API guide covers request bodies, polling, and troubleshooting. The Seedance 2.0 API documentation describes a broader production toolkit. Besides text-to-video, Seedance supports first/last-frame generation and an omni-reference mode that can accept image, video, and audio references. PiAPI exposes 4–15 second durations, six aspect ratios, and Quality, Fast, and Mini task variants. The Quality task supports 480p, 720p, and 1080p. See the Seedance 2.0 API guide and examples for a model-specific walkthrough. These extra Seedance modes did not influence the shared test result: we used only a matching text prompt or first-frame image. They do matter for workflow selection. If a project needs end-frame control, multiple visual references, motion references, or audio references, Seedance offers tools H3's current PiAPI integration does not expose. MiniMax publishes H3 weights , so “open-weight” is the precise description. That gives technically capable teams another deployment path outside a hosted API. It does not mean a local deployment and PiAPI will have identical performance, safety controls, hardware requirements, or task options. ## MiniMax H3 vs Seedance 2.0 pricing, latency, and real cost On August 11, 2026, PiAPI priced H3 768p at $0.05 per requested second and Seedance Quality 720p at $0.20 per requested second. Each five-second H3 task therefore cost $0.25, and each Seedance task cost $1.00. The full test cost $0.75 for H3 and $3.00 for Seedance, or $3.75 combined. Output Processing time End-to-end time Actual charge H3 dialogue 178.28s 178.52s $0.25 Seedance dialogue 136.57s 137.43s $1.00 H3 product I2V 192.19s 193.58s $0.25 Seedance product I2V 432.30s 433.79s $1.00 H3 fencing 178.86s 179.95s $0.25 Seedance fencing 123.30s 123.73s $1.00 H3's median processing time was 178.86 seconds, while Seedance's was 136.57 seconds. Seedance was faster in two pairs, but its product task took 432.30 seconds and prevents a simple “Seedance is always faster” conclusion. Latency varies with queue and workload, so these figures are observations, not service guarantees. Both models completed 3/3 tasks. With no failed or charged rejected attempts, cost per technically usable output equals the task price: $0.25 for H3 and $1.00 for Seedance. The formulas are: ``` usable rate = technically usable outputs / total attempts cost per usable output = total charged generation cost / technically usable outputs ``` This is where the buying decision becomes concrete. H3 is compelling for ideation, prompt exploration, large batches, and workflows where a person will select or edit results. Seedance's premium is easier to justify when identity, motion, physics, and first-pass polish reduce expensive review or regeneration work. Review current PiAPI pricing before budgeting a production batch. ## Is MiniMax H3 uncensored compared with Seedance 2.0? No hosted API should be described simply as “uncensored.” PiAPI's current H3 integration exposes txt2video and img2video ; it does not expose a separate H3 less-restriction task. Seedance 2.0 has separately named less-restriction variants, but “less restriction” does not mean unrestricted use. PiAPI policy and applicable law still apply. It is also important to separate three contexts. MiniMax's open-weight H3 release can be deployed locally under its applicable license and configuration. A third-party host can add its own moderation. PiAPI provides a hosted integration with its documented task types and policies. A claim about one environment does not automatically describe the other two. We did not run explicit-content tests for this article. Readers evaluating allowed content should use the PiAPI content policy and current task documentation rather than social-media claims about MiniMax H3 uncensored access. ## Which model should you choose? Choose Seedance 2.0 Quality when the output is close to a final production asset and the cost of visual errors exceeds the extra generation charge. It was the better choice in our dialogue composition, product-identity, and complex-motion examples. It is also the clearer option for reference-heavy workflows because PiAPI exposes first/last-frame and omni-reference modes. Choose MiniMax H3 when budget, iteration volume, or the open-weight ecosystem matters more than getting the strongest first output. It can make visually appealing footage, and its product restyling showed real creative value. At the tested rate, it supports four attempts for the generation price of one Seedance Quality attempt. Workflow Better fit Why Polished dialogue composition Seedance 2.0 Cleaner tested frame and more coherent studio staging; audio verdict pending Identity-sensitive product animation Seedance 2.0 Preserved more of the source bottle's silhouette and details Fictional product concepts or restyling MiniMax H3 Attractive photorealistic treatment at lower generation cost Complex action and environmental physics Seedance 2.0 Clearer rain, movement progression, splash, and physical interaction High-volume ideation MiniMax H3 $0.25 per tested output versus $1.00 for Seedance Quality First/last-frame or multimodal references Seedance 2.0 Broader PiAPI-exposed reference modes For native audio, wait for the listening review before choosing on speech or synchronization alone. ## Limitations of this comparison This was a six-output PiAPI comparison conducted on August 11, 2026: one attempt per model in three scenarios. It cannot measure repeatability, output variance, or a statistically reliable success rate. A different prompt, seed, duration, mode, provider, or model update could produce a different result. The resolutions were close but not identical: H3 used 768p and Seedance used 720p. The product input was 9:16 while outputs were 16:9. The dialogue prompt was shortened before API submission, though both models received the same final text. Visual assessment is editorial and subjective, even when grounded in exact prompts and retained files. Finally, audio performance was outside the evaluation scope. The conclusions therefore apply to visible output quality, prompt adherence, motion, identity consistency, latency, and cost—not speech accuracy, sound effects, or lip sync. ## Frequently asked questions ### Is MiniMax H3 better than Seedance 2.0? MiniMax H3 is better for low-cost iteration; Seedance 2.0 is better for premium output quality in our tests. Seedance won the visible dialogue, product-fidelity, and complex-motion comparisons. H3 cost 75% less per five-second output, making it attractive when volume and experimentation matter more than first-pass polish. ### Which has better video quality, MiniMax H3 or Seedance 2.0? Seedance 2.0 produced better visible quality across our three matched pairs. It delivered cleaner studio staging, stronger product-identity retention, and more convincing rain and physical interaction. This conclusion applies to six outputs generated through PiAPI on August 11, 2026; it is not a universal model ranking. ### Which is cheaper through PiAPI, MiniMax H3 or Seedance 2.0? MiniMax H3 was cheaper at the tested tiers. H3 768p cost $0.05 per second, while Seedance 2.0 Quality 720p cost $0.20 per second. A five-second output therefore cost $0.25 with H3 and $1.00 with Seedance. Check current PiAPI pricing before budgeting a production batch. ### Which model has better native audio and lip sync? This comparison does not name an audio winner. All six files contained stereo AAC audio, but the evaluation focused on visible results rather than speech accuracy, sound quality, synchronization, or lip sync. The visual portion of the dialogue test favored Seedance. ### Can both models generate video from an image? Yes. PiAPI exposes first-frame image-to-video for MiniMax H3 and first/last-frame generation for Seedance 2.0. Both animated the same perfume input in our test. Seedance retained more of the original design, while H3 transformed it into a more photorealistic but substantially redesigned product concept. ### Which model supports more reference inputs through PiAPI? Seedance 2.0 supports more reference workflows through PiAPI. Its omni-reference mode can accept image, video, and audio references, while first/last-frame mode controls boundary frames. H3's current PiAPI integration supports a text prompt or one first-frame image. We did not score Seedance's extra modes in the matched quality tests. ### Is MiniMax H3 uncensored, and how do its restrictions compare with Seedance 2.0? PiAPI does not expose a less-restriction H3 task type. Seedance offers separately named less-restriction variants, but they are still moderated and subject to policy and law. Local open-weight H3 deployments and other hosts may behave differently, so do not generalize their settings to PiAPI's hosted integration. ### Which API is better for product videos? Seedance 2.0 is the safer choice when brand and product identity must survive animation; it preserved more of our bottle's shape and graphic details. MiniMax H3 is useful for inexpensive concept exploration or intentional photorealistic restyling. Test your own publication-safe product asset before scaling either workflow. ### Are these results a benchmark? No. This is a practical comparison of six outputs: one generation per model across three matched scenarios. It does not establish repeatability, variance, or statistical significance. We publish the prompts, settings, costs, task records, limitations, and every counted result so readers can judge what the evidence does and does not support. ## Conclusion: Seedance performance or H3 value? The MiniMax H3 vs Seedance 2.0 decision is not a tie between identical products. In our August 11, 2026 tests, Seedance earned its premium with cleaner staging, better identity retention, and more convincing complex motion. H3 offered credible concept footage at one quarter of the per-output cost. Choose Seedance when the generation must survive a demanding creative review. Choose H3 when you need affordable exploration, higher attempt volume, or an open-weight-oriented path. Before production, run your own representative prompt and review both the video and audio—not just a contact sheet. Ready to run that test? Test MiniMax H3 in the PiAPI workspace for the lower-cost path, or test Seedance 2.0 in the PiAPI workspace when premium output quality is the priority. ## How to Create Product Ads With the Seedance 2.5 API Create Seedance 2.5 product ads with prepared references and reusable 9:16 prompts. See three original examples and the real cost per usable clip. With the Seedance 2.5 API , you can create Seedance 2.5 product ads by giving the model a clean product reference, translating brand rules into visible constraints, and generating one simple action per clip. We tested that workflow with a fictional skincare product and made three original 5-second ecommerce video ads in 9:16: a studio reveal, a lifestyle interaction, and a UGC-style hook. All three intended clips were usable. Confirmed fully loaded spend was $15.03, or $5.01 per usable clip. This guide is for developers, e-commerce teams, and performance marketers who need repeatable source footage. It includes the request structure, reusable prompt blocks, observed results, and cost calculation. Key takeaways - Start with one reference that shows the whole product, its materials, proportions, and exact wordmark. - Split the prompt into identity, scene, motion, framing, and exclusions so each instruction has a clear job. - Generate one continuous action per clip. Add captions, voiceover, cuts, and offers during editing. - Review product identity, hand contact, framing, and editability before paying for another attempt. - Measure total spend divided by usable clips. The intended clips cost $3.00 each, while the fully loaded result was $5.01 to $6.01 per usable clip. On this page: Results · Workflow · Reference preparation · API request · Three examples · Cost · FAQ ## Seedance 2.5 Product Ads at a Glance Variant Main control Observed result Production decision Premium reveal Fixed product; moving light and camera Brand details remained stable throughout the shot Use as hero source footage Lifestyle interaction One hand action with a specified grip Plausible lift with minor scale change Use without rerunning UGC-style hook Gentle handheld move toward camera Stable product; lower face entered the top edge Use after a small crop ## How to Create Seedance 2.5 Product Ads Definition: A Seedance 2.5 product ad is a short generated video that uses the Seedance 2.5 task plus product references and shot instructions to create editable advertising footage. Product identity, scene, movement, framing, and exclusions are controlled in the request rather than left implicit. - Define the product identity that must not change. - Create or select one clean product reference. - Write brand constraints as visible, testable details. - Choose one shot, one camera move, and one product action. - Submit the reference and prompt through the Seedance API. - Inspect the original output for product drift, motion defects, framing, and editability. - If the clip is unusable, change one major variable and run it again. - Record every charged attempt, then calculate cost per usable clip. This article used a fictional facial-mist brand called VELORA, avoiding third-party logos and real product claims. All three concepts share the same reference and technical settings. For campaign messaging, platform formats, and final-ad review beyond source-clip generation, use the broader AI product-ad workflow . ## Prepare the Product Reference and Brand Constraints A product reference is a visual specification, not only a mood image. Show the full silhouette, keep the label readable, and remove props that might be mistaken for packaging. Our master image contained exactly one bottle: frosted pale-sage glass, a matte ivory cap, a narrow brass collar, one lower brass ring, and the word VELORA in dark green. The straight-on angle made each feature easy to check in the output. The fictional VELORA master reference. The request specified 9:16; the downloaded file measured 768 × 1344. The image request specified 9:16, but the downloaded PNG measured 768 × 1344, or 4:7. All three videos arrived at 720 × 1280, exact 9:16. That is one observed result, not a promise that every ratio mismatch will resolve this way. Check downloaded files rather than trusting requested settings. ## Product reference checklist - One product, fully visible from top to base - Front-facing logo or wordmark in sharp focus - Enough contrast to separate the product from the background - No extra copy, hands, reflections, or duplicate objects - Empty space matching the intended vertical composition - No distorted, duplicated, or cropped product details - Rights cleared for the product, image, logo, and publication use ## Brand constraint template ``` Use @image1 as the exact product identity reference. Preserve: - [product silhouette and proportions] - [primary material and color] - [cap, closure, hardware, or trim] - [exact front wordmark or label] Do not change: - [geometry] - [color] - [label spelling or placement] - [number of products] ``` Avoid vague instructions such as “keep it on brand.” Name the shape, material, color, hardware, and wordmark; the same list becomes an acceptance check. ## Send a Seedance 2.5 API Request PiAPI uses a task endpoint for Seedance 2.5. The request below shows the structure used for this experiment. Replace the temporary image URL and prompt with your own values. ``` curl --request POST \ --url https://api.piapi.ai/api/v1/task \ --header 'Content-Type: application/json' \ --header 'X-API-Key: YOUR_PIAPI_API_KEY' \ --data '{ "model": "seedance", "task_type": "seedance-2.5", "input": { "prompt": "Use @image1 as the exact product identity reference...", "mode": "omni_reference", "image_urls": ["https://your-public-url.example/product-reference.png"], "duration": 5, "resolution": "720p", "aspect_ratio": "9:16", } }' ``` The first reference URL maps to @image1 . Every intended task used one public image URL, omni_reference , 5 seconds, 720p, and 9:16. Audio behavior should be verified against the live API contract rather than inferred from an unsupported request field. Check the current fields in the PiAPI Seedance 2.5 API guide before shipping. The returned files contained AAC audio streams even though the request did not include an audio control field. We preserved the originals and removed the streams from these publication copies. Check requested settings and observed media properties separately. PiAPI describes itself as a non-official API service and states that it is not affiliated with ByteDance. The request contract and experiment results in this article describe Seedance 2.5 access through PiAPI. ## Build Reusable Product-Ad Prompts A reusable Seedance 2.5 product video prompt can be assembled from five blocks: ``` [IDENTITY] Use @image1 as the exact product identity reference. Preserve [visible product details]. [SHOT] Create one continuous vertical 9:16 [reveal/lifestyle/UGC] shot in [setting]. [ACTION AND CAMERA] [Product or person action]. The camera [specific restrained movement]. [COMPOSITION] Keep [product placement] and leave [safe area] for later ad copy. [EXCLUSIONS] Do not change [identity details]. No [common artifacts, extra objects, generated text, or audio]. ``` Put identity first, then describe one scene in chronological order. A slow push, one hand lift, or one move toward camera is easier to review than several actions compressed into five seconds. For short vertical ads, request source footage. Reserve a safe area, then add price copy, claims, captions, and calls to action in an editor. For other reference roles and shot patterns, review these Seedance 2.5 prompt examples . ## Example 1: Premium Product Reveal The reveal prompt placed the bottle on a warm-gray stone pedestal against a dark sage background. It asked for one warm light sweep and a slow push-in with a small arc. The bottle stayed upright and motionless so the camera and light supplied the movement. ``` Use @image1 as the exact product identity reference. Preserve the VELORA bottle's cylindrical silhouette, frosted pale-sage glass, matte ivory spray cap, narrow brushed-brass collar, lower brass ring, proportions, and exact dark-green VELORA wordmark throughout the entire shot. Create one continuous premium product-reveal shot in a native vertical 9:16 composition. The bottle stands upright and motionless on a low warm-gray stone pedestal against a dark sage studio background. A narrow warm light sweeps once from left to right across the bottle while the camera makes a very slow, smooth push forward with a subtle 10-degree arc from front-left to centered. Add faint atmospheric haze only behind the pedestal. Keep the bottle fully visible and centered with clean empty space above it for a later text overlay. Commercial cosmetic lighting, realistic frosted glass and brushed metal, stable geometry, restrained motion, no cuts. Do not change the bottle, cap, collar, brass ring, colors, proportions, or wordmark. No added label copy, misspelled text, duplicate bottle, hands, people, liquid splash, flowers, fruit, floating particles in front of the label, generated captions, border, watermark, or audio. ``` What the result showed: The clip delivered the pedestal, dark-sage studio, light transition, and smooth push-in. The silhouette, cap, brass details, and wordmark stayed readable, leaving a usable hero clip. ## Example 2: Lifestyle Product Interaction For the lifestyle variant, we changed the setting and action while keeping the product-identity block intact. The prompt assigns the hand one simple task and tells it where to grip the bottle. ``` Use @image1 as the exact product identity reference. Preserve the VELORA bottle's silhouette, materials, brass details, proportions, and exact VELORA wordmark. Create one continuous vertical 9:16 lifestyle product shot in a quiet modern bathroom at sunrise. Begin with the bottle upright on a pale travertine vanity beside a folded ivory towel. After one second, one natural adult hand enters slowly from the right, grips the lower half without covering the wordmark, lifts it approximately eight centimeters, and holds it steady facing the camera. Use a locked close-up camera with only a subtle focus adjustment. Leave clean space in the upper third for later ad copy. Use plausible hand contact and stable bottle geometry. No cuts, face, extra hands, jewelry, spraying, added packaging, generated text, watermark, or audio. ``` What the result showed: The hand enters from the right, lifts the bottle, and keeps the wordmark visible. Contact looks plausible. The bottle changes scale slightly, but its identity and label remain usable, so another paid generation was not justified. ## Example 3: UGC-Style Vertical Hook The UGC prompt trades polished camera movement for gentle handheld motion. It also reserves space for a hook caption that would be added after generation. ``` Use @image1 as the exact product identity reference. Preserve the VELORA bottle's silhouette, materials, brass details, proportions, and exact VELORA wordmark. Create a believable five-second UGC-style vertical ad hook filmed like a handheld smartphone video in soft morning window light. Frame an adult creator from shoulders to waist against a neutral apartment background, with the face outside the top edge. The creator holds the bottle upright near the center, brings it slightly closer during the first two seconds, then holds it steady for the final three seconds. Use gentle natural handheld movement. Keep negative space in the upper quarter for a later hook caption, but do not generate the caption. Use plausible fingers and stable product identity in one continuous shot. No speaking, lip sync, face, extra fingers, warped hands, second product, invented claim, generated text, watermark, or audio. ``` What the result showed: The clip reads as a natural social video, with coherent fingers and a stable bottle. The creator's lower face enters the top edge despite the exclusion. A modest crop can remove it, so we accepted the source instead of buying another attempt. This UGC example also covers the short vertical variant. ## Calculate Cost per Usable Seedance 2.5 Clip Cost per generation hides the price of rejected or misconfigured work. Use this formula instead: ``` Cost per usable clip = total charged generation cost / number of usable clips ``` The Seedance 2.5 API pricing and specifications listed 720p output at $0.60 per second when this experiment ran. Each 5-second video therefore charged $3.00. Two technically valid but incorrect prompt submissions were also billed. We include them in the fully loaded workflow cost because automation mistakes still consume production budget. One earlier reveal task lost its polling connection before its ID was recorded, so its final status and charge remain unknown. Cost item Confirmed cost Product reference image $0.03 Three intended 5-second videos $9.00 Two incorrect-prompt submissions $6.00 Confirmed total $15.03 Possible disconnected task Up to $3.00 All three intended clips were usable as source footage. Confirmed cost per usable clip was therefore $15.03 / 3 = $5.01 . If the disconnected task also completed and billed, the conservative result is $18.03 / 3 = $6.01 per usable clip. The creative-only number was $3.00 per intended clip, but workflow errors make $5.01 to $6.01 the better budget range. “Usable” means suitable for editing or publication, not proven conversion performance. Test record, August 6, 2026: Three usable 5-second 720p clips required $15.03 in confirmed spend. Including the unresolved task creates a conservative ceiling of $18.03, or $6.01 per usable clip. ## Product-Ad Production Checklist ## Before generation - Confirm rights to the product reference and brand assets. - Record the downloaded image dimensions and inspect the label at full size. - Turn brand rules into visible identity and exclusion lists. - Define one shot, one action, one camera move, and one safe area. - Validate the actual payload before paying for a task. ## After generation - Download and preserve the original file. - Check resolution, aspect ratio, duration, frame rate, and audio streams. - Review the first, middle, and last frames for geometry and label drift. - Check hands, contact, cropping, duplicate products, and generated text. - Decide whether a crop or edit is cheaper than another generation. - Record every charge, including rejected and misconfigured tasks. ## Seedance 2.5 Product-Ad FAQ ### How do you create a product ad with the Seedance 2.5 API? Prepare a clean product image, reference it as @image1 , and submit it through omni_reference with a prompt covering identity, scene, motion, composition, and exclusions. Generate one simple shot, inspect the downloaded file, and revise one major variable at a time if the clip is not usable. ### What reference image works best for a vertical product video? Use one front-facing product on a simple background, with the full silhouette and label visible. Match the intended vertical composition, leave safe space for later copy, and remove extra props or reflections. Verify the downloaded image dimensions before submitting it because requested and returned dimensions can differ. ### How do you keep a product label consistent in AI video? Use a sharp, readable reference and repeat the exact wordmark in the identity block. Describe its color and placement, then prohibit misspellings, added copy, occlusion, and redesign. Review the label across the whole clip, not only the first frame, because text and geometry can drift during motion. ### Can Seedance 2.5 make UGC-style ads? Yes. Ask for smartphone-like framing, gentle handheld motion, one plausible product action, and clean space for a later caption. Keep the generated clip simple and add speech, claims, captions, offers, and cuts in post-production. In our test, the source was usable after a minor top crop. ### How much does a usable Seedance 2.5 product clip cost? In this five-second 720p experiment, each intended generation cost $3.00. After two billed setup errors and a $0.03 reference image, three usable clips cost $5.01 each on confirmed spend. The conservative figure was $6.01 if an unresolved task also billed. Check current pricing before production. ### Can Seedance 2.5 outputs be used in paid product ads? Confirm usage against the current PiAPI terms, the rights attached to every reference asset, and the rules of the advertising platform where the clip will run. This experiment uses a fictional product and makes no claim that generation alone clears trademarks, likenesses, music, product claims, or campaign compliance. ## Create Your First Product-Ad Source Clip The repeatable part of this workflow is simple: prepare one inspectable product reference, freeze the identity block, change only the scene and action, and count every paid attempt. That produced three usable Seedance 2.5 product ads with a transparent fully loaded cost. Try the product-reveal prompt in the Seedance 2.5 playground , then carry the tested request fields into your application. Sources checked August 6, 2026: Seedance 2.5 Preview API documentation , Seedance 2.5 API pricing and specifications , and Google Ads asset-generation guidance . The live API documentation is the source of truth for the current request contract. ## How to Use the MiniMax H3 API: Text-to-Video and Image-to-Video Examples Use the MiniMax H3 API through PiAPI with text-to-video and image-to-video examples, native audio, pricing, parameters, polling, and troubleshooting. MiniMax H3 can generate a short video from a prompt or animate a supplied first frame. But a useful MiniMax H3 API example needs more than a request body: you also need to know which parameters PiAPI exposes, how asynchronous polling works, what a successful task costs, and where the model can still ignore instructions. This guide is for developers and creative teams integrating MiniMax H3 through PiAPI . We ran three paid tasks on August 10, 2026, and preserved the prompts, task IDs, timings, charges, input asset, and output metadata. We kept the imperfect result rather than paying for a cleaner replacement and hiding the limitation. Direct answer: MiniMax H3 through PiAPI is an asynchronous video-generation API for text-to-video and first-frame image-to-video. It returns an MP4 with stereo audio and currently supports 512p or 768p output, 5–15 second requests, and 16:9, 9:16, or 1:1 aspect ratios. Evidence note: “Documented” facts below come from the PiAPI MiniMax Generate Video API documentation verified on August 10, 2026. “Observed” results come from our three original PiAPI tasks. This small example set is not a benchmark. ## Key takeaways - The PiAPI model identifier is Qubico/minimax-h3 . - Use txt2video for prompt-only generation and img2video when supplying a first frame. - PiAPI exposes 512p or 768p, 5–15 second requests, and 16:9, 9:16, or 1:1 framing. - All three inspected MP4 files contained AAC stereo audio. - Pricing is $0.03 per second at 512p and $0.05 per second at 768p. - Three successful 5-second tasks cost $0.55, with no retries or failures. - Generation is asynchronous: create a task, save its ID, poll the task endpoint, and then download the completed MP4. ## Table of contents - What is the MiniMax H3 API? - Requirements and supported parameters - How to use MiniMax H3 with PiAPI - Text-to-video example - Image-to-video example - A complex 768p vertical example - Pricing and measured cost - Native audio - Limitations and troubleshooting - When to use MiniMax H3 through PiAPI - FAQ ## What is the MiniMax H3 API? MiniMax H3 is a video-generation model from MiniMax, described in the company’s official H3 overview . Through PiAPI, developers can access a specific supported subset of its video capabilities using PiAPI’s task API. The distinction matters. Upstream MiniMax material may discuss a broader H3 capability set, but the PiAPI endpoint covered here has a narrower documented contract: Capability PiAPI status in this guide Evidence Text-to-video Supported Documented as txt2video ; two completed original tasks First-frame image-to-video Supported Documented as img2video ; one completed original task Native audio Supported Documented by PiAPI; AAC stereo streams observed in all three MP4 files Output resolution 512p and 768p Documented values and confirmed in the downloaded files Requested duration 5–15 seconds Documented range; 5-second requests used in all three examples Aspect ratio 16:9, 9:16, and 1:1 Documented values; 16:9 and 9:16 tested 2K or multi-shot through this endpoint Not documented Not included in the current PiAPI request schema or this evidence set This article does not treat upstream claims such as 2K output, multi-shot generation, video-to-video transfer, or generalized editing as PiAPI features. If those capabilities are not present in the current PiAPI request schema, they should not be promised in a PiAPI implementation. You may also encounter the name “Hailuo H3” in product and search results. PiAPI also provides a separate Hailuo API product path. For this integration, the important implementation detail is the exact PiAPI model identifier Qubico/minimax-h3 . Use the identifier and feature set in the current PiAPI documentation rather than inferring API behavior from product naming. ## MiniMax H3 API requirements and supported parameters You need: - A PiAPI account and API key. - An HTTP client such as cURL, JavaScript fetch , or Python requests . - A public JPG/PNG URL or a base64 data URI for img2video . - A polling loop because video generation does not finish inside the initial POST request. The current PiAPI MiniMax API documentation defines the following request fields: Field Required Supported value or constraint Notes model Yes Qubico/minimax-h3 Use the exact identifier task_type Yes txt2video , img2video img2video requires image input.prompt Yes Up to 2,000 characters Describe visual action and desired sound input.image For image-to-video JPG/PNG URL or base64 data URI, up to 4,096 px Treated as the first frame input.resolution No 512p , 768p PiAPI documents 512p as the default input.duration No 5–15 seconds Our 5-second requests returned 5.167-second files input.aspect_ratio No 16:9 , 9:16 , 1:1 Match the intended publishing channel input.seed No Integer Useful for recording inputs; do not assume perfect determinism config.service_mode No public Current documented service mode ## Text-to-video versus image-to-video Decision txt2video img2video Required input Prompt Prompt plus first-frame image Best fit Creating a scene from a written description Animating a composition or product image you already control Image field Empty or omitted Public JPG/PNG URL or base64 data URI Main review risk Subject, motion, text, and scene interpretation First-frame preservation, unwanted redesign, obstruction, and motion strength Keep the prompt concrete: name the subject and setting, main action, camera motion, visual treatment, desired sound, and exclusions such as speech, music, logos, or readable text. Exclusions are instructions, not guarantees. Our complex example requested “no readable signs,” but the output still contained pseudo-readable menu lettering. ## How to use the MiniMax H3 API with PiAPI The complete flow has six steps: - Open the MiniMax H3 workspace , create a PiAPI API key, and keep it outside source control. - Send a creation request to POST https://api.piapi.ai/api/v1/task . - Save the returned task_id . - Poll GET https://api.piapi.ai/api/v1/task/{task_id} at a reasonable interval. - Stop on a completed, failed, or client-side timeout condition. - Download the completed MP4 and validate its video, audio, and duration. ## Quick cURL request Set your API key in an environment variable instead of pasting it into source control: ``` export PIAPI_API_KEY="your-api-key" ``` On PowerShell: ``` $env:PIAPI_API_KEY = "your-api-key" ``` Then submit a text-to-video task: ``` curl --request POST \ --url https://api.piapi.ai/api/v1/task \ --header "Content-Type: application/json" \ --header "x-api-key: $PIAPI_API_KEY" \ --data '{ "model": "Qubico/minimax-h3", "task_type": "txt2video", "input": { "prompt": "A ceramic coffee cup beside a rain-streaked window, slow camera push-in, warm light, realistic materials, rain ambience, no speech, no music, no readable text.", "resolution": "512p", "duration": 5, "aspect_ratio": "16:9", "seed": 0 }, "config": { "service_mode": "public" } }' ``` The creation response returns a task object with a task_id and an initial status such as pending . It does not immediately return a finished video. ## Complete JavaScript example with polling The code below submits a task, waits between polls, handles completed and failed states, and returns the output URL: ``` const API_BASE = "https://api.piapi.ai/api/v1"; const API_KEY = process.env.PIAPI_API_KEY; if (!API_KEY) { throw new Error("PIAPI_API_KEY is not set"); } const sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); async function piapiRequest(path, options = {}) { const response = await fetch(`${API_BASE}${path}`, { ...options, headers: { "Content-Type": "application/json", "x-api-key": API_KEY, ...options.headers, }, }); const payload = await response.json(); if (!response.ok || payload.code !== 200) { throw new Error( `PiAPI request failed: ${payload.message || response.statusText}` ); } return payload.data; } async function createMiniMaxH3Task(input) { return piapiRequest("/task", { method: "POST", body: JSON.stringify({ model: "Qubico/minimax-h3", task_type: input.image ? "img2video" : "txt2video", input: { prompt: input.prompt, image: input.image || "", resolution: input.resolution || "512p", duration: input.duration || 5, aspect_ratio: input.aspectRatio || "16:9", seed: input.seed ?? 0, }, config: { service_mode: "public", }, }), }); } async function waitForTask( taskId, { pollIntervalMs = 5000, timeoutMs = 15 * 60 * 1000 } = {} ) { const deadline = Date.now() + timeoutMs; while (Date.now() < deadline) { const task = await piapiRequest(`/task/${taskId}`, { method: "GET", }); if (task.status === "completed") { // Our August 10 tests returned output.video_url. // The current documentation sample shows output.video. const videoUrl = task.output?.video_url || task.output?.video; if (!videoUrl) { throw new Error("Task completed without a video URL"); } return { task, videoUrl }; } if (task.status === "failed") { throw new Error( task.error?.message || `Task ${taskId} failed` ); } await sleep(pollIntervalMs); } throw new Error(`Task ${taskId} did not finish before the timeout`); } const createdTask = await createMiniMaxH3Task({ prompt: "A cinematic close-up of a ceramic coffee cup beside a rain-streaked window. Slow camera push-in, warm light, realistic materials, rain ambience, no speech, no music, no readable text.", resolution: "512p", duration: 5, aspectRatio: "16:9", seed: 0, }); console.log("Created task:", createdTask.task_id); const result = await waitForTask(createdTask.task_id); console.log("Video URL:", result.videoUrl); ``` Store the returned MP4 promptly. Temporary output URLs may not be suitable as permanent publication storage. ## MiniMax H3 text-to-video API example For the baseline test, we chose a simple scene with one main subject, restrained camera motion, visible rain, and environmental sound. ## Prompt ``` A cinematic close-up of a handmade ceramic coffee cup on a dark wooden table beside a rain-streaked window. Steam rises naturally from the coffee while soft rain taps the glass and a low distant thunder roll is heard. The camera makes a slow, steady push toward the cup. Warm interior light, realistic materials, shallow depth of field. No speech, no music, no readable text. ``` ## Settings and task evidence Item Value Task ID e4cef166-2111-4629-9672-31e71583fc38 Task type txt2video Resolution 512p Requested duration 5 seconds Aspect ratio 16:9 Seed 0 Charged cost $0.15 Created to completed 129.12 seconds Actual file 896×512, 5.167 seconds, 24 fps Streams H.264 video, AAC stereo audio Example 1 output: MiniMax H3 text-to-video at 512p from the coffee-and-rain prompt. Requested duration: 5 seconds. Charged cost: $0.15. The cup, handle, window, and tabletop remain stable in the representative frames, and the rain-streaked glass stays visible. The restrained change suits the requested push-in, although the movement is subtle. Steam is not clear in the sampled frames, so the article should not present it as a strong success without a closer full-motion review. Verdict: Use this as the clean baseline for the request and polling walkthrough. ## MiniMax H3 image-to-video API example Image-to-video uses the supplied image as the first frame. PiAPI accepts a public JPG/PNG URL or a base64 data URI. For this test, we created a publication-safe 576×1024 JPEG of an unbranded teal perfume bottle. The generation helper uploaded it to a temporary URL before submitting the PiAPI task. ## Image-to-video request ``` const createdTask = await createMiniMaxH3Task({ prompt: "The camera slowly moves in a gentle half-orbit around the teal glass perfume bottle while the sheer fabric in the background drifts subtly in a light breeze. Keep the bottle shape, cap, color, pedestal, and overall composition consistent with the first frame. Add quiet room ambience, a soft fabric rustle, and one delicate glass chime near the end. No speech, no music, no text, no logo.", image: "https://your-public-host.example/perfume-first-frame.jpg", resolution: "512p", duration: 5, aspectRatio: "9:16", seed: 0, }); ``` If you prefer a data URI, construct it without logging the full encoded payload: ``` import { readFile } from "node:fs/promises"; const imageBytes = await readFile("./perfume-first-frame.jpg"); const imageDataUri = `data:image/jpeg;base64,${imageBytes.toString("base64")}`; // Pass imageDataUri as the image value. ``` ## Settings and task evidence Item Value Task ID a8f2d2e2-328b-4568-9c72-349f9bf9e214 Task type img2video Input Locally created 576×1024 JPEG Resolution 512p Requested duration 5 seconds Aspect ratio 9:16 Seed 0 Charged cost $0.15 Created to completed 150.47 seconds Actual file 512×896, 5.167 seconds, 24 fps Streams H.264 video, AAC stereo audio The locally created 576 × 1024 first frame used for the MiniMax H3 image-to-video task. Example 2 output: The 512p image-to-video result preserved the bottle, while the moving fabric became more prominent than requested. Charged cost: $0.15. The bottle shape, teal color, cap, pedestal, and central placement remain highly consistent across the sampled frames. The background fabric moves more than the requested subtle drift and increasingly crosses in front of the product. The requested half-orbit is not obvious in the contact sheet. Verdict: Use this to show first-frame preservation, while calling out the distracting fabric motion. ## A complex 768p vertical example Our third task increased scene complexity rather than pretending to be a controlled 512p-versus-768p comparison. It combined two people, a hand-to-hand object transfer, background activity, steam, reflections, camera movement, signage, and layered sound instructions. ## Prompt ``` A handheld cinematic shot at a busy night-market drink stall. A vendor passes a steaming paper cup across the counter to a customer, both hands remaining anatomically natural and the cup staying consistent during the handoff. Neon reflections shimmer on the wet counter while steam rises and people move softly in the background. The camera tracks sideways at walking speed. Native audio: gentle crowd chatter, a drink machine hiss, cup movement on the counter, and light rain. No music, no readable signs, no logos. ``` ## Settings and task evidence Item Value Task ID 5e2a3a43-25c4-4683-a36a-a395e9a5fe14 Task type txt2video Resolution 768p Requested duration 5 seconds Aspect ratio 9:16 Seed 0 Charged cost $0.25 Created to completed 229.65 seconds Actual file 768×1344, 5.167 seconds, 24 fps Streams H.264 video, AAC stereo audio Example 3 output: A complex 768p vertical handoff. The cup and hands stayed mostly coherent, but generated menu text appeared despite the text-avoidance instruction. Charged cost: $0.25. The cup stays visually consistent through the sampled handoff, and the interaction between the vendor’s gloved hand and the customer’s hand is mostly coherent. Steam, reflections, and the busy stall environment are visible. However, the output contains prominent pseudo-readable menu and stall lettering even though the prompt explicitly prohibited readable signs and logos. Verdict: Keep this as limitation evidence: negative text instructions did not reliably suppress generated signage. This single task also took longer than either 512p task in our three-run set. That is an observation, not proof that resolution caused the difference; prompt complexity, queue conditions, and other service factors were not controlled. ## MiniMax H3 API pricing and cost examples PiAPI documents per-second pricing based on requested resolution. The rates below were verified against the MiniMax Generate Video documentation and the three successful charges on August 10, 2026. Check the PiAPI pricing page for broader account and plan details. Resolution Rate 512p $0.03 per requested second 768p $0.05 per requested second That produces the following estimated successful-task costs: Requested duration 512p 768p 5 seconds $0.15 $0.25 10 seconds $0.30 $0.50 15 seconds $0.45 $0.75 PiAPI states that successful tasks are charged. Preserve every task record anyway so failures, refunds, retries, or moderation outcomes are not silently excluded from later cost analysis. ## What our example set cost Result Charge Coffee, 512p × 5 seconds $0.15 Perfume image-to-video, 512p × 5 seconds $0.15 Night market, 768p × 5 seconds $0.25 Total $0.55 All three attempts completed successfully, and all three were retained as usable and publication-approved evidence. There were no failed or retried tasks. The rounded cost per usable or approved output was $0.18. The resulting 100% usable and approval rates describe only this three-task evidence set, not a general model success rate. ## Does MiniMax H3 generate native audio? Yes. PiAPI documents native stereo audio muxed into the returned MP4 rather than requiring a separate audio-generation request. All three files we downloaded contained: - H.264 video - AAC audio - Two audio channels with a stereo layout - A 32 kHz audio sample rate We also extracted the audio tracks successfully with FFmpeg, confirming that the streams were decodable. However, stream presence is not the same as a listening evaluation. Do not claim that the requested rain, glass chime, crowd, machine hiss, or synchronization was accurate until the clips receive a deliberate listening pass. For production automation, inspect both streams after download: ``` ffprobe -v error \ -show_streams \ -show_format \ -of json \ output.mp4 ``` This catches cases where a download exists but the expected media streams or duration do not. ## Limitations and troubleshooting ## The API is asynchronous The POST request creates work; it does not wait for the final MP4. Save task_id , poll with a delay, and stop on completion, failure, or a client-side timeout. Without a timeout, a network or task-state problem can leave a worker polling indefinitely. ## Handle both documented and observed output fields Our completed task records returned the URL in output.video_url , while the current documentation sample displays output.video . Treat the documentation as the contract, but make the client tolerant during integration: ``` const videoUrl = task.output?.video_url || task.output?.video; ``` Log unexpected response shapes without logging your API key or an entire base64 input. ## Validate image inputs before submission For img2video , use JPG or PNG, stay within the documented 4,096 px limit, and ensure the API service can fetch the URL. Browser access alone is not enough when authentication, expiring signatures, or hotlink protection blocks server-side retrieval. If using a data URI, confirm the MIME type matches the file and avoid passing raw base64 without the data:image/...;base64, prefix. ## Check prompt length before submitting PiAPI documents a 2,000-character prompt limit. Validate the final rendered string before submission, especially when prompts are assembled from templates. ## Returned duration can differ slightly Each of our three 5-second requests produced a file with a measured duration of 5.167 seconds. Budget from requested duration, but validate actual duration when a downstream editor, timeline, or ad platform requires exact timing. ## Negative prompt instructions are not guarantees The night-market example requested no readable signs or logos. Generated pseudo-text still appeared prominently. For brand-sensitive work: - Avoid compositions dominated by signs, menus, labels, or screens. - Use a clean first-frame image when layout control matters. - Reserve space for real typography to be added in post-production. - Review every frame, and add factual claims or prices as real typography in post-production. ## Product-background motion can become too strong In the perfume example, the bottle stayed consistent but the fabric became more prominent than requested and crossed the product. Use restrained motion language, specify that the product must remain unobstructed, and plan for review or post-production rather than assuming “subtle” will be interpreted exactly. ## Seed does not prove determinism We recorded seed 0 for reproducibility of the request record, but we did not run matched repeats. Do not promise identical results from the same seed without controlled evidence. ## Do not infer performance from three tasks The 768p complex task took 229.65 seconds from creation to completion, compared with 129.12 and 150.47 seconds for the two 512p tasks. Resolution may be one factor, but the prompts, scene complexity, and service conditions differed. These timings are transparent examples, not a latency guarantee. ## Store outputs promptly Download successful MP4 files to storage you control. Keep the original request, completed response, task ID, timestamps, charge, and local filename together so you can reproduce captions and cost calculations later. ## Why this guide does not show 2K or multi-shot output The PiAPI endpoint documented and tested here exposes 512p and 768p text-to-video and first-frame image-to-video. Broader MiniMax capabilities should not be represented as available through PiAPI until the PiAPI documentation and request schema support them. ## When should you use MiniMax H3 through PiAPI? Based on the documented endpoint and these three examples, MiniMax H3 through PiAPI is worth testing for: - Short-form video concepts in landscape, vertical, or square formats - Programmatic text-to-video prototyping - Animating a controlled first frame - Product or lifestyle motion studies where the source image anchors composition - Workflows that benefit from video and generated audio in one MP4 - Batch systems that can create, poll, validate, download, and review asynchronous tasks Do not treat it as a one-step production solution when you need guaranteed typography, exact brand-layout preservation, frame-perfect duration, proven seed determinism, or output that can publish without review. Generation should sit inside a pipeline that validates the media and includes human approval. For a current model decision, review our MiniMax H3 vs Seedance 2.0 comparison . For broader examples of how teams can apply MiniMax video generation to ads, storytelling, and e-commerce, see the existing Hailuo video use-case guide . After reviewing the supported parameters, evidence, and expected cost, try MiniMax H3 through PiAPI . ## Frequently asked questions ### How do I use the MiniMax H3 API? Send a POST request to PiAPI’s /api/v1/task endpoint with model Qubico/minimax-h3 , a task type, and the required input fields. Save the returned task ID, poll /api/v1/task/{task_id} , and download the MP4 after the task reaches completed . ### Does the MiniMax H3 API generate audio? Yes. PiAPI documents native stereo audio muxed into the returned MP4. Each of our three inspected files contained a decodable AAC stereo stream. Audio presence does not by itself prove that every requested sound or synchronization cue was followed accurately. ### How much does the MiniMax H3 API cost through PiAPI? PiAPI documents $0.03 per requested second at 512p and $0.05 per requested second at 768p. A successful 5-second task therefore costs $0.15 at 512p or $0.25 at 768p. Our three successful examples cost $0.55 in total. ### Can MiniMax H3 generate video from an image? Yes. Use task_type: "img2video" and pass a JPG or PNG as a public URL or base64 data URI. PiAPI documents a maximum image size of 4,096 px. The image is used as the first frame of the generated video. ### How long can MiniMax H3 videos be through PiAPI? The current PiAPI documentation accepts requested durations from 5 to 15 seconds. Actual media duration may vary slightly: all three of our 5-second requests produced files measured at 5.167 seconds. Inspect the downloaded file when a downstream timeline requires exact timing. ### Does PiAPI support MiniMax H3 at 2K? Not through the request schema documented and tested for this guide. PiAPI currently lists 512p and 768p. Do not treat broader upstream MiniMax resolution claims as PiAPI endpoint support unless the PiAPI documentation and available request values are both updated. ### What is the difference between MiniMax H3 and Hailuo H3? The names may appear together in product pages and search results, but they should not determine your request schema. For PiAPI code, always use the documented model identifier Qubico/minimax-h3 and verify supported features against the current PiAPI endpoint documentation. ### Which aspect ratios does the MiniMax H3 API support? PiAPI currently documents 16:9 for landscape video, 9:16 for vertical video, and 1:1 for square video. Choose the ratio at generation time based on the destination rather than relying on aggressive cropping afterward, which may remove important composition details. ### Is a failed MiniMax H3 task charged? PiAPI states that charges apply when generation completes successfully. Keep failed and retried task records in your own evidence log, and verify the task status and current billing documentation before building automated retries or accurately reporting the total generation cost. ## Conclusion Treat MiniMax H3 through PiAPI as an asynchronous media pipeline: submit Qubico/minimax-h3 , save the task ID, poll with a timeout, download the MP4, and validate both streams. The three tasks provided a stable text-to-video baseline, strong first-frame preservation, and a useful failure where generated signage ignored a text-avoidance instruction. They also matched the documented 512p and 768p rates, with $0.55 in successful charges. Preserve the request, task evidence, cost, validation, and an honest account of what each output missed. Ready to test the workflow? Try MiniMax H3 through PiAPI , or open the API documentation to use the current request schema. ## Seedance 2.5 vs Seedance 2.0: A PiAPI Video Comparison Compare Seedance 2.5 vs Seedance 2.0 using paired PiAPI video tests. See quality, consistency, cost, API differences, and whether 2.5 is worth 3x more. Is Seedance 2.5 worth three times the price of Seedance 2.0 ? We generated six videos through PiAPI—one output from each model across three prompts—to find out. Seedance 2.5 produced the stronger cinematic and character-led videos, while Seedance 2.0 remained competitive and handled the mechanical transformation particularly well. The upgrade makes the most sense when polish and character consistency matter more than generation cost. Quick verdict: On PiAPI, ByteDance's Seedance 2.5 and flagship Seedance 2.0 ( seedance-2 ) are video-generation task types for text- and image-guided workflows. In our six-video test, 2.5 led on cinematic and character work; 2.0 won mechanical motion and costs one-third as much at documented 720p rates. - Choose Seedance 2.5 for final campaign shots, cinematic movement, and character-focused content. - Choose Seedance 2.0 for lower-cost iteration and simpler motion tasks. - Treat the results as directional: this comparison uses one output per model in each scenario. On this page: At a glance · Method · Video tests · Price verdict · FAQ ## Seedance 2.5 vs Seedance 2.0 at a Glance Comparison point Seedance 2.5 API Seedance 2.0 API PiAPI task type seedance-2.5 seedance-2 Documented 720p rate $0.60 per output second $0.20 per output second Observed advantage Cinematic and character motion Mechanical product motion Best fit Final shots where polish matters Cost-conscious iteration The rates above come from the PiAPI Seedance 2.5 documentation and Seedance 2.0 documentation , checked August 4, 2026. Review the current contracts before estimating a production workload. ## How We Tested Seedance 2.5 vs Seedance 2.0 We ran three scenarios on August 4, 2026: one text-to-video prompt, one single-image character animation, and one start-to-end-frame product transformation. Each model received the same prompt and reference assets, with one output generated per scenario. All six delivered files are approximately eight seconds long. Audio was excluded from the review and removed from the publication copies so the comparison stays focused on visual performance. - Test 1 is resolution-matched at 1280×720 for both models. - Test 2 is not resolution-matched: the Seedance 2.0 file is 1920×1080 and the Seedance 2.5 file is 1280×720, so sharpness is not part of that verdict. - Test 3 uses 1920×1080 files from both models, but its start and end references depict noticeably different lamp designs. ## Test 1: Text-to-Video Motion The first prompt asks a red paper airplane to cross a sunlit library, bank around a globe, and land on a blue notebook in one continuous shot. ``` Single continuous 8-second cinematic shot. A bright red paper airplane launches from a wooden desk in a sunlit library, glides between two bookshelves, banks left around a brass globe, then lands open on a blue notebook. The camera follows closely at wing height in one smooth tracking move. Show believable airflow, gentle paper flex, stable red color, and warm morning light. No cuts, no people, no extra aircraft, no text, and no logos. ``` Delivered files: about 8 seconds · 1280×720 · 16:9 · publication audio removed ## Seedance 2.5 Output The Seedance 2.5 video had stronger depth and lighting, while its camera movement gave the flight more momentum. The airplane also stayed easier to follow through the scene. ## Seedance 2.0 Output The Seedance 2.0 output followed the globe-to-notebook journey clearly, but the airplane's shape and movement varied more. Neither model fully showed the airplane opening after landing. Test 1 result: Seedance 2.5 produced the more cinematic and fluid text-to-video result. ## Test 2: Image-to-Video Character Consistency The second prompt starts from one rainy-street portrait. The character must open her umbrella, walk through a puddle, look toward the camera, and smile while the camera tracks backward. The same fictional character and rainy-street reference was supplied to both models. ``` Use @image1 as the exact character and scene reference. In one continuous 8-second shot, the woman opens the transparent umbrella, takes three measured steps forward through the shallow puddle, then glances toward the camera and gives a subtle smile. The camera tracks backward smoothly at chest height. Preserve her face, freckles, hairstyle, mustard-yellow raincoat, dark teal scarf, teal boots, umbrella design, rainy street, and blue-hour lighting. Show natural hand movement, realistic rain, and a small splash at each step. No cuts, no extra people, no wardrobe changes, no text, and no logos. ``` Delivered files: about 8 seconds · Seedance 2.5 at 1280×720 · Seedance 2.0 at 1920×1080 · publication audio removed ## Seedance 2.5 Output Seedance 2.5 kept the character's appearance more stable across the shot, and the final glance and smile felt more natural. The coat, scarf, umbrella, and street remained recognizable from the input. ## Seedance 2.0 Output Seedance 2.0 completed the action and preserved the setting, but the character and movement varied slightly more as the video progressed. The higher export resolution is not treated as a quality win in this pair. Test 2 result: Seedance 2.5 had the stronger character consistency and expression. Sharpness was excluded because the delivered resolutions differ. ## Test 3: Start-and-End-Frame Product Motion The final prompt asks a folded desk lamp to unfold through connected hinge movement, switch on gradually, and finish at the supplied end frame. Start frame End frame ``` Use @image1 as the opening frame and @image2 as the required ending frame. Create one continuous 8-second product transformation. Keep the coral-red base fixed on the pedestal while the lower aluminum arm rises from its hinge, the upper arm unfolds into a tall Z shape, and the round frosted head rotates downward. As the lamp reaches the final position, turn on the warm light gradually until the result matches @image2. Use smooth, physically connected hinge motion with slight mechanical damping. Keep the camera, pedestal, background, product materials, and lighting composition stable. No cuts, no melting or morphing, no extra parts, no text, and no logos. ``` Delivered files: about 8 seconds · 1920×1080 · 16:9 · publication audio removed ## Seedance 2.5 Output Seedance 2.5 reached the upright lamp state and switched on the light, but it moved between positions more abruptly and showed more product and framing drift during the transition. ## Seedance 2.0 Output Seedance 2.0 produced the steadier and more mechanically readable transformation. The unfolding sequence made the hinge movement easier to follow before the lamp reached the target frame. This is not a clean product-identity test. The supplied start and end images already use different bases, arm colors, proportions, and compositions, so some morphing was unavoidable for both models. Test 3 result: Seedance 2.0 delivered the clearer mechanical transition, although better-matched endpoint images would make a future test more conclusive. ## Is Seedance 2.5 Worth 3x More? At the documented 720p rates, an eight-second Seedance 2.5 output costs $4.80, compared with $1.60 for the flagship Seedance 2.0 task. Across three planned outputs per model, that is $14.40 for Seedance 2.5 and $4.80 for Seedance 2.0. For the same documented $4.80 output cost, you could generate one eight-second 720p video with Seedance 2.5 or three with Seedance 2.0. The Seedance 2.0 pricing and API guide covers its broader model family and request options. The premium is easiest to justify when the finished shot matters more than the iteration budget. Our six outputs do not show that Seedance 2.5 is universally three times better; its clearest advantages appeared in cinematic presentation and character consistency. ## Which Seedance Model Should You Choose? Choose Seedance 2.5 when: - The output is a final campaign or showcase shot. - Character identity and subtle facial expression matter. - Cinematic lighting and camera movement are part of the brief. - You can spend more per attempt for a more polished result. Choose Seedance 2.0 when: - You need several variations or expect to iterate heavily. - Budget per generated second matters. - The task uses relatively simple or structured motion. - A good, usable result matters more than extracting the last degree of polish. ## Frequently Asked Questions ### Is Seedance 2.5 better than Seedance 2.0? Seedance 2.5 was better overall in our three PiAPI tests, particularly for cinematic text-to-video and character consistency. Seedance 2.0 remained usable in every scenario and performed better in the product-transformation test. The better choice depends on whether visual polish or generation cost matters more to your workflow. ### How much more does Seedance 2.5 cost on PiAPI? At the documented 720p rates checked August 4, 2026, Seedance 2.5 costs $0.60 per output second and flagship Seedance 2.0 costs $0.20. That makes Seedance 2.5 three times the output price: $4.80 versus $1.60 for an eight-second video. ### Should I upgrade every Seedance workflow to 2.5? Probably not. Seedance 2.5 is stronger for selected final shots and character-led scenes, while Seedance 2.0 remains practical for drafts, variations, and cost-sensitive production. A mixed workflow—iterate with 2.0, then use 2.5 for priority shots—can balance cost and polish. ## Final Verdict In this Seedance 2.5 vs Seedance 2.0 comparison, Seedance 2.5 delivered the strongest overall visual results. Seedance 2.0 remained competitive across all three prompts and won the structured product-motion test. If you produce a small number of high-value videos, Seedance 2.5's extra polish can justify the premium. If you generate at volume or need room to experiment, Seedance 2.0 remains the more economical choice. See more Seedance 2.5 prompt examples , then generate a video in the PiAPI workspace with the model that fits your shot. Sources checked August 4, 2026: Seedance 2.5 Preview API documentation and Seedance 2.0 API documentation . The videos and reference images on this page are the original supplied comparison assets. Results can vary between generations. ## 5 Seedance 2.5 Prompts With Real Video Examples See 5 Seedance 2.5 prompts with real video examples, PiAPI settings, reference inputs, API requests, output costs, and practical lessons for creators. What do Seedance 2.5 prompts produce when you run them through PiAPI? These five original outputs cover text-to-video, image reference, first-and-last-frame control, motion transfer, and product relighting. Each example includes a recreation prompt, the supplied input where available, settings, the real output, and the most useful takeaway. Developers, marketers, and AI video creators can use these examples as practical starting points. The recreation prompts describe the supplied inputs and observed outputs; they are not task-log transcripts or a controlled model benchmark, and another run may produce a different result. Key takeaways - Five generated videos covered three supported workflows at 720p. - Clear subject, motion, camera, lighting, and reference instructions produced the most readable results. - Reference media helped control transformations and camera rhythm. - Actual output shape followed the supplied reference in cases where it differed from the planned aspect ratio. - The five requests specified 26 output seconds, with a documented output-only cost of $15.60 at $0.60 per second. On this page: Results · Method · Five prompts · Prompt lessons · API example · FAQ ## What Makes a Good Seedance 2.5 Prompt? Definition: A Seedance 2.5 prompt is a compact shot brief for PiAPI's video task. It names the subject, motion, camera behavior, visual treatment, and role of each reference. For a 4–15-second output, it should also state what must remain stable and what must not appear. ## Which Seedance 2.5 Prompt Pattern Should You Use? Goal Input pattern PiAPI mode Give the reference this role Create an original scene Text only text_to_video No reference; describe the complete shot Animate a product or subject One or more images omni_reference Preserve identity, shape, materials, or style Control the start and destination First and last images first_last_frames Define the two endpoint states Reuse motion or camera pacing Video omni_reference Guide movement without copying the original subject Change a visual treatment Image or video omni_reference Preserve the subject while changing lighting or setting ## Seedance 2.5 Video Examples at a Glance All five outputs were generated at 720p. The live Seedance 2.5 API documentation lists 480p and 720p output, integer durations from 4 to 15 seconds, and support for image, video, and audio references. Example Input pattern Actual output Documented cost basis Main result Rainy greenhouse tram Text only 5.08s, 1280×720 $3.00 Cinematic lateral tracking with strong warm/cool contrast Watermelon product reveal Image reference 5.08s, 1280×720 $3.00 Stable product shot with mist and a moving light sweep Desk lamp unfolding First and last frames 6.08s, 1280×720 $3.60 Smooth mechanical transformation with a stable camera Delivery robot tracking Example 1 as motion reference 5.08s, 1280×720 $3.00 output + about $1.52 video input Camera rhythm transferred into a new subject and setting Desk lamp relighting Image reference 5.08s, 1280×720 $3.00 output Clean cool-lit product treatment with stable motion The output-only total is $15.60. Using the documentation's half-rate formula for Example 4's 5.08-second video reference adds about $1.52, for an estimated set total of about $17.12 before account-specific billing adjustments. Observed in this PiAPI example set: Five requests specified 26 output seconds, while the returned 720p MP4 files total about 26.42 seconds. The set covers text-only, image-reference, first/last-frame, and video-reference workflows and was reviewed on August 3, 2026; it is not a model benchmark. ## How We Generated These Seedance 2.5 Examples On PiAPI, Seedance 2.5 is accessed with model seedance and task type seedance-2.5 . This is an output-led prompt guide rather than a task-log audit: the recreation prompts were written from the supplied inputs and observed outputs. The returned files are H.264 MP4 videos at 1280×720 and 24 frames per second. The prompt plan set generated audio to off, but every returned file contains an AAC audio stream. The publication copies remove those audio streams while preserving the original downloads for task-record verification. For broader model context, read the Seedance 2.5 model and playground guide . For endpoint, pricing, and limit details, use the Seedance 2.5 API specifications , with the live API documentation taking precedence if the two pages differ. ## Example 1: Cinematic Text-to-Video This baseline uses no reference media. The prompt separates the subject, camera direction, environmental motion, lighting, and exclusions. Recreation prompt ``` At blue hour, a small yellow maintenance tram glides slowly through a rain-soaked glass greenhouse filled with tropical plants. The camera tracks parallel to the tram at waist height in one continuous shot. Water beads on the glass, the wheels rotate naturally, and nearby leaves shift gently from the tram's airflow. Warm amber light glows inside the tram against the cool blue greenhouse. Realistic cinematic lighting, stable geometry, natural motion, no cuts, no text, no people. ``` Settings: Text to video · 5 seconds · 720p · 16:9 · audio off What the result showed: The output captured the rainy greenhouse, parallel tracking composition, and warm tram light against a cool blue environment. The tram stayed stable, although foreground foliage hid most of the wheel movement requested in the prompt. ## Example 2: Product Image-to-Video Prompt This example uses an image reference to turn a static watermelon into a commercial product reveal. The prompt gives the reference one clear job: preserve the product while the model changes the presentation around it. Recreation prompt ``` Use @image1 as the exact product reference. Preserve the product's silhouette, materials, colors, label placement, and proportions. Create a premium vertical product reveal: the product stands on a dark stone pedestal while a narrow warm light sweeps slowly across its surface. The camera makes a smooth 20-degree arc from the front-left to a centered close-up. Add subtle drifting mist behind the pedestal, never in front of the product. One continuous shot, clean commercial lighting, stable edges, readable packaging where visible in @image1, no added text, no extra products, no hands. ``` Settings: Omni reference · 5 seconds · 720p · planned 9:16 · actual 16:9 · audio off What the result showed: The watermelon remained clear and stable while the light sweep and mist created a polished product-shot treatment. The supplied reference is horizontal, and the output was also horizontal even though the recreation plan requested 9:16. That is consistent with the documented reference-aspect-ratio behavior. ## Example 3: First-and-Last-Frame Transformation First and last frames are useful when a scene needs a defined starting state and destination. Here, the same desk lamp begins folded and ends fully open. Recreation prompt ``` Begin exactly from @image1 and end exactly on @image2. In one continuous locked-camera shot, the folded desk lamp unfolds through its real hinges: the base stays fixed, the stem rises, the arm extends, and the shade rotates into its final position. The lamp switches on only during the final second. Preserve the lamp's color, materials, proportions, desk surface, framing, and background throughout. Smooth physically plausible mechanical motion, no cuts, no camera movement, no new parts, no disappearing parts, no text. ``` Settings: First and last frames · 6 seconds · 720p · recreated endpoint crops · audio removed for publication What the result showed: The lamp unfolded smoothly through a coherent mechanical sequence while the base, tabletop, and camera remained stable. The light also became visibly brighter near the end, making this the clearest transformation example in the set. ## Example 4: Motion Transfer From a Video Reference Example 4 reuses the unedited greenhouse-tram output from Example 1. The prompt asks Seedance 2.5 to retain its camera direction, subject speed, and pacing while replacing the scene and subject. Reference video: t2v eg1.mp4 Recreation prompt ``` Use @video1 only as the reference for camera movement, subject speed, and pacing. Replace the yellow tram and greenhouse with a compact white autonomous delivery robot rolling left to right through a clean covered night-market corridor after rain. Do not reproduce the tram, glass greenhouse, tropical plants, or yellow-and-blue color palette from @video1. Track parallel to the robot at the same approximate height and distance as the reference. Keep all six wheels rotating naturally and the robot's white body geometry stable. Reflections move across the wet floor as the camera travels. One continuous shot, realistic night lighting, no cuts, no text, no people. ``` Settings: Omni reference · 5 seconds · 720p · 16:9 · audio off What the result showed: The new clip retained a similar lateral tracking rhythm while replacing the tram and greenhouse with a white robot and wet market corridor. The scene did not copy the original setting, and the moving floor reflections reinforced the camera movement. ## Example 5: Reference-Guided Desk Lamp Relighting The final example uses the supplied desk-lamp product sheet as an identity reference, then changes the setting and lighting while preserving the product design. Recreation prompt ``` Use @image1 as the exact lamp product reference. Preserve the lamp's identity, silhouette, materials, ivory-and-sage colors, proportions, circular base, and hinge details. Present the open lamp on a gray stone pedestal against softly illuminated pale-blue translucent panels with cool diffused daylight. Keep the product stable while the camera moves smoothly toward a centered commercial composition. No mist, extra products, hands, added text, cuts, or audio. ``` Settings: Reference-guided generation · 5 seconds · 720p · actual 16:9 · audio off What the result showed: The result is a clean lamp product shot with cool lighting, pale-blue panels, and stable motion. The ivory body, sage accents, circular base, and articulated shape remain recognizable from the supplied product reference. ## What the Five Seedance 2.5 Prompts Taught Us The examples point to five practical prompt-writing habits: - Describe one shot, not a sequence of edits. All five prompts use continuous motion that fits within five or six seconds. - Separate subject motion from camera motion. “The tram glides” and “the camera tracks parallel” give the model two distinct instructions. - Give each reference one role. Example 2 uses an image for product identity, while Example 4 uses a video for movement and pacing. - Name what must stay fixed. The lamp example explicitly holds the base, camera, tabletop, and background stable during the transformation. - Check effective output shape. A reference image can override the request aspect ratio, so prepare the source asset in the format you intend to publish. Limitations observed: Foliage obscured a requested mechanical detail in Example 1, Example 2 returned a horizontal composition instead of the planned vertical ad, and Example 4 showed four visible wheels instead of the requested six. Inspect every delivered file before adapting a prompt for production. ## How to Recreate a Seedance 2.5 API Example Send an authenticated task to POST https://api.piapi.ai/api/v1/task with model seedance and task type seedance-2.5 . Keep your API key on the server and use publicly accessible URLs for any supplied references. ``` curl --request POST "https://api.piapi.ai/api/v1/task" \ --header "X-API-Key: $PIAPI_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "seedance", "task_type": "seedance-2.5", "input": { "prompt": "A small yellow maintenance tram glides through a rain-soaked glass greenhouse at blue hour. The camera tracks parallel in one continuous shot. Warm interior light contrasts with the cool greenhouse. No cuts, text, or people.", "mode": "text_to_video", "duration": 5, "resolution": "720p", "aspect_ratio": "16:9", "audio": false } }' ``` For reference-guided prompts, add image_urls , video_urls , or audio_urls and mention each asset in the prompt as @image1 , @video1 , or @audio1 . The API currently supports up to 9 images, 3 videos, and 3 audio files. See the Seedance 2.5 request contract for validation rules and response details. ## Seedance 2.5 Prompt FAQ ## Can I copy these Seedance 2.5 prompt examples? Yes. Copy the structure, then replace the subject, action, environment, and reference roles with details from your own project. If a prompt uses @image1 or @video1 , supply the corresponding media URL in the request. Results can vary between generations, so review the returned video before using it in production. ## Can Seedance 2.5 use image, video, and audio references? Yes. PiAPI's Seedance 2.5 task supports image, video, and audio references through image_urls , video_urls , and audio_urls . The current limits are 9 images, 3 videos, and 3 audio files. An audio reference requires at least one image or video reference in the same request. ## How long can a Seedance 2.5 video be on PiAPI? The current PiAPI documentation accepts integer durations from 4 to 15 seconds, with 5 seconds as the default. Because product pages and older articles can retain earlier limits, use the live API documentation as the source of truth when building or updating an integration. ## Does Seedance 2.5 support 1080p? Yes. PiAPI supports 480p , 720p , and 1080p for the seedance-2.5 task type, with 720p as the default. 1080p was added after this article first published, so plan your generation and publishing workflow around whichever of the three fits your budget: $0.15, $0.35, and $0.80 per second respectively. ## How much did these five Seedance 2.5 examples cost? The five examples contain 26 requested output seconds at 720p. At the documented PiAPI pricing rate of $0.60 per output second, the output-only total is $15.60. Example 4 also uses a 5.08-second video reference; applying the documented half-rate input-video formula adds about $1.52, for an estimated total of about $17.12. ## Try a Seedance 2.5 Prompt on PiAPI These Seedance 2.5 prompts show how text, images, endpoint frames, and motion references can guide different kinds of short video. Start with one clear subject and one achievable shot, then add only the reference and preservation instructions your workflow needs. For a model-specific e-commerce process with reference preparation, three original 9:16 examples, and fully loaded cost, follow the Seedance 2.5 product-ad workflow . Try one of these prompts in the Seedance 2.5 playground , or review the Seedance 2.5 API documentation before integrating the task into your application. - Five audio-free publication MP4s and poster images are implemented. - The supplied watermelon and desk-lamp inputs are implemented as WebP assets. - Example 3 uses transparent recreation crops prepared from the supplied folded/open lamp product sheet. - The article-specific 2000×1000 WebP cover is implemented. - The prompts are labeled as recreation prompts rather than task-log transcripts. *Sources checked August 3, 2026: Seedance 2.5 Preview API documentation , Seedance 2.5 API specifications , and Seedance 2.5 model and playground guide . The live API documentation is the source of truth for the current request contract.* ## Seedance 2.5 API Specifications on PiAPI Explore Seedance 2.5 API specifications on PiAPI: model ID, three modes, 1-30 second videos, 480p/720p pricing, reference limits, and access. The Seedance 2.5 API is live on PiAPI through POST /api/v1/task using model seedance and task type seedance-2.5 . It supports text-to-video, first/last-frame, and omni-reference modes, 1–30-second videos, 480p, 720p, or 1080p output, multimodal references, and optional audio generation. Use the table below for the confirmed PiAPI contract, or test the same request fields in the live Seedance 2.5 playground . Quick specifications - Endpoint: POST /api/v1/task with task type seedance-2.5 - Modes: text_to_video , first_last_frames , and omni_reference - Duration: any integer from 1 to 30 seconds - Output: 480p or 720p, with 720p as the default - Pricing: $0.30/second at 480p or $0.60/second at 720p On this page: Specifications · Modes · API example · Pricing and limits · Access · FAQ ## Seedance 2.5 API Specifications at a Glance PiAPI exposes one Seedance 2.5 task type. The table summarizes its confirmed request fields, output controls, and reference limits as verified on July 31, 2026. Specification Seedance 2.5 on PiAPI Endpoint POST https://api.piapi.ai/api/v1/task Authentication X-API-Key header Model seedance Task type seedance-2.5 Prompt Up to 4,000 characters Modes text_to_video, first_last_frames, omni_reference Duration Any integer from 1 to 30 seconds Resolution 480p or 720p; default 720p Aspect ratios 21:9, 16:9, 4:3, 1:1, 3:4, 9:16 Audio generation Boolean audio switch Image references Up to 9 Video references Up to 3 Audio references Up to 3 Total references Up to 12 across all reference types ## Supported Seedance 2.5 Generation Modes ## text_to_video Generate a video from a prompt without supplying reference media. Set the duration, resolution, aspect ratio, and audio preference directly in the request. ## first_last_frames Use image URLs to control the first and/or last frame of the generated video. In the PiAPI playground, the supplied frames determine the output shape, so it does not send a separate aspect ratio for this mode. ## omni_reference Combine image, video, and audio URLs to guide the subject, motion, scene, or sound. A request can contain up to 12 references in total, subject to the individual caps of 9 images, 3 videos, and 3 audio files. ## Seedance 2.5 API Request Example This cURL request creates a 10-second, 720p text-to-video task with audio enabled. Keep the API key on your server and replace the example prompt with the scene your application needs. ``` curl --request POST "https://api.piapi.ai/api/v1/task" \ + --header "X-API-Key: $PIAPI_API_KEY" \ + --header "Content-Type: application/json" \ + --data '{ "model": "seedance", "task_type": "seedance-2.5", "input": { "prompt": "A cinematic tracking shot through a futuristic city at night", "mode": "text_to_video", "duration": 10, "resolution": "720p", "aspect_ratio": "16:9", "audio": true } }' ``` ## Seedance 2.5 Pricing and Current Limits Seedance 2.5 API pricing on PiAPI is $0.30 per output second at 480p and $0.60 per output second at 720p. A 10-second output therefore costs $3.00 at 480p or $6.00 at 720p. If a request includes input video, its duration is billed at half the selected output rate; this additional charge applies only when video references are supplied. See PiAPI pricing for broader platform information. The current PiAPI contract does not expose 1080p or 4K output. It also has no fast, mini, or less-restriction variants. Because there is no less-restriction task type, private asset:// references are not supported by Seedance 2.5. ## How to Access Seedance 2.5 on PiAPI - Open the Seedance 2.5 page . - Sign in to PiAPI. - Run a prompt in the live playground. For a backend integration, send an authenticated task request with model: "seedance" and task_type: "seedance-2.5" using the request shape above. ## Seedance 2.5 API FAQ ## How do I access the Seedance 2.5 API on PiAPI? Use either PiAPI's live Seedance 2.5 playground or the task API. For API access, send POST /api/v1/task from your backend with an X-API-Key header, model seedance , and task type seedance-2.5 . ## Can Seedance 2.5 generate a 30-second video? Yes. The PiAPI implementation accepts any integer duration from 1 to 30 seconds, with 30 seconds as the maximum. You are not limited to preset values such as 5, 10, or 15 seconds. The final price depends on the selected resolution and any input-video duration. ## Does the Seedance 2.5 API support 4K video? Yes. Seedance 2.5 on PiAPI supports 480p , 720p , and 1080p output, with 720p as the default. 1080p was added after this article first published; 4K is still rejected. Per-second pricing is $0.15 at 480p, $0.35 at 720p, and $0.80 at 1080p. ## Try Seedance 2.5 on PiAPI On PiAPI, the Seedance 2.5 API supports three generation modes, 1–30-second output, multimodal references, audio control, and per-second pricing. Try Seedance 2.5 on PiAPI , then reuse the same request contract in your application. Source and verification: PiAPI's live Seedance 2.5 product page and playground contract , verified July 31, 2026. ## How to Make AI Ad Videos: 3 Product-and-Actor Examples Learn how to make AI ad videos from product and actor images. Review three real outputs, scripts, formats, limitations, and a practical publishing checklist. You can make an AI ad video by preparing a clear product image, writing a short script, adding an optional actor or logo, and choosing a format for the channel where the ad will run. In this guide, we use the same workflow to create three product ads: a mini blender in 9:16, a foldable desk lamp in 1:1, and a compact garment steamer in 16:9. Definition: An AI ad video is an advertising draft generated from inputs such as a product image, script, optional actor reference or logo, and aspect ratio. Instead of only editing footage that already exists, generative AI builds new scenes and actions from the supplied assets and instructions. Key takeaways • A clear product image and focused script are the most important inputs. • An actor image is optional, but it helps when a person needs to present or use the product. • A 9:16, 1:1, or 16:9 layout changes the available space for the product, actor, and captions. • Every generated ad still needs a human review for product accuracy, hand interactions, captions, claims, branding, and CTA placement. ## Table of Contents - What you need to make an AI ad video - A simple product-image-to-ad workflow - Three AI product-ad examples with Korean scripts - How to choose an aspect ratio - What to review before publishing - AI ad video FAQ ## What You Need to Make an AI Ad Video Start with the product and the message, not the camera equipment. A polished video can still be ineffective when the product identity or customer benefit is unclear. These five inputs establish a practical first draft. Input Preparation standard Product image Show the full shape, color, controls, packaging, and other details that must remain consistent. Ad script Focus on one customer problem, one product benefit, and one CTA. Actor image (optional) Choose a clear face and natural hands, with enough space in the composition for the product. Logo (optional) Use a clean transparent-background file and limit it to a controlled position such as the final CTA frame. Aspect ratio and length Choose the publishing destination first, then start with 9:16, 1:1, or 16:9. Avoid forcing long text or extra logos into the product image. Small lettering can blur or change during generation. When using an actor reference, an image where the person is not already holding another product is easier to combine with the new item. A short script can follow a Hook–Benefit–CTA structure: ``` Hook: State the customer problem or desired outcome. Benefit: Explain one specific way the product helps. CTA: Suggest one clear next action. ``` “Adjust the lamp angle to reach the part of your desk that still looks dark” gives the model a clearer visual action than “Make life more convenient.” Do not add functions, performance figures, or benefits that the real product cannot support. ## A Simple Product-Image-to-Ad Workflow We used the following four steps for all three examples. ## 1. Prepare the product image and optional actor image Choose a product image that shows its silhouette and important physical details. If the ad includes a presenter, select a person who fits the likely customer or use context. The two images do not need identical lighting, but avoid combinations with a major visual conflict, such as a bright kitchen product paired with a dark nighttime portrait. ## 2. Write a short script A 10–15 second ad does not have time to explain every feature. Use the opening line to state the problem, the middle to show one use case or benefit, and the final line to suggest an action. Keep sentences short enough to read as captions. ## 3. Choose a format and duration for the channel Start with 9:16 for vertical feeds, 1:1 for square feeds, and 16:9 for horizontal video or website placements. Do not rely on mechanically cropping one master video. Recheck the product position, actor framing, and caption-safe area in each format. ## 4. Review the product, person, copy, and CTA Watch once for the overall advertising flow. Watch again for the product shape and color, then inspect the face, hands, use method, captions, and CTA. A believable-looking shot still needs to be replaced if it demonstrates the product incorrectly or unsafely. For more model-specific scene and prompt guidance, see the Seedance 2.0 AI marketing video guide . ## Three AI Product-Ad Examples With Korean Scripts Each result below used one product image, one actor image, and one Korean ad script. We evaluated the completed videos for prompt adherence, visual quality, detail consistency, artifacts, and practical usability—not for campaign performance. The inputs and full outputs were reviewed side by side on July 30, 2026. Example Format Observed strength Improvement before publishing Practical verdict Mini blender 9:16 · 15.04 seconds Product colors, transparent container, actor, and Korean captions remain relatively stable. Trim toward the planned duration and recheck hand-to-container contact. Strong Foldable desk lamp 1:1 · 10.04 seconds Product color and the use sequence are easy to understand. Improve joint proportions and caption size for mobile. Strong Compact garment steamer 16:9 · 15.04 seconds Actor, bedroom environment, and product colors remain consistent. Replace the bedding sequence, iron-like handling, and missing captions. Moderate ## Example 1: Mini Blender TikTok and Reels Ad Format: 9:16 vertical, 720 × 1280, actual duration 15.04 seconds Original Korean script 아침마다 큰 블렌더를 꺼내기 번거로우셨나요? 이 컴팩트 블렌더로 과일과 음료를 간편하게 준비해 보세요. 바쁜 아침을 더 가볍게. 오늘부터 시작해 보세요. English meaning: Is taking out a large blender every morning inconvenient? Prepare fruit and drinks more easily with this compact blender. Make busy mornings lighter—start today. The video follows a clear sequence from product-only shot to actor introduction, ingredient preparation, finished drink, and CTA. The ivory-and-sage colors, transparent container, and actor appearance remain relatively stable, while most Korean captions are readable. The planned duration was 10 seconds, but the result is 15.04 seconds. Hand and product contact should also be checked when ingredients enter the container and when the actor lifts it to drink. With a simple duration edit, this is the strongest representative example. Evaluation: Prompt adherence moderate · visual quality high · detail high · artifacts low · practical usability strong ## Example 2: Foldable Desk Lamp Instagram Feed Ad Format: 1:1 square, 720 × 720, actual duration 10.04 seconds Original Korean script 책상 위 조명이 원하는 곳까지 닿지 않나요? 접이식 램프의 각도와 밝기를 작업에 맞게 조절해 보세요. 집중할 때는 선명하게, 쉬는 시간에는 부드럽게. 오늘의 책상을 더 편하게 만들어 보세요. English meaning: Does your desk light fail to reach where you need it? Adjust the foldable lamp’s angle and brightness for the task. Keep it clear while focusing and softer while resting. Make today’s desk more comfortable. The result moves from a product-only shot to the actor adjusting the lamp, pressing the control, and working at the brighter desk. The ivory-and-sage finish and circular base remain recognizable, creating an easy-to-understand square feed ad. Some shots alter the length and proportions of the folding joints. The captions also sit low and appear small on a mobile screen. Increasing their size and keeping them inside a safer area would make the result more publishable. Evaluation: Prompt adherence high · visual quality high · detail moderate · artifacts low · practical usability strong ## Example 3: Compact Garment Steamer YouTube Ad Format: 16:9 horizontal, 1280 × 720, actual duration 15.04 seconds Original Korean script 외출 전 옷의 주름 때문에 준비가 번거로우신가요? 컴팩트한 스티머로 필요한 부분을 차분하게 정돈해 보세요. 큰 다리미판 없이 준비 공간은 더 간단하게. 오늘의 옷차림을 깔끔하게 시작하세요. English meaning: Do clothing wrinkles make getting ready inconvenient? Neatly smooth the areas that need attention with a compact steamer. Keep the preparation space simpler without a large ironing board. Start the day with a cleaner outfit. The actor, bright bedroom, and beige jacket connect naturally to the reference image, and the white-and-coral product colors remain mostly consistent. The ending, where the actor finishes preparing to leave, also follows the script direction. However, the opening spends too long ironing bedding instead of the jacket, and the device behaves more like a conventional iron than an upright steamer. Steam is difficult to see and the Korean captions are missing. This result is useful as evidence for why human review matters, but it should be edited or regenerated before use as an ad. Evaluation: Prompt adherence moderate · visual quality high · detail moderate · artifacts moderate · practical usability moderate ## Try Making an AI Ad Video AI Video Ad Generator ### Turn your product image into an ad-video draft Upload a product image, add an optional actor or logo, enter your script, and choose an aspect ratio to create a first advertising draft. Make an AI ad video ## How to Choose an Aspect Ratio Choose the aspect ratio based on where the video will appear. TikTok recommends vertical creative, Meta provides 9:16 guidance for Reels with key messages inside safe areas, and Google Ads supports horizontal 16:9, vertical 9:16, and square 1:1 video assets for relevant YouTube placements. Placement Useful starting ratio Example in this guide Review priority TikTok / Reels / Shorts 9:16 Mini blender Early product visibility and caption-safe area Instagram / Facebook feed 1:1 Foldable desk lamp Centered product placement and mobile caption size YouTube / website 16:9 Compact garment steamer Problem–solution order and product visibility across the wide frame A supported ratio does not guarantee a good composition. Cropping 16:9 footage into 9:16 can remove the actor’s hands or the product, while reusing vertical captions in a wide frame can leave awkward space. Recheck the current specifications and advertising policies of the publishing platform. Official references checked July 30, 2026: TikTok creative tips , Meta Reels ads guide , and Google Ads video specifications . ## What to Review Before Publishing An AI generator can create the draft, but a person must decide whether it is publishable. Review once without sound and once with sound. - [ ] Does the product shape, color, control layout, and packaging match the real item? - [ ] Is the product shown correctly and safely? - [ ] Do the actor’s face, fingers, gaze, and product interactions look natural? - [ ] Are spelling, line breaks, caption size, and safe-area placement correct? - [ ] Can every performance statement or benefit be supported by real product information? - [ ] Does the logo retain its color, proportions, and wording? - [ ] Does the CTA follow naturally from the ad without covering the product? - [ ] Does the selected duration and aspect ratio match the actual placement? Not every problem requires discarding the full video. Caption placement and duration can often be fixed in editing, while a wrong product shape or unsafe demonstration is usually better regenerated. Separating editable problems from regeneration problems reduces unnecessary repetition. In environments where CTAs and interface controls appear in different positions by device or format, keep the product, logo, and captions away from the frame edges. Use the platform preview and guidance such as Google Ads’ video ad safe-zone documentation . ## AI Ad Video FAQ ## Do I need both a product image and an actor image? No. The product image is the main reference, but the actor is optional. Product-only showcases, packaging ads, and simple demonstrations can start without an actor. Add a person when the ad needs a presenter or a lifestyle use scene. ## How long should an ad script be? For a 10–15 second product ad, use roughly one sentence for the problem, one for the main benefit, and one for the CTA. Read the script aloud and check whether it fits the intended duration without rushing. ## Which aspect ratios should I use for TikTok, Instagram, and YouTube? Start with 9:16 for TikTok, Reels, and Shorts; 1:1 for square feed ads; and 16:9 for standard YouTube or website placements. Always confirm the latest specification and caption-safe area for the campaign type. ## Do AI-generated product ads need human review? Yes. AI can change product colors or parts, create awkward hand interactions, or demonstrate a use that was never requested. Captions and product claims are not automatically accurate. A product owner should review the facts, visuals, branding, CTA, and publishing format. ## Can I use the same workflow for my product? Yes. Start with one clear product image and a short Hook–Benefit–CTA script. Add an actor or logo only when needed, choose the ratio for the placement, and review the generated result. Change the input and regenerate when the product form or use sequence is materially wrong. ## Make Your First AI Product Ad The workflow comes down to four steps: prepare the product image, write the script, select the aspect ratio, and review the generated result. The three examples used the same process but produced different levels of readiness. The blender and desk-lamp ads could be used after small edits, while the garment-steamer output revealed a use sequence that should be regenerated. These examples are not fixed templates. They show how to decide what must remain accurate in your own product and what needs to be inspected after generation. When you are ready, create an ad from your product image with the PiAPI AI Advertisement Video Generator . ## How to Create a Professional Resume Photo With AI Learn when to use a resume photo, how to choose the right outfit, background, crop, and file format, and how to review an AI-generated result. Before preparing a resume photo, check whether employers in your target market expect one. In the United States and United Kingdom, applicants are generally advised to leave photos off a standard resume or CV unless the employer or role specifically requests one. In other markets and application formats, a professional photo may be common or built into the form. The job listing and application instructions should always take priority. If a photo is appropriate, the goal is simple: use a clear, current image that still looks naturally like you. An AI resume photo can change the clothing or background of an ordinary portrait while keeping the applicant recognizable. The final result still needs a human review for identity, professional context, and file requirements. Quick answer: A good resume photo follows the destination's instructions, shows the real applicant clearly, and uses clothing, lighting, and a background that fit the role without distracting from the application. With AI, compare the result with the source before using it. ## Key Takeaways - Confirm that a photo is requested or appropriate in the target market. - Follow the employer or platform requirements for crop, dimensions, format, and file size. - Start with a clear, near-front-facing source photo in even light. - Choose clothing and a background that fit the role rather than the most formal preset. - Compare facial features, hair, clothing joins, and background edges with the source. - Use a separate, compliant photo for passports and other official identity documents. ## Table of Contents - Should You Put a Photo on Your Resume? - What Makes a Good Resume Photo? - Outfit, Background, Expression, and Framing - How to Choose a Source Photo for AI - How to Create a Resume Photo With AI - Professional Headshot Styles by Role - AI Output Quality Checklist - AI Generation vs Editing vs a Photo Studio - Before Using Your AI Resume Photo - Resume Photo FAQ ## Should You Put a Photo on Your Resume? There is no universal rule. Start with the country, employer, industry, and application form. The U.S. Equal Employment Opportunity Commission advises employers not to ask for an applicant photograph before a job offer. UK CV guidance from Birmingham City Council similarly says not to include a photo when applying for a job in the UK. By contrast, the official Europass CV builder supports adding a professional photograph, reflecting a different application context. Use this practical order: Priority What to Check Decision 1 Job listing and employer instructions Follow any explicit request or restriction 2 Application form Use the photo field only if it is part of the process 3 Local and industry convention Check reliable guidance for the market and role 4 Personal preference Add a photo only when it supports, rather than distracts from, the application If the employer does not ask for a photo and the local convention is to omit one, focus on the resume content. You can still use a professional headshot on LinkedIn, a portfolio, a speaker profile, or a company team page. Our LinkedIn AI headshot guide covers that use case separately. ## What Makes a Good Resume Photo? A good resume photo is current, recognizable, and technically suitable for the application. The face should be easy to see, the expression should feel natural, and the framing should leave enough space for the platform's crop. Professional does not always mean a dark suit and a serious expression. A clean shirt, blazer, smart-casual outfit, or role-specific uniform can all work when they reflect how someone would realistically appear in that field. The test is whether the image feels credible for the role and whether the same person would be immediately recognizable in an interview. If the application specifies an aspect ratio, pixel size, file format, or maximum file size, follow that instruction. Avoid treating a size found in a general guide as a worldwide standard. Always preview the uploaded image because application systems may crop the head or shoulders automatically. ## Outfit, Background, Expression, and Framing The strongest resume photos are visually simple. The face remains the main point of attention, while the other choices quietly support the applicant's professional context. Element A Reliable Starting Point What to Avoid Outfit Clean, role-appropriate clothing in a solid or restrained color Large logos, distracting patterns, or clothing that implies a qualification you do not hold Background White, light gray, muted solid color, or a softly blurred professional setting Busy rooms, strong color casts, or objects intersecting the head Expression Relaxed eyes and a neutral or slight natural smile An exaggerated smile, tense expression, or obvious beauty filter Framing Near-front-facing, head and shoulders visible, some space around the hair Extreme selfie angle, tight crop, or shoulders cut unevenly Lighting Soft and even across the face Strong overhead shadow, backlighting, or mixed color temperatures ## Match the Outfit to the Role Look at the organization's public team photos and the level of formality in the role. Corporate, legal, finance, and executive applications may call for a structured jacket or suit. Technology, consulting, startup, and creative roles may suit a clean shirt or smart-casual blazer. Medical or research clothing should appear only when it accurately reflects the person's real work or qualification. ## Choose a Background and Lighting When no background is specified, white, light gray, and other quiet solid colors are dependable. A softly blurred office or outdoor background can work for a portfolio or profile, but a simple background is usually easier to crop into an application form. Use soft window light or even indoor lighting. Make sure one side of the face is not much darker than the other and that the background does not make the hair disappear. ## Review the Expression and Framing Keep the camera close to eye level and look toward the lens. A slight natural smile usually feels approachable without turning the image into a casual social photo. Leave the full hair outline and both shoulders visible so the final crop can be adjusted without cutting into the face. ## How to Choose a Source Photo for AI The source image sets the ceiling for an AI resume headshot. AI can change an outfit or background, but it cannot reliably recover identity details that are hidden, blurred, filtered, or heavily distorted. Choose a source photo with: - one person in the frame; - a sharp, near-front-facing face; - the camera close to eye level; - even light and natural skin texture; - visible hairline, ears where possible, and both shoulders; - no strong beauty filter, sunglasses, mask, or object covering the face; - enough resolution to inspect the eyes, hair, and facial outline. Avoid group crops, screenshots, motion blur, extreme wide-angle selfies, and images where the face occupies only a small part of the frame. A useful source keeps the face near the center, the camera close to eye level, and the hair and shoulders visible. Extreme angles, blur, and strong filters make identity preservation less reliable. ## How to Create a Resume Photo With AI The PiAPI AI Headshot Generator uses a portrait as the identity reference, then applies a selected outfit and background. The workflow is useful when you already have a clear photo but want to compare a few professional looks without arranging a new shoot. ## Step 1: Confirm the Destination Check that a photo is appropriate, then record the required crop, aspect ratio, file type, and file-size limit. ## Step 2: Prepare the Source Choose a clear portrait using the checklist above. Crop only enough to remove distractions; keep the hair and shoulders available for the generator. ## Step 3: Choose an Outfit and Background Select the combination closest to the real role and working environment. A restrained option is usually safer than a dramatic transformation. ## Step 4: Generate More Than One Option Create a small set with different professional combinations. Review the first outputs before changing more settings so you can identify whether the source or the style choice is causing a problem. ## Step 5: Compare and Export Place the result beside the source at the same size. Check identity, hair, clothing joins, background edges, and the application preview. Export only the result that still looks like the same person. The source and generated image should be shown side by side so readers can inspect identity, clothing, lighting, and crop. The source and generated image should be shown side by side so readers can inspect identity, clothing, lighting, and crop. ## Create a professional resume photo from your portrait Upload a clear photo, compare outfit and background presets, and review the result before downloading. Try the AI Headshot Generator ## Professional Headshot Styles by Role We tested four outfit and background combinations with one synthetically generated source portrait. Each image shown is the first output from its run, with no extra skin retouching or color correction. The examples are not a promise that every input will produce the same result; they are a practical way to compare role fit and identity preservation. ## Corporate / Formal A restrained suit and solid background create a conventional corporate option. Check the tie, collar, lapels, and facial outline at full size. ## Startup / Smart Casual A blazer, open professional shirt, and softly blurred office can suit technology, consulting, and startup roles without looking overly formal. ## Creative Professional The outdoor setting feels approachable, but the wider framing and facial outline should be compared carefully with the source. ## Medical / Research Use occupational clothing only when it honestly reflects the person's role. Inspect the coat, collar, buttons, and hair edges for generated artifacts. Across the four first outputs, the formal and medical styles stayed closer to the source face, while the creative example showed a more noticeable change in facial outline and framing. The key lesson is to judge both visual polish and resemblance; the most attractive image is not automatically the most accurate one. ## AI Output Quality Checklist An AI portrait can look convincing as a thumbnail while showing problems at full size. Compare the source, generated result, and final application preview in that order. Review Area What to Inspect Pass Condition Identity Face shape, eyes, nose, mouth, apparent age, skin tone, glasses A colleague or interviewer would immediately recognize the same person Hair and skin Hairline, flyaways, ears, skin texture No smearing, plastic texture, or unnatural edges Clothing Collar, buttons, tie, lapels, jewelry Joins and symmetry look physically plausible Background Hair and shoulder edges, shadows, light direction No halo, cutout edge, or conflicting light Export Crop, dimensions, format, file size Matches the destination's requirements and preview The creative result looks polished, but the face appears narrower and the framing is wider than the source. Side-by-side review makes those changes easier to notice. The creative result looks polished, but the face appears narrower and the framing is wider than the source. Side-by-side review makes those changes easier to notice. If the face shape, apparent age, skin tone, or expression changes materially, choose another output. A professional resume photo should improve presentation without inventing a different identity. ## AI Generation vs Editing vs a Photo Studio Choose the method based on what is wrong with the source image. If the face and lighting are already good and only the crop or background needs tidying, ordinary editing is the simplest option. If you want to compare several outfits or backdrops from a clear portrait, AI generation can be efficient. If you need help with pose, lighting, and expression from the start, a professional studio offers the most control during capture. Method Best For Main Advantage What to Check AI generation Comparing professional outfits and backgrounds from a clear portrait Fast visual options without a new shoot Identity changes and generated clothing artifacts Standard editing Cropping, sizing, exposure, or background cleanup on a good photo Preserves the original face most directly Excessive smoothing or poor background removal Photo studio Applicants who need capture, pose, lighting, and live direction Quality can be controlled from the camera onward Cost, scheduling, and delivery terms ## Before Using Your AI Resume Photo Keep the final review practical: - Does it look like you? The face, age, skin tone, hair, and expression should remain recognizable. - Is it honest for the role? Clothing should fit the professional context without implying a uniform, qualification, or position you do not hold. - Does it meet the instructions? Follow the employer's rules, including any restriction on AI-created or edited images. Do not reuse an AI-composited resume photo for a passport or official identity document. For example, the U.S. State Department's digital passport photo guidance says not to use AI-created or edited photos. Always follow the latest rules from the authority issuing the document. ## Resume Photo FAQ ## Should I Include a Photo on My Resume? It depends on the market and application. In the United States and United Kingdom, standard guidance is generally to omit a resume or CV photo unless it is specifically requested. Other markets and formats may commonly include one. Follow the employer, application form, and reliable local guidance. ## What Size Should a Resume Photo Be? Use the dimensions, aspect ratio, file format, and maximum size specified by the employer or platform. If no dimensions are given, fit the image to the supplied photo field and inspect the upload preview instead of assuming one universal standard. ## Does the Background Have to Be White? Not unless the application says so. White, light gray, and muted solid colors are safe starting points because they are easy to crop and keep attention on the face. A softly blurred professional setting may work for profiles and portfolios. ## What Should I Wear in a Resume Photo? Choose clean clothing that matches the role and the organization's level of formality. A suit can fit corporate or executive roles, while a shirt or smart-casual blazer may feel more credible for technology, consulting, startup, and creative work. ## Can I Use a Selfie as the AI Source? Yes, if it is sharp, near-front-facing, evenly lit, and taken close to eye level. Avoid an extreme wide-angle selfie, a strong filter, or a crop that hides the hair and shoulders. ## Can I Use an AI-Generated Resume Photo? Consider it only when the employer permits it and the result still represents your real appearance. Review identity, clothing, background, crop, and file requirements before submitting. ## What if the AI Output Does Not Look Like Me? Do not use that output. Compare the source and result at the same size, then choose a closer variation or switch to ordinary editing of a good original photo. ## Can I Use an AI Photo for a Passport or Official ID? Do not assume that you can. Official documents have separate capture and editing rules. Prepare a compliant photo according to the current instructions from the issuing authority. ## Is AI Better Than a Photo Studio? Neither is always better. AI is convenient when you have a clear portrait and want to compare professional styles. A studio is more suitable when you need help with lighting, pose, and expression during the shoot. Standard editing is enough when the original photo is already strong. ## Conclusion A strong resume photo starts with the application context, not with a preset. Confirm that a photo belongs in the application, follow the upload requirements, and choose a source image that shows your real face clearly. Clothing, background, and formality should support the role without making the applicant look like someone else. AI can help you compare professional looks from an ordinary portrait, but the final decision remains yours. Review the generated image beside the source, inspect it at full size, and use only a result that is recognizable, honest, and technically suitable. When you are ready, create a professional resume photo with the PiAPI AI Headshot Generator . ## References - U.S. Equal Employment Opportunity Commission: Prohibited Employment Policies/Practices — applicant photograph guidance, accessed 2026-07-28 - Birmingham City Council: CV writing — UK CV photo guidance, accessed 2026-07-28 - Europass: Create your Europass CV — professional photograph support in the Europass workflow, accessed 2026-07-28 - U.S. Department of State: Uploading a Digital Photo — official passport photo editing guidance, accessed 2026-07-28 - PiAPI AI Headshot Generator — upload, outfit, background, generation, and review workflow, checked 2026-07-28 ## How to Use an Image Background Remover API Learn how to use PiAPI's image background remover API with cURL: create a task, poll its status, download the result, and check transparent PNG quality. An image background remover API accepts an image and returns its foreground subject as a reusable cutout. PiAPI's Image Background Remover provides this through an asynchronous image-editing workflow: create a task, save its ID, poll for completion, and download the result. This guide covers the workflow with cURL and JavaScript. It also examines three before-and-after examples—a ceramic mug, fine white fur, and transparent glass—to show what the API response alone cannot tell you about edge quality. ## Definition An image background remover API accepts an image, detects its foreground subject, removes the surrounding background, and returns a reusable cutout. With PiAPI, you create a background-removal task, poll its task ID, and retrieve the completed image URL. ## Quick answer To use PiAPI's image background remover API: - Get a PiAPI API key and choose a safe test image with a public URL. - Create a background-remove task with a removal model. - Save the task_id returned by the API. - Poll GET /api/v1/task/{task_id} until the task completes or your application reaches its own timeout. - Download output.image_url, then inspect the cutout on light and dark backgrounds. ## Key takeaways - PiAPI uses an asynchronous create-task and get-task workflow. - The product currently lists RMBG-1.4, RMBG-2.0, and BEN2. - Check outputs on both light and dark destination backgrounds. - Download completed results instead of assuming the returned URL is permanent. ## In this guide - What the API does - What you need - API workflow - JavaScript example - Model testing - Three real examples - Quality checklist - Repeat processing - Playground vs API - Pricing and limitations - FAQ ## What is an image background remover API? An image background removal API separates a foreground subject from its surrounding scene and returns an image that can be placed in a new composition. A common result is a PNG with alpha transparency: pixels outside the subject can be fully or partially transparent instead of being filled with white. Background removal keeps the foreground and removes the scene around it. Object removal erases a selected element, while generative replacement creates new visual content. The PiAPI endpoint covered here produces a foreground cutout through the documented background-remove task type. The output still needs visual review. A mask can retain the main object while leaving color spill around hair, losing translucent areas, or carrying part of the old background through clear glass. ## What you need before starting - a PiAPI account and a PiAPI API key; - a test image available through a publicly reachable URL; - cURL for the first request; - Node.js 18 or later for the JavaScript example; - a location where completed images can be saved. The current create-task API reference demonstrates a URL in input.image . Do not assume that this field accepts a local filesystem path. Upload local sources to an accessible location before submitting the task. ## Security Keep the PiAPI API key on the server or in an environment variable. Never place a real API key in browser code, a public repository, an article, or a screenshot. For bash or zsh, set the key with: ``` export PIAPI_API_KEY="replace-with-your-key" ``` In PowerShell, use: ``` $env:PIAPI_API_KEY = "replace-with-your-key" ``` ## How to use the PiAPI image background remover API ## Step 1: Test the image and choose a model PiAPI's current product page lists RMBG-1.4, RMBG-2.0, and BEN2. For a new image category, compare the same representative source across the available models and inspect it on the backgrounds used by your product. Use the embedded playground before writing the API request. Check interior gaps, fine edges, and transparent areas, then keep the same source image and selected model for the code walkthrough. For task history and the full product experience, use the full PiAPI Image Background Remover . ## Step 2: Create a background-removal task Send a POST request to https://api.piapi.ai/api/v1/task with: - model: Qubico/image-toolkit task_type: background-remove input.rmbg_model: the selected removal model input.image: a publicly reachable image URL This example uses RMBG-2.0 to demonstrate the request shape, not as a universal recommendation. ``` curl --request POST \ --url "https://api.piapi.ai/api/v1/task" \ --header "Content-Type: application/json" \ --header "x-api-key: ${PIAPI_API_KEY}" \ --data '{ "model": "Qubico/image-toolkit", "task_type": "background-remove", "input": { "rmbg_model": "RMBG-2.0", "image": "https://example.com/images/product-photo.jpg" } }' ``` According to the current documentation, a successful create response contains a pending task and a data.task_id . Save that identifier; the image is not necessarily ready when the create request returns. ``` { "code": 200, "data": { "task_id": "YOUR_TASK_ID", "model": "Qubico/image-toolkit", "task_type": "background-remove", "status": "pending", "output": null }, "message": "success" } ``` ## Step 3: Poll the task ID Use the returned ID with the endpoint described in the get-task API reference : ``` curl --request GET \ --url "https://api.piapi.ai/api/v1/task/${TASK_ID}" \ --header "x-api-key: ${PIAPI_API_KEY}" ``` Use bounded polling instead of an endless loop. The interval and attempt limit in this guide are application safeguards, not published PiAPI limits. Adjust them after checking the latest documentation and account constraints. When the task completes, read data.output.image_url . Stop on an unsuccessful HTTP response, a task error, or your application's timeout, and log enough context to investigate without recording the API key. ## Step 4: Download and save the result Once output.image_url is available, download the file rather than treating the URL as permanent storage. ``` curl --fail --location \ --url "${OUTPUT_IMAGE_URL}" \ --output "product-photo-background-removed.png" ``` Check the download's HTTP status and content type before passing it to another system. The general PiAPI output-storage guide does not currently state a retention period specifically for this background-removal endpoint, so save any result your application needs to keep. ## Complete JavaScript example This Node.js example creates a task, polls with an application-defined limit, checks the completed response, and saves the output. It uses the built-in fetch available in Node.js 18 and later. ``` import { writeFile } from "node:fs/promises"; const API_BASE = "https://api.piapi.ai/api/v1"; const API_KEY = process.env.PIAPI_API_KEY; const INPUT_IMAGE_URL = "https://example.com/images/product-photo.jpg"; const POLL_INTERVAL_MS = 2_000; // Application choice, not a published API limit const MAX_POLL_ATTEMPTS = 60; // Application choice, not a published API limit if (!API_KEY) { throw new Error("Set PIAPI_API_KEY before running this script."); } async function piapiRequest(path, options = {}) { const response = await fetch(`${API_BASE}${path}`, { ...options, headers: { "x-api-key": API_KEY, ...(options.body ? { "Content-Type": "application/json" } : {}), ...options.headers, }, }); const body = await response.json().catch(() => null); if (!response.ok) { throw new Error( `PiAPI request failed (${response.status}): ${JSON.stringify(body)}` ); } return body; } async function createBackgroundRemovalTask() { const response = await piapiRequest("/task", { method: "POST", body: JSON.stringify({ model: "Qubico/image-toolkit", task_type: "background-remove", input: { rmbg_model: "RMBG-2.0", image: INPUT_IMAGE_URL, }, }), }); const taskId = response?.data?.task_id; if (!taskId) { throw new Error("Create response did not include data.task_id."); } return taskId; } const delay = (milliseconds) => new Promise((resolve) => setTimeout(resolve, milliseconds)); async function waitForTask(taskId) { for (let attempt = 1; attempt <= MAX_POLL_ATTEMPTS; attempt += 1) { const response = await piapiRequest(`/task/${taskId}`); const task = response?.data; if (task?.status === "completed") { const imageUrl = task?.output?.image_url; if (!imageUrl) { throw new Error("Completed task did not include output.image_url."); } return imageUrl; } if (task?.status === "failed" || task?.error?.code) { throw new Error(`Background-removal task failed: ${JSON.stringify(task.error)}`); } await delay(POLL_INTERVAL_MS); } throw new Error("Task did not complete within the configured polling window."); } async function downloadImage(imageUrl, outputPath) { const response = await fetch(imageUrl); if (!response.ok) { throw new Error(`Output download failed (${response.status}).`); } const bytes = Buffer.from(await response.arrayBuffer()); await writeFile(outputPath, bytes); } async function main() { const taskId = await createBackgroundRemovalTask(); console.log(`Created task: ${taskId}`); const imageUrl = await waitForTask(taskId); await downloadImage(imageUrl, "background-removed.png"); console.log("Saved background-removed.png"); } main().catch((error) => { console.error(error.message); process.exitCode = 1; }); ``` For production use, add structured logs, cancellation, deliberate retry rules, and durable storage. Treat these as application policies rather than API guarantees. ## How to test the available background-removal models You can compare the available background-removal models RMBG-1.4, RMBG-2.0, and BEN2 in the PiAPI playground. There is no single winner for every image: correction needs can change with the subject and destination design. - Select two or three images representative of your workload. - Run the exact same files through each available model. - Preserve the original output files without retouching them. - Place every cutout over white, near-black, and the actual destination background. - Compare subject retention, interior gaps, fine edges, color spill, and translucent areas. - Record which model requires the least manual correction for that image category. Do not infer speed or reliability from one run. Measure those factors separately under realistic workload conditions. ## What our three background-removal examples showed We tested three deliberately different subjects: a solid ceramic mug, a white dog with fine fur, and a transparent glass bottle. All three downloaded PNGs contained alpha transparency. We then placed each cutout over white and near-black backgrounds to make edge and transparency problems easier to see. These are first-party qualitative workflow observations, not a cross-model benchmark. The specific removal model was not recorded, so the results should not be used to rank RMBG-1.4, RMBG-2.0, or BEN2. ## Simple product: ceramic mug Before After: transparent PNG shown over a checkerboard The mug was isolated cleanly, including the open space inside its handle. Its rim, glaze, handle, and base remained intact. A faint warm fringe became visible along parts of the edge over near-black, showing why even a straightforward product image should be checked against its destination color. ## Fine edges: white fur Before After: transparent PNG shown over a checkerboard The output retained the dog's body, ears, paws, tail, and many wispy strands. It looked cohesive on near-black. On white, however, navy-gray contamination appeared around the ears, coat, and tail. Fine detail can survive while still carrying color from the original scene. ## Difficult material: transparent glass Before After: transparent PNG shown over a checkerboard The bottle's outer boundary, cap, liquid line, reflections, and glass walls remained visible. It looked convincing on white, but near-black revealed pale residual color inside and around the translucent material. Transparent and reflective objects deserve compositing tests, not just a quick check of the outer silhouette. ## How to check background-removal quality Background-removal quality means retaining the intended subject while removing the old scene without halos, color spill, lost interior gaps, or broken translucent areas. A transparent file is therefore not automatically production-ready. ## Pre-production QA checklist - Open the original output and confirm that it contains alpha transparency. - Place the cutout over white (#FFFFFF). - Place the cutout over near-black (#111111). - Test it on the actual destination background. - Inspect hair, fur, thin edges, handles, straps, and interior gaps at 100% zoom. - Look for light or dark halos and color inherited from the source background. - Inspect glass, smoke, veils, reflections, and other partially transparent regions. - Confirm that no part of the intended subject was cropped or erased. - Review the image again at its real display size. - Confirm that the saved format works in the next system in your pipeline. White backgrounds expose dark or colored fringes. Dark backgrounds expose light halos and areas that became too opaque. Testing both catches problems that may remain invisible in a checkerboard preview. ## How to handle repeat or automated processing For repeat workloads, track each image as its own task. Store the source identifier, PiAPI task ID, status, attempt count, and final output location. - take a queued source image; - create one background-removal task; - store its task ID; - poll with bounded concurrency; - log failed or timed-out work for review; - download completed outputs into durable storage; - run automated file checks before publishing. Check the current PiAPI documentation before setting concurrency, rate-limit, webhook, or bulk-processing behavior. This guide does not assume a dedicated batch endpoint for background removal. ## Playground vs API: Which workflow should you use? Start with the playground for an unfamiliar image category. Move to the API after establishing an input policy, model choice, download process, and QA standard. Choose the playground when Choose the API when Testing one or a few images Processing repeat workloads Comparing available models manually Integrating removal into an application Reviewing edge quality interactively Tracking task IDs programmatically Validating a new source-image category Building an automated media pipeline ## How much does PiAPI background removal cost, and what are its limitations? PiAPI currently lists background removal at $0.001 per generation . Pricing can change, so verify the current per-image pricing before estimating a production workload. - the documented JSON request uses a publicly reachable image URL; - the product playground currently accepts JPEG, JPG, and PNG uploads, but that does not establish every API input limit; - rate limits, file-size limits, and image-dimension limits are not specified on the create-task page reviewed for this guide; - the output-storage page does not state a retention period specifically for this endpoint; - glass, fur, reflections, and source-background color can still require inspection or cleanup. ## Frequently asked questions ### What is an image background remover API? An image background remover API segments the foreground subject from its surroundings and returns a reusable cutout. It supports product catalogs, profile images, creative tools, and automated media workflows where manual masking would create a bottleneck. ### How do I remove an image background through the PiAPI API? Send a POST request to /api/v1/task with Qubico/image-toolkit, the background-remove task type, a removal model, and an image URL. Save the returned task ID, poll /api/v1/task/{task_id}, and download output.image_url after the task completes. ### Which PiAPI background-removal model should I use? Test RMBG-1.4, RMBG-2.0, and BEN2 with the same representative image, then inspect every result over light, dark, and real destination backgrounds. Choose from observed correction needs rather than assuming one model is universally best. ### What does the PiAPI background-removal API return? The create request returns task data that includes a task_id and an initial status such as pending. After the task completes, the get-task response exposes the result under data.output.image_url. Download that image if your application needs durable storage. ### How can I get cleaner hair or fur edges? Start with a sharp, adequately sized image and clear subject separation. Compare available models using the same source, then inspect the cutout on both light and dark backgrounds. If source-background color remains in fine strands, use a more suitable source or add edge-decontamination review before publishing. ### Can I process multiple product images? Yes. Create and track repeated tasks in your own queue, use bounded concurrency, store every task ID, log failures, and download completed outputs. Confirm current PiAPI account limits and bulk-service documentation before choosing throughput or retry settings. ### Should I store the returned output image? Yes, if the result is needed beyond the immediate task workflow. The current general output-storage documentation does not list a retention period specifically for background removal. Download the file, verify it, and store it in a location whose retention and access controls match your application. ### When should I use the playground instead of the API? Use the playground to test individual images, compare models, and review edge quality manually. Use the API when the input policy and quality standard are established and you need task tracking, application integration, repeat processing, or automated downloads. ## Start with one representative image Reliable background removal depends on both task handling and visual QA. Preserve the task ID, use bounded polling, save the completed result, and test the cutout against its real destination backgrounds. A solid product may need only a quick edge check, while fur and glass can expose color spill or transparency problems. To put this image background remover API workflow into practice, test a representative image with PiAPI , compare the available models, and inspect the downloaded PNG on light and dark backgrounds before automating it. ## How to Make AI Dance Videos: Presets, Reference Videos, and Troubleshooting Tips Learn how to make an AI dance video from one photo, choose between dance presets and reference videos, prepare better inputs, and troubleshoot unstable motion. AI dance videos animate a person, avatar, or character image with a dance motion. The basic inputs are one subject image and either a built-in dance preset or a custom reference video. The same motion can produce different results depending on the source composition and how clearly the hands and feet are visible. This guide explains how to make an AI dance video from one photo, choose a motion source, prepare the input image, and troubleshoot common problems using two original examples. Key points: Start with a built-in preset for the quickest test. Use only reference videos you have permission to use. Choose a full-body image with visible limbs and open space around the subject. Change one variable at a time when troubleshooting. ## What is an AI dance video? Definition: An AI dance video is a generated video that applies movement from a preset or reference video to a still subject image. It lets a photo or character perform a dance without filming the subject or manually animating every frame. The basic relationship is “subject image + motion = generated video.” The appearance mainly comes from the image, while the movement changes with the selected motion source. Review these inputs separately when diagnosing a result. ## How to make an AI dance video from one photo PiAPI's Kling AI Dance Generator uses Kling 2.6 Motion Control to apply dance motion to a still image. The workflow has five steps: - Prepare the subject image: Choose an image with one person or character and as much of the full body visible as possible. - Upload the image: The current playground accepts JPEG, JPG, and PNG images. - Choose the dance motion: Select a built-in preset for a quick start or a custom reference video for a specific motion. - Review the settings and generate: Check the available orientation, quality, and source-audio controls before submitting. - Review the entire video: Watch the face, hands, feet, clothing, and framing from beginning to end. When retrying, change only the image, motion, or one setting so the difference is easier to judge. ## Should you use a preset or a reference video? Choose a preset for a quick test and a reference video when you need a specific movement. Both require a subject image, but their preparation and level of motion control differ. Comparison Dance preset Custom reference video Required assets Subject image Subject image and a video you may use Preparation Minimal A suitable reference clip is required Motion source Select from provided choices Uploaded clip guides the motion Best for Fast prototypes and style comparisons Trying specific choreography or movement For a first test, use a preset to check whether the source image works well with motion. Switch to a custom reference if the preset list does not contain the movement you need. For a broader tool comparison, see our guide to choosing an AI dance generator . ## What makes a good input photo for AI dance? A full-body image is usually easier to evaluate than a tightly cropped portrait because it reveals the body's position and the limbs. Check the following before uploading: - Keep one main person or character in the image. - Show the full body or most of it inside the frame. - Make the face, torso, arms, hands, legs, and feet visible where possible. - Avoid crossed limbs or hands and feet hidden by clothing or objects. - Use a subject that is easy to distinguish from the background. - Leave space above, below, and beside the subject for larger movements. - Use a sharp image with a clearly readable starting pose. Input example: the full-body image used for both generations. These checks do not guarantee a perfect result, but they make it easier to separate input problems such as hidden hands or insufficient framing from motion-related problems. ## How to choose a custom reference video PiAPI's playground supports custom reference-video uploads as well as built-in presets. This is useful for choreography that is not available in the preset list or a specific movement from an approved clip. Choose a clip with one clearly visible dancer, the body and limbs inside the frame, and limited camera shake, cuts, and strong motion blur. Use only a video for which you have permission to generate and publish derivative output. We did not generate a custom-reference result for this article, so this guide does not compare its output quality or motion accuracy with the presets. ## Example: animating the same photo with Phonk Dance and Scuba Dance Test conditions (July 23, 2026): We applied two built-in presets to the same input image. The supplied Phonk Dance output is 14.93 seconds and the Scuba Dance output is 7.67 seconds. Quality mode, orientation, task ID, cost, and retry count were not recorded. Because the preset outputs have different durations, this is not a controlled performance benchmark. The examples show how we reviewed subject consistency, hands, feet, clothing, and framing. ## Phonk Dance: a large-motion example that stays inside the frame Generated example: the same input image animated with the Phonk Dance preset. The Phonk Dance output includes sideways steps, raised arms, crouching, and forward movement. The face, hairstyle, navy jacket, white shirt, and beige trousers remain broadly consistent, and the full body stays inside the frame. During faster hand movement, the fingertips briefly become less distinct. The source motion used inside the preset is not displayed, so we did not score exact choreography reproduction. ## Scuba Dance: an example for reviewing expression and overlapping hands Generated example: the same input image animated with the Scuba Dance preset. The Scuba Dance output includes forward gestures, crossed arms, sideways steps, and a low crouch. Clothing colors, hairstyle, body proportions, and the background remain broadly consistent, with no obvious breakdown in the legs or shoes. The expression becomes more cheerful than the source image near the beginning. The hand shape also looks less clear where a hand overlaps the face or torso. Review expression, hand overlap, and framing throughout the complete output—not just the first seconds. ## Why AI dance videos fail and what to try next An unwanted result rarely proves one specific cause. Identify the most visible symptom, then change one input or setting in the next generation. Symptom Possible cause What to try next Hands or feet look unstable Limbs are hidden, motion is fast, or a hand overlaps the body Use an image with clearer limbs or try another preset Face or clothing changes Subject is small, image is unclear, or there are many overlaps Replace it with a sharper image containing one clear subject Motion does not match the intent The selected preset or reference differs from the intended movement Change the motion source while keeping other settings fixed Body leaves the frame There is too little space around the subject Use an image with more open space on every side It is unclear which change helped Several inputs or settings changed together Change the image, motion, and settings one at a time This process is a diagnostic method, not a guarantee of improvement. Fast hand movement and body overlap can only be judged by playing the generated video from beginning to end. ## Try PiAPI's Kling AI Dance Generator PiAPI's Kling AI Dance Generator is a browser interface for Kling 2.6 Motion Control. Upload an image and select a dance preset or custom reference video to generate an AI dance video. Test the image and movement in a short demo, then continue to Kling 2.6 Motion Control or the Kling 2.6 API when you need a development workflow. ## Frequently asked questions ### Can I make an AI dance video from one photo? Yes. Upload one subject photo or character image, then choose a dance preset or custom reference video. Start with one full body, visible hands and feet, and clear space around the subject. ### Do I need a reference video? No. Built-in dance presets let you test the workflow without preparing your own reference video. Upload a custom reference video only when you want a specific movement and have permission to use it. ### What photos work best for AI dance videos? Use a clear image with one main subject, most or all of the body visible, unobstructed hands and feet, and open space around the subject. ### What should I do if hands or feet look distorted? Try an image with clearer limbs or a different dance preset. Keep other settings unchanged and adjust one variable at a time. ### Can I use a character instead of a person? Yes, character images can be used as input. Start with one clearly visible subject and make sure the face, torso, arms, and legs are easy to distinguish. ### Can I try an AI dance video for free? At the time of this article's review (July 23, 2026), PiAPI's Kling AI Dance Generator offered a five-second free demo. Availability, account requirements, and credit terms may change, so check the current playground before generating. ## Summary Use a preset for the fastest test and a custom reference video when you need a specific motion. In both cases, begin with a full-body image with visible limbs, then review the whole output. Switch between presets with the same image first to find the movement that best matches the intended style. ## Try AI dance video generation from one photo Upload one image and choose a dance preset to create an AI dance video with PiAPI's Kling AI Dance Generator. ## Seedream 5 Pro vs Nano Banana Pro: Which Model Should You Use? Compare Seedream 5 Pro vs Nano Banana Pro with matched playground examples, PiAPI pricing, text rendering, editing, and API differences. Find your best fit. Seedream 5 Pro was the better fit in our July 17, 2026 PiAPI sample when exact prompt details, text accuracy, and source-image preservation mattered most. Nano Banana Pro offered the lower 2K price—$0.105 versus $0.136—and the listed 4K tier. These findings come from six first outputs across three matched prompts, not a universal quality ranking. We compared Seedream 5 Pro vs Nano Banana Pro through the PiAPI playground using two text-to-image prompts and one image-editing prompt. Each scenario uses one unedited output from each model. It is a practical snapshot to help teams decide what to test next, not a consistency benchmark. Quick takeaways - Choose Seedream 5 Pro when precise instructions, exact text, or preservation of an input image matters most. - Choose Nano Banana Pro when you need a priced 4K tier or want the lower 2K price: $0.105 versus $0.136. - Test both when composition and creative interpretation matter more than strict prompt compliance. - The six images are directional evidence; repeat the comparison with prompts from your own workflow before choosing. ## Table of Contents - Quick comparison - Model overview - How we tested - Image comparison results - Pricing - API differences - Which model should you use? - Limitations - FAQ - Final verdict ## Seedream 5 Pro vs Nano Banana Pro: Quick Comparison The table separates documented PiAPI facts from observations in our six-output playground sample. Comparison point Seedream 5 Pro Nano Banana Pro Evidence Documented 2K price $0.136 per image $0.105 per image PiAPI documentation Priced resolution tiers 1K and 2K 1K, 2K, and 4K PiAPI documentation API reference-image input Up to 10 image URLs documented Not stated on the designated API page PiAPI documentation Product-photo test More precise watch details Wider atmosphere, but missed the crown and hand positions Our first-output test Poster test More accurate text and numbers Better landscape orientation, but repeated a time Our first-output test Image-editing test Better source framing and geometry preservation More restrained added detail, but changed the framing Our first-output test The facts in this article were checked against the Seedream 5 API documentation and Nano Banana Pro API documentation on July 17, 2026. ## What Are Seedream 5 Pro and Nano Banana Pro? ## Seedream 5 Pro through PiAPI Seedream 5 Pro is an image-generation task available through PiAPI using model: seedream and task_type: seedream-5-pro . Its documentation lists 1K and 2K output, JPEG, PNG, and WebP formats, and up to 10 optional reference-image URLs for editing or style transfer. The default Pro size is 1K. At a 16:9 aspect ratio, the documentation gives approximately 1312×736 pixels for 1K and 2560×1440 pixels for 2K. A separate seedream-5-pro-less-restriction task uses the same input schema with a more permissive moderation tier and higher pricing. You can try the model in the Seedream 5 Pro playground . ## Nano Banana Pro through PiAPI Nano Banana Pro is an image-generation task available through PiAPI using model: gemini and task_type: nano-banana-pro . Its designated documentation lists 1K, 2K, and 4K pricing and shows request fields for the prompt, output format, aspect ratio, resolution, and safety level. The Nano Banana Pro API page does not currently describe a reference-image input field or list its complete aspect-ratio and output-format options. We therefore treat the image upload used in our playground edit as playground behavior, not proof of an API field. You can test the visible controls in the Nano Banana Pro playground . ## How We Tested Seedream 5 Pro vs Nano Banana Pro We used three matched scenarios in the PiAPI playground: a photorealistic watch product shot, an event poster with exact text and numbers, and a sneaker edit using the same source image. Each pair used the same prompt, and we kept the first completed output from each model without regenerating only the weaker side. We selected 16:9 and the closest visible 2K setting where available, then downloaded the original PNG files without editing them. The downloaded dimensions were not fully equivalent: Seedream outputs included 1312×736 and 2560×1440 files, while all three Nano Banana Pro outputs were 1376×768. That mismatch limits resolution-level conclusions, so the analysis focuses on visible prompt adherence, composition, text accuracy, and edit preservation. Because this is one output per model per scenario, it cannot establish consistency, average failure rates, or which model will win across every prompt. It can show how the two models handled these three specific tasks on the test date. ## Image Comparison Results ## Example 1: Photorealism and Material Detail Seedream 5 Pro Nano Banana Pro Both models produced convincing product photographs with realistic metal, leather, water, and stone. Seedream 5 Pro followed the requested watch details more closely, placing the crown on the right and setting the hands near 10:10, while Nano Banana Pro moved the crown to the top and used a different hand position. Nano Banana Pro delivered a strong atmospheric scene, but Seedream 5 Pro was the more precise result for this prompt-controlled product shot. ## Example 2: Text, Numbers, and Structured Layout Seedream 5 Pro Nano Banana Pro Seedream 5 Pro reproduced the required event text and numbers accurately with a clear hierarchy, but placed a portrait-oriented poster inside the landscape canvas. Nano Banana Pro followed the requested landscape grid more directly, although it repeated 14:05 and separated the schedule times from their corresponding labels. Seedream 5 Pro was stronger for exact text handling, while Nano Banana Pro adhered more closely to the requested overall orientation. ## Example 3: Playground Image-to-Image Editing Shared input image Seedream 5 Pro Nano Banana Pro Both models completed the four requested edits: blue laces, an amber gum sole, a coral-red heel tab, and a warm-yellow background. Seedream 5 Pro preserved the source image's crop, scale, angle, and shoe construction more closely, although its heel tab was larger than requested. Nano Banana Pro created a more restrained heel tab, but noticeably changed the framing, scale, and viewing angle of the original product photograph. ## Seedream 5 Pro vs Nano Banana Pro Pricing PiAPI listed the following API prices on July 17, 2026. They are separate from the playground examples used in this article. Model and tier 1K 2K 4K Seedream 5 Pro $0.068 $0.136 Not listed Seedream 5 Pro less-restriction $0.085 $0.17 Not listed Nano Banana Pro $0.105 $0.105 $0.18 At 2K, Nano Banana Pro costs $0.105 per image and Seedream 5 Pro costs $0.136—a difference of $0.031 per image. PiAPI also lists a 4K price for Nano Banana Pro, while the Seedream 5 Pro page lists only 1K and 2K. For Seedream Pro, the first reference image is included at no extra charge and additional references carry a surcharge. The designated documentation currently gives conflicting surcharge amounts, so this article does not quote one. Check the live documentation when budgeting a multi-reference edit. Pricing can change, so check the Seedream 5 API pricing and Nano Banana Pro API pricing before shipping a cost-sensitive workflow. ## Seedream 5 Pro vs Nano Banana Pro API Differences Both models use PiAPI's POST https://api.piapi.ai/api/v1/task endpoint, but their task identifiers and documented input fields differ. API detail Seedream 5 Pro Nano Banana Pro model seedream gemini task_type seedream-5-pro nano-banana-pro Resolution field size resolution Documented resolution values 1K , 2K Pricing lists 1K , 2K , 4K ; request example shows 1K Aspect ratio Ten ratios are explicitly listed Request example shows 16:9 ; full list is not stated Output format jpeg , png , webp Request example shows png ; full list is not stated Reference-image input image_urls , up to 10 URLs Not stated on the designated page Moderation or safety Strict task plus a separate less-restriction task Request example includes safety_level: high For implementation details, use the current documentation rather than copying fields from the playground interface. For pricing, cURL, and tested editing workflows, use the Seedream 5.0 Pro API guide . ## Which Model Should You Use? ## Choose Seedream 5 Pro if... - Your prompts contain exact object details that must be followed closely. - Text and numerical accuracy matter more than perfect adherence to a requested poster orientation. - You need a documented API reference-image input for editing or style transfer. - Preserving the crop, scale, angle, and construction of an input product image is important. - You need documented JPEG, PNG, or WebP output selection. If you prefer Seedream but are unsure which tier to use, the Seedream 5 Pro vs Seedream 5 Lite comparison covers that narrower decision. ## Choose Nano Banana Pro if... - You want the lower documented 2K price through PiAPI. - Your workflow needs the 4K tier listed by PiAPI. - Strong landscape composition matters and you can review exact text before publishing. For the choice within Google's model family, see the Nano Banana 2 vs Nano Banana Pro comparison . ## Test both if... Use both playgrounds when your work mixes precise constraints with open-ended art direction. A brand campaign, product catalog, or poster system may value text accuracy, composition, editing fidelity, and price differently. Testing one representative prompt from your real workflow will tell you more than treating either model as a universal winner. ## Limitations of This Comparison This comparison uses only three prompts and one first output per model per prompt. Image generation is variable, so a different run could change the result. We did not test repeated generations, seeds, generation speed, failure rates, or average consistency. The downloaded output dimensions were not identical across models or across all Seedream examples. We also used the playground for the edit test, so the successful Nano Banana Pro image upload should not be interpreted as documentation of an API reference-image field. Finally, prices, parameters, and task behavior may change after July 17, 2026. ## Frequently Asked Questions ### What is the main difference between Seedream 5 Pro and Nano Banana Pro? In our three-prompt PiAPI playground sample, Seedream 5 Pro followed exact visual instructions, poster text, and source-image framing more closely. Nano Banana Pro followed the requested poster orientation and added a more restrained heel tab. Separately, PiAPI lists Nano Banana Pro at a lower 2K price and includes a 4K tier. ### Is Seedream 5 Pro or Nano Banana Pro better for image quality? Our three first-output pairs do not establish a universal image-quality winner. Both models created convincing product images: Seedream 5 Pro followed the watch details more precisely, while Nano Banana Pro gave more space to the surrounding scene. Your choice should follow the constraints that matter in your own prompts. ### Which model rendered text more accurately? Seedream 5 Pro rendered the required poster text and numbers more accurately in our single poster test. Nano Banana Pro produced a stronger landscape layout, but repeated 14:05 and separated times from their labels. This is one output from each model, so test your own copy before using either model for production typography. ### Which model is cheaper at 2K through PiAPI? Nano Banana Pro is cheaper at 2K as of July 17, 2026. PiAPI lists Nano Banana Pro at $0.105 per image and Seedream 5 Pro at $0.136 per image, making Nano Banana Pro $0.031 cheaper per standard image before any Seedream reference-image surcharge. ### Can both models edit images through PiAPI? Seedream 5 Pro has documented API editing support; Nano Banana Pro editing was verified only in the PiAPI playground. Seedream accepts up to 10 reference-image URLs for editing or style transfer, while the designated Nano Banana Pro API page does not state a reference-image field. Do not assume playground and API inputs are identical. ### Which model supports higher resolution? Nano Banana Pro has the higher priced resolution tier through PiAPI: 4K, compared with Seedream 5 Pro's listed 2K maximum. PiAPI lists Seedream 5 Pro at 1K and 2K, while Nano Banana Pro pricing covers 1K, 2K, and 4K. Confirm the available request options and pixel dimensions before building a fixed-resolution workflow. ## Final Verdict: Seedream 5 Pro or Nano Banana Pro? The Seedream 5 Pro vs Nano Banana Pro decision comes down to control, resolution, and price. Choose Seedream 5 Pro when exact prompt details, text accuracy, a documented reference input, and source preservation are the priority. Choose Nano Banana Pro when you want the lower 2K price through PiAPI, need the listed 4K tier, or prefer more flexibility in the composition. Run one representative production prompt through both playgrounds before committing to a model. You can test Seedream 5 Pro , test Nano Banana Pro , and manage your API access in the PiAPI workspace . ## How to Upscale Images and Increase Resolution Learn how to upscale images, increase resolution, choose between 2x, 4x, and 8x, and process repeat image workflows with PiAPI online. To upscale images means increasing their width and height in pixels. The result has larger pixel dimensions, which can make an image more suitable for bigger digital layouts, product pages, presentations, or further editing. The right scale factor depends on the resolution of the source image and where you plan to use the output. The PiAPI Image Upscaler lets you enlarge an image by 2x, 4x, or 8x. A higher resolution does not automatically create more authentic image detail, so review the result at the size in which it will appear. ## Key takeaways - Upscaling increases both the width and height of an image. - The 2x, 4x, and 8x factors apply to both dimensions. - Choose the lowest factor that reaches the required output size. - Check edges, textures, and small text after enlarging the image. ## In this article - What does image upscaling mean? - Increase resolution in three steps - Choose 2x, 4x, or 8x - When image enlargement is useful - What to check for image quality - Process multiple images through an API - Frequently asked questions ## What does image upscaling mean? Image upscaling increases the pixel dimensions of a file. A scale factor applies to both sides: 2x doubles the width and height, 4x multiplies each dimension by four, and 8x multiplies each dimension by eight. The total pixel count grows more quickly. A 2x output contains four times as many pixels as the input. At 4x, the output contains 16 times as many pixels, while an 8x output contains 64 times as many. These figures help you estimate the target resolution before processing a file. More pixels do not add source information; clarity still depends on the original image. ## How to increase image resolution in three steps An image upscaler lets you increase image resolution without a long editing process: - Upload an image: Open the PiAPI Image Upscaler and select the file you want to enlarge. - Choose a scale factor: Select 2x, 4x, or 8x based on the output dimensions you need. - Review the result: Start processing, then inspect the enlarged image at the size in which it will be used. Calculate the target dimensions before selecting a factor. A lower factor may already be enough and produces a more manageable file. ## 2x, 4x, or 8x: Which factor should you choose? The largest factor is not always the most practical choice. Start with the dimensions the final asset actually requires. Factor General use What to consider 2x Moderate enlargement and digital content Often enough when the source is already close to the target size 4x Larger layouts, presentations, or product images Review small text, edges, and fine structures after processing 8x Very large output dimensions or specialized workflows Limitations in the source may become more visible at a high scale factor To upscale an image to 4K, compare its existing pixel dimensions with the target resolution first. “4K” describes a target resolution; it does not automatically mean that you should select the 4x factor. ## When is image enlargement useful? Image enlargement is useful when an existing file is too small for its next placement. Common uses include product images for online stores, graphics for presentations, marketing assets, and illustrations that need to fit a larger layout. Print preparation may also require a higher resolution, depending on the final size and the print provider's requirements. For websites, match the image dimensions and file size to its displayed size. ## What should you check for image quality? Start with a clear source file. Heavy compression, blur, or visible artifacts may become more noticeable after enlargement. Inspect outlines, small text, faces, flat areas, and fine patterns in the processed output. “Without quality loss” always depends on the source. Larger dimensions do not guarantee new authentic detail, so use the lowest factor that reaches the target size and looks clean in context. ## Process multiple images with an Upscaling API An online tool suits occasional files. For regular or integrated processing, an Upscaling API can support ecommerce catalogs, media libraries, and software products without handling every image manually. PiAPI provides the Clean & Upscale Workspace for this workflow. Before integrating it, check the current API parameters, input requirements, and pricing against your image volume and output needs. ## Frequently asked questions about upscaling images ## What does it mean to upscale an image? Upscaling increases an image's width and height in pixels. A 2x factor doubles both dimensions, while 4x and 8x produce larger outputs. Review the result for visible quality issues at its intended display size. ## Can you enlarge images without losing quality? A higher pixel count does not guarantee a lossless improvement. Results depend on the source, scale factor, and display size. Start with a clear input, then check edges, text, textures, and compression artifacts. ## When should you choose 2x, 4x, or 8x? Use 2x for a moderate increase, 4x for larger dimensions, and 8x only when required. Calculate the target width and height first; the lowest sufficient factor simplifies output and quality control. ## Can you upscale multiple images through an API? Yes. An Upscaling API supports repeat or integrated workflows without processing each file manually. Product catalogs, media platforms, and automated asset pipelines are common uses. Review current API requirements and billing before implementation. ## Increase resolution with PiAPI Determine the target dimensions first, then choose 2x, 4x, or 8x. Review the output in its real use context rather than judging it only by the larger pixel count. Open the PiAPI Image Upscaler , upload your image, and choose the appropriate scale factor. For repeat workflows, the Clean & Upscale Workspace also provides access to the Upscaling API. ## Seed Audio 1.0 Stress Test: How It Handles Difficult TTS Scripts Hear how Seed Audio 1.0 through PiAPI handles names, numbers, acronyms, punctuation, long scripts, and reference audio in our transparent TTS stress test. Seed Audio 1.0 field test PiAPI PiAPI July 15, 2026 Most text-to-speech demos stop at a short, clean sentence. Production scripts do not. We put Seed Audio 1.0 through six PiAPI tests covering difficult names, technical terms, changing punctuation, everyday numbers, and longer narration, then kept the first supplied output from each test—including one clear failure. If you are evaluating the model for an app, explainer, podcast, or scripted voiceover, you can read every input and play every result below. The scope is deliberately narrow: this is a test of PiAPI's byteaudio / seed-audio-1.0 endpoint for speech generation and reference-audio guidance, not every capability associated with the wider Seed Audio model family. Seed Audio 1.0 completed five default-voice scripts in our PiAPI test, including difficult names and a 192-word passage. Reference guidance transferred a synthetic Japanese singing voice into smooth English, but that output omitted most of its script and began with silence. Default TTS looked reliable; reference-based results need careful review. What we tested: Five default-voice TTS scripts and one synthetic reference-audio input, with one primary output per script. Every generated result used WAV at 24 kHz with neutral rate, pitch, and loudness settings. We evaluated the files through full human listening and technical metadata inspection. Last tested: July 15, 2026. Task IDs and generation latency were not recorded. ## Contents ## What is Seed Audio 1.0 through PiAPI? Seed Audio 1.0 through PiAPI is a text-to-speech endpoint that turns written scripts into downloadable speech. It uses the model name byteaudio and task type seed-audio-1.0 , with optional reference audio for voice or style guidance. PiAPI's current Seed Audio documentation defines that request shape and the available speech controls. Here, “Seed Audio 1.0” refers specifically to the PiAPI endpoint and playground , not every capability associated with the wider model family. The tested task does not document music or sound-effect generation. We ran the test on July 15, 2026. Tests 1–5 used the default voice without a reference. Test 6 used a 9.85-second AI-generated Japanese song as its reference input; it was not a recording of a real person. Settings stayed fixed across the set. Model byteaudio Task type seed-audio-1.0 Output format WAV Sample rate 24,000 Hz Speech, pitch, and loudness rate 0 / 0 / 0 Primary outputs One per script We did not regenerate an output because it sounded weak. Keeping the first result prevents a capability test from becoming a gallery of hand-picked successes. We inspected container, sample rate, channel count, and duration, then listened to each file from beginning to end against its exact script. Our rubric covered text fidelity, audio quality, pronunciation and prosody, artifacts, completeness, voice consistency, and practical usefulness. These six files show what happened in these examples, not a universal accuracy rate. Generation latency, task IDs, and request timestamps were not supplied, so we do not estimate them. The five default-voice outputs were complete, smooth, and free of reported audible defects in our listening review. The reference-audio test was different: voice characteristics transferred convincingly on two English lines, but most of the requested text disappeared. Test Duration Accuracy Delivery Practical result Baseline narration 18.20 s High; complete Natural Ready to use Everyday numbers 20.38 s High; complete Smooth Ready to use Names and technical terms 22.28 s High; complete Stable Ready with terminology review Punctuation and prosody 36.50 s High; complete Expressive Ready to use Long-form stability 86.68 s High; complete Well paced Ready to use AI-generated reference guidance 7.63 s Low; partial Smooth on two lines Regenerate or edit Observed result: Seed Audio 1.0 completed 5 of 5 default-voice scripts in this corpus. The only incomplete file was reference-guided Test 6, which omitted most of its requested text. ## Test 6: AI-Generated Reference-Audio Guidance The final test used an AI-generated Japanese song rather than a real person's recording. This was deliberately different from the clean spoken reference recommended for ordinary voice-guidance work. ## Reference audio Reference: Synthetic Japanese song · 9.85 seconds · WAV · 44.1 kHz The requested English script was: At first, the studio was quiet and controlled. Then the launch alert arrived: we had ninety seconds to respond. I slowed down, checked every signal, and said, “Stay calm. We know exactly what to do.” By the final update, the tension had passed. I took a breath and added, “The system is stable. We can stand down.” ## Generated result Output: Reference-guided voice · 7.63 seconds · WAV · 24 kHz Ratings: Text fidelity: Low · Spoken-segment quality: High · Artifacts: High · Voice consistency: High · Completeness: Partial The output began with silence and omitted most of the requested script. It spoke only “Stay calm. We know exactly what to do” and “The system is stable. We can stand down.” Those surviving lines sounded smooth in English and retained recognizable vocal characteristics from the Japanese reference, which is a striking cross-language result. But good voice similarity cannot compensate for missing most of the text. We cannot conclude that the musical reference caused the omissions from one run. What we can say is that this raw file failed as a complete narration and would need regeneration or editing. Practical verdict: Convincing voice transfer on two English lines, but a failed full-script result. Clean default-voice delivery held up across five very different scripts. The set moved from ordinary prose to numbers, difficult names, dense technical vocabulary, expressive dialogue, and a longer narrative. Every one of those outputs was complete and accurate in our listening review, with no reported clipping, robotic delivery, or disruptive artifacts. Seed Audio also preserved pacing across different inputs. The numbers in Test 2 did not break the rhythm, the dense vocabulary in Test 3 did not destabilize the voice, and the 192-word passage in Test 5 remained controlled through the ending. Test 4 showed that punctuation can produce more than pauses: its questions, quotations, and changes in tension created expressive delivery. Even the failed reference test revealed a narrower strength. The two lines that survived sounded smooth in English while retaining recognizable characteristics from a synthetic Japanese singing voice. That suggests the reference mechanism can carry vocal identity across language and delivery style, although the completeness failure prevents a broader production claim. Test 6 failed in a way that was easy to verify. The request contained 57 words, but the 7.63-second output began with silence and delivered only two quoted lines. Most of the surrounding narration disappeared. We rated text fidelity low, artifacts high, completeness partial, and the raw file weak for commercial use despite the quality of the surviving speech. This is also a warning against judging a reference-guided output by its most impressive moment. A convincing voice match can draw attention away from missing words, excess silence, or an incomplete ending. Production review needs to compare the entire file against the entire script. Tests 1–5 did not expose a comparable problem, but the corpus is intentionally small. One output per script cannot establish repeatability, failure rates, or broad multilingual reliability. Treat the absence of errors in five examples as strong observed evidence—not a guarantee. - Write dates, times, percentages, and phone numbers in the form you want spoken. - Review brand names, proper nouns, and acronyms even when the first output sounds convincing. - Use punctuation deliberately to signal questions, pauses, contrast, and urgency. - Listen through the entire generated file, including the beginning and final sentence. - Compare the output line by line with the exact source text for omissions and repetition. - For reference guidance, prefer 5–10 seconds of clean speech, as recommended in the PiAPI documentation . - Treat singing or music-backed references as experimental when script completeness matters. - Keep the first output when publishing a test, and disclose any reruns or edits. The final two reference recommendations are workflow safeguards, not a claim that music caused the Test 6 failure. We only ran one reference example. The evidence shows that a synthetic Japanese song transferred useful vocal characteristics while the associated output also omitted most of the script. For the default-voice workflows represented here, Seed Audio 1.0 produced usable raw audio. Ordinary narration, technical explainers, everyday numeric content, expressive dialogue, and an approximately 90-second voiceover all came through cleanly. Tests 1–5 did not require corrective editing in our listening review. Human review is still necessary when exact wording matters. Brand-sensitive pronunciations, legal or regulated scripts, numbers with financial consequences, and all reference-guided outputs should be checked against the source text. Test 6 demonstrates why: impressive voice transfer can coexist with a major completeness failure. This test does not establish broad multilingual reliability, statistical consistency, or behavior for every input near PiAPI's documented limits. It also does not evaluate music or sound-effect generation. For implementation steps, see how to use the Seed Audio 1.0 API . Five of the six outputs were immediately usable in our review. Seed Audio 1.0 handled difficult names, technical terminology, expressive punctuation, everyday numbers, and an 86.68-second passage with complete, natural delivery. That makes the default voice a credible option for the narration and scripted voiceover workflows represented by these tests. The reference-audio result prevents an unqualified recommendation. Although the voice transferred convincingly from synthetic Japanese singing to English speech, leading silence and major text omissions made the raw result unusable as a complete narration. Use reference guidance with full-output review and be prepared to regenerate. Ready to evaluate it with your own scripts? Open the PiAPI Seed Audio 1.0 playground and test the inputs that matter to your workflow. ## Seedream 5 Pro vs Seedream 5 Lite: Which Model Should You Use? Seedream 5 Pro vs Seedream 5 Lite compared through real prompt tests, PiAPI pricing, resolutions, and API differences. See which model fits your workflow. Seedream 5 Pro made fewer errors in our exact-text and multi-object examples. Seedream 5 Lite costs less at PiAPI's shared 2K setting and adds a 3K option plus sequential generation. Both produced polished images, and Lite followed the cabin's foreground-path instruction more closely. This Seedream 5 Pro vs Seedream 5 Lite comparison is for developers, creators, marketers, and production teams choosing a model through PiAPI. We reviewed three prompt pairs on July 14, 2026, using identical prompt text within each pair, then compared the images with PiAPI's pricing and API options. Because the exports have different dimensions, this is a first-output review rather than a controlled resolution, speed, or consistency benchmark. ## Quick verdict - Observed Pro edge: Seedream 5 Pro preserved all seven chart values and followed the complex cabinet prompt more precisely. - Documented Lite advantages: PiAPI lists Seedream 5 Lite at $0.052 per strict 2K or 3K image, compared with $0.136 for Pro at 2K. Lite also supports sequential generation. - Observed shared strengths: Both produced polished photorealistic scenes, readable typography, coherent layouts, and strong multi-subject placement. - Evidence boundary: The review used one output per model and prompt, unequal exported dimensions, no task metadata, and no latency records. ## Table of contents - Quick comparison - What the two models are - How we evaluated them - Three generation examples - Pricing and API differences - Which model to choose - Limitations and FAQ ## Seedream 5 Pro vs Seedream 5 Lite: Quick Comparison Pro made fewer exact-text and instruction errors in this small sample. Lite's clearest advantages are documented API options: a lower 2K request price, 3K output, and sequential generation. The quality rows below are observations from one supplied Pro/Lite pair per prompt. The specification rows come from PiAPI documentation reviewed on July 14, 2026. Decision factor Seedream 5 Pro Seedream 5 Lite Best fit Exact text, structured layouts, and precise prompt constraints Cost-conscious generation, 3K output, and sequential image workflows Photorealistic cabin example More restrained editorial mood; missed the wooden path Strong composition; followed the wooden-path instruction more closely Text and data example Preserved all seven day/value pairs Changed MON 38 to MON 28 in one chart label Multi-subject example Followed the requested object layout and compass direction more closely Strong layout, but the compass appeared to point northeast instead of northwest Tested speed Not measured Not measured Tested reliability One supplied output per prompt; no failure records One supplied output per prompt; no failure records Strict PiAPI sizes 1K or 2K 2K or 3K Strict PiAPI price $0.068 at 1K; $0.136 at 2K $0.052 at 2K or 3K Reference images Up to 10; first free, then $0.003 per additional reference Up to 10; no reference surcharge documented Sequential generation Not supported disabled or auto , with 1–15 images The specification and pricing rows come from the PiAPI Seedream 5 API documentation , reviewed July 14, 2026. Prices and availability can change, so verify the live documentation before budgeting a production workload. ## What Are Seedream 5 Pro and Seedream 5 Lite? Seedream 5 Pro and Seedream 5 Lite are image-generation task types in PiAPI's seedream API family. Both accept text prompts, up to 10 reference-image URLs, common aspect ratios, and JPEG/PNG/WebP output. Pro supports 1K/2K; Lite supports 2K/3K and sequential generation. ## What is Seedream 5 Pro? Seedream 5 Pro is PiAPI's seedream-5-pro image-generation task type, with strict 1K and 2K output options. PiAPI lists it at $0.068 per strict 1K image and $0.136 per strict 2K image. You can explore it in the Seedream 5 Pro playground or use the Seedream 5 Pro API examples for implementation guidance. The upstream model reference is ByteDance's official Seedream 5.0 Pro page . PiAPI-specific task types, sizes, and prices should still be verified against PiAPI's documentation because provider configurations may differ. The Pro name should not be read as proof that it wins every prompt. In our cabin example, Lite followed one important composition instruction more closely. Pro's clearest advantage in this sample appeared when the prompt demanded exact data and multiple bound attributes. ## What is Seedream 5 Lite? Seedream 5 Lite is PiAPI's seedream-5-lite image-generation task type, with strict 2K and 3K output options at $0.052 per image. It also supports sequential related-image generation of up to 15 images. The Seedream 5 Lite playground lets you test whether that lower request price meets your quality threshold. ByteDance maintains an official Seedream 5.0 Lite page for the upstream model. For requests made through PiAPI, use the provider documentation as the source of truth for supported parameters and billing. Lite supports 2K and 3K output through PiAPI and produced readable, structurally coherent results in all three examples. Its visible weaknesses were specific: one incorrect chart label and one apparent direction error in the spatial prompt. ## How We Evaluated Seedream 5 Pro vs Lite We used three prompts designed around different production needs: photorealistic editorial imagery, information-dense text rendering, and precise multi-subject control. The user generated one output per model for each prompt and supplied the six original PNG files for visual inspection. ## What was controlled—and what was not The text of each prompt was identical across its Pro and Lite pair. However, the exported files were not equivalent: all three Pro images were 1024×1024, while Lite produced 1792×2240 for the cabin and 2048×2048 for the other two examples. The cabin pair also used different aspect ratios. We therefore evaluated visible prompt adherence, layout, realism, and usability, but did not award a resolution or sharpness winner. No task IDs, request payloads, generation times, failed tasks, or credit-consumption records were available. Speed, reliability, cost per supplied image, and cross-run consistency remain untested. ## Evaluation rubric Use this same checklist when testing either model with your own prompts: Criterion What to inspect Prompt adherence Required subjects, counts, colors, directions, exclusions, and relationships Text accuracy Spelling, numbers, punctuation, units, labels, and internal consistency Visual quality Composition, lighting, coherence, material rendering, and overall finish Detail Fine structures that remain meaningful rather than noisy or invented Artifacts Broken geometry, malformed objects, duplicated elements, or corrupted text Commercial usefulness Whether the image can be used immediately, needs a small edit, or requires regeneration We use these criteria to organize concrete observations rather than reducing each image to broad labels. The analysis below names the visible success or failure that affects whether an output is usable. ## How to repeat the comparison fairly Use a controlled test before choosing a model for production: - Choose representative prompts. Test the text, subjects, layouts, and failure cases that appear in your real workload rather than relying on showcase prompts. - Match the request settings. Use the strict Pro and Lite task types, the shared 2K size, the same aspect ratio, and the same output format. Keep each paired prompt byte-for-byte identical. - Run more than once. Generate at least three images per model and prompt. Alternate which model runs first so time-of-day or service-load effects are less likely to favor one side. - Keep the evidence. Save task IDs, request payloads, timestamps, completion states, consumed credits, original URLs, and untouched output files. Record technical failures instead of silently replacing them. - Score before choosing a favorite. Mask the model labels where practical, apply one rubric to every output, and calculate usable-first-pass rate. Compare the median result rather than selecting only the strongest image from each model. This procedure separates model behavior from resolution, selection, and export differences. It also reveals whether a lower request price remains cheaper after retries and corrections. ## Example 1: Photorealism and Environmental Detail ## Prompt ``` Create a photorealistic travel-editorial photograph of a modern glass-and-timber cabin beside a still alpine lake at blue hour. The cabin has one stone chimney with a thin plume of smoke, warm amber interior lights, and a light covering of snow on the roof. Snow also rests naturally on the nearby pine branches. The lake shows a clear but slightly rippled reflection of the cabin and lights. A narrow wooden footpath leads from the foreground to the cabin. Add low mist above the far shoreline and layered mountains in the background. Render the glass, timber, stone, snow, smoke, water, reflections, and natural blue-hour lighting realistically. No people, vehicles, animals, readable text, logos, or fantasy elements. ``` ## Outputs Seedream 5 Pro: restrained blue-hour mood with stone steps in place of the requested wooden path. Seedream 5 Lite: brighter cabin composition that follows the wooden-path instruction. ## Short analysis / evaluation Both models produced convincing travel-editorial scenes with snow, warm cabin light, mist, mountains, and reflected light on the lake. Pro created the more restrained blue-hour mood, with natural-looking forest, water, and snow detail, but it changed the requested wooden footpath into stone steps. Lite followed the path instruction more closely and gave the architecture greater prominence, although its smoke plume was heavier than requested and the processing appeared cooler and brighter. Observed result: This pair is a practical tie. Pro produced the quieter editorial mood; Lite followed the foreground-path composition more closely. Because the aspect ratios differ, the pair does not support a general composition or detail winner. ## Example 2: Typography and Information-Dense Layout ## Prompt ``` Design a high-density editorial infographic for a fictional city environmental report. Use a precise Swiss-modernist grid, an ivory background, deep navy text, teal data bars, and coral-red highlights. All the following text and numerical values must appear exactly as written and remain clearly readable: CITY AIR QUALITY REPORT RIVER DISTRICT — JULY 2026 AQI 42 — GOOD PM2.5 8 µg/m³ PM10 18 µg/m³ CO₂ 418 ppm 7-DAY AQI MON 38 TUE 45 WED 41 THU 52 FRI 47 SAT 35 SUN 42 HEALTH GUIDANCE WALK OR CYCLE OPEN WINDOWS CHECK AGAIN AT 6 PM SOURCE: CITY SENSOR NETWORK UPDATED 14 JULY 2026, 09:00 Place the title and district subtitle at the top. Arrange the four current-reading values in four clearly separated summary cards. Below them, create a seven-bar chart labeled with the exact day abbreviations and values; the relative bar heights must match the numbers. Place the three health-guidance actions in a clearly separated section with simple line icons. Put the source and update time in a compact footer. Use only the supplied words and numbers. Do not invent, omit, duplicate, or rewrite any text or value. Do not add people, photographs, logos, maps, or decorative copy. ``` ## Outputs Seedream 5 Pro: readable report with all seven day and value pairs preserved. Seedream 5 Lite: polished layout, but the first chart label reads MON 28 instead of MON 38. ## Short analysis / evaluation Both models rendered a dense amount of text clearly and created a coherent hierarchy of cards, chart, guidance, and source information. Pro preserved every supplied chart value and day pairing, matched the relative bar heights, and rendered characters such as µ , ³ , and ₂ cleanly. Its small miss was the absent em dash between 42 and GOOD in the AQI card. Lite reproduced nearly all the brief and produced an attractive, readable layout. However, the first x-axis label says MON 28 even though the number above the same bar is 38 . A data graphic with contradictory values cannot be published without correction, making this the most meaningful difference across the three tests. Observed result: Pro performed better on this text-and-data prompt. Lite's output is visually polished, but its contradictory Monday values require correction. ## Example 3: Complex Prompt and Multi-Subject Control ## Prompt ``` Create a front-facing photorealistic museum display cabinet divided into exactly six equal cells in a 3-column by 2-row grid. Each cell contains exactly one object. Top-left: a transparent glass cube containing one suspended red rose. Top-center: a matte-black ceramic teapot with a polished gold handle. Top-right: a round white clock with black hands showing exactly 10:10. Bottom-left: exactly six emerald-green gemstones stacked as a small pyramid. Bottom-center: a miniature red bicycle facing left. Bottom-right: a silver compass with its needle pointing northwest. Keep all dividers straight, all objects centered, and all cells clearly separated. Use a neutral gray studio background with consistent soft lighting. No labels, no text, and no extra objects. ``` ## Outputs Seedream 5 Pro: coherent six-cell display with stronger directional adherence. Seedream 5 Lite: clean arrangement, but the compass appears to point northeast rather than northwest. ## Short analysis / evaluation Both models kept the 3×2 cabinet, placed the six requested subjects in the correct cells, showed six gemstones, and oriented the bicycle to the left. Pro produced more convincing glass, ceramic, metal, bicycle, and gemstone materials while maintaining the requested clock time. Lite created a clean catalog-style arrangement, but the compass's red needle appeared to point northeast rather than northwest. Observed result: Pro followed this multi-object prompt more precisely. Lite remains usable for a concept or catalog layout, but this version would need the compass direction corrected. ## Image Quality and Prompt-Adherence Results Pro made fewer instruction-level errors in the text/data and multi-subject pairs; the cabin pair was mixed. Lite maintained strong visual quality across all three supplied outputs. Category Pro observation Lite observation Pair-level conclusion Photorealistic environment Strong mood and realism; path material missed Strong composition; path followed, smoke too heavy Tie, with different strengths Dense text and numerical data One punctuation omission; chart values correct One incorrect chart label creates a contradiction Pro Multi-subject control Correct structure and stronger directional adherence Correct structure; apparent compass-direction error Pro Visual polish High across all three outputs High across all three outputs Tie Cross-run consistency Not tested Not tested No verdict Match the model to the cost of correction. Pro may justify its higher price for exacting prompts; for reviewed, visually led work, Lite's price and output options may offer better value. ## Speed, Reliability, and Cost We did not measure generation time or failure rate. Without timestamps or failed-task records, neither model receives a speed or reliability advantage here. PiAPI's documentation provides the following pricing comparison as of July 14, 2026: Strict task type Size Documented price per image seedream-5-pro 1K $0.068 seedream-5-pro 2K $0.136 seedream-5-lite 2K $0.052 seedream-5-lite 3K $0.052 Documented 2K price difference: Pro costs $0.084 more per image, or approximately 2.62 times Lite's listed price. For 100 strict 2K generations, that is $13.60 with Pro versus $5.20 with Lite—a difference of $8.40 before retries, reference-image charges, or workflow overhead. Those numbers describe listed API pricing, not the measured cost of the six supplied examples. Check the current Seedream 5 API documentation before publishing a budget or client quote. ## Resolution, Reference Images, and API Differences Both tiers use the same seedream API model family, but you select the tier through task_type . Use seedream-5-pro or seedream-5-lite for the strict variants. PiAPI also documents less-restriction variants with a 25% price markup; those should be treated as separate configurations rather than mixed into a quality comparison. The most relevant workflow differences are: - Resolution: Pro supports 1K and 2K. Lite supports 2K and 3K. A fair native-quality test should use the shared 2K setting. - Reference images: Both accept up to 10 public image URLs. Pro includes the first reference and lists a $0.003 charge for each additional one; PiAPI documents no Lite reference surcharge. - Sequential generation: Lite supports disabled or auto and can request 1–15 related images. Pro does not support the Lite sequential-generation fields. - Output controls: Both document the same aspect-ratio list and support JPEG, PNG, and WebP output. For implementation details, use the Seedream 5.0 API guide alongside the live PiAPI Seedream documentation . Keep task type, resolution, output format, aspect ratio, and prompt fixed when you run your own comparison. ## Which Seedream 5 Model Should You Choose? ## Choose Seedream 5 Pro if... - Your prompts contain exact numbers, units, short labels, or structured information. - Small attribute-binding mistakes create meaningful review or editing costs. - The fewer-error results from this sample match the kinds of prompts you expect to run. - You are comfortable paying the documented $0.136 per strict 2K image. Pro still needs review: its cabin output replaced the requested wooden path with stone steps. ## Choose Seedream 5 Lite if... - Your workflow prioritizes documented cost per image. - You need a documented 3K option or sequential generation of related images. - The prompt is primarily visual and allows a review or correction pass. - You found Lite's brighter, more prominent cabin composition closer to your preferred style. Lite's numerical and compass errors matter, but they do not make the model broadly unusable. They show where exactness checks are most important. ## Test both if... Test both models when one prompt combines visual polish with strict text, counts, or spatial directions. Run at least three generations per model using the same 2K resolution and aspect ratio, then compare usable-first-pass rate—not only the most attractive image. A model that costs less per request can become more expensive if it requires frequent retries or manual correction. ## Limitations of This Comparison This article evaluates a deliberately small sample. Each model contributed one output for each of three prompts, so we cannot estimate variability, consistency, retry rate, or average quality. Image models are nondeterministic, and another run may change the category result. The exported files were also not controlled. Pro files were 1024×1024; Lite files were 1792×2240 for Example 1 and 2048×2048 for Examples 2 and 3. Example 1 used different aspect ratios, which prevents a clean composition comparison. We avoided interpreting apparent sharpness as a model advantage. These results apply to the PiAPI-accessible model configurations and documentation reviewed on July 14, 2026. Model behavior, pricing, availability, and API details can change. Use this comparison to choose what to test next, not as a substitute for validating your own prompts. ## Frequently Asked Questions ### What is the difference between Seedream 5 Pro and Seedream 5 Lite? Seedream 5 Pro and Seedream 5 Lite use the same PiAPI seedream model family but different task types. Pro supports 1K/2K; Lite supports 2K/3K, lower documented pricing, and sequential generation. In the three supplied pairs, Pro made fewer exact-text and instruction errors, while both produced strong visual quality. ### Is Seedream 5 Pro always better than Seedream 5 Lite? No. Pro performed better in our text-and-data and multi-subject examples, but Lite followed the cabin's wooden-path instruction more closely. Results depend on the prompt, settings, and run. Pro's name and price do not guarantee a better image, so test representative prompts before choosing a default model. ### Should I use Seedream 5 Pro or Lite? Use Pro when exact numbers, text, counts, directions, or object relationships are expensive to correct. Use Lite when documented cost, 3K output, or sequential generation matters more and you can review the result. If your workload mixes those needs, test both at the same 2K resolution and aspect ratio. ### Which Seedream 5 model is cheaper through PiAPI? Seedream 5 Lite is cheaper at the shared strict 2K setting in PiAPI's July 2026 documentation. Lite is listed at $0.052 per 2K image, while Pro is $0.136. That makes Pro approximately 2.62 times the Lite price at 2K, before retries or any additional reference-image charges. ### What resolutions do Seedream 5 Pro and Lite support through PiAPI? PiAPI documents 1K and 2K output sizes for Seedream 5 Pro, and 2K and 3K for Seedream 5 Lite. The shared resolution is 2K, making it the appropriate setting for a controlled model comparison. Actual pixel dimensions can vary with aspect ratio, so retain the original output metadata. ### Can I use Seedream 5 Pro and Lite through the same API? Yes. Both use PiAPI's seedream model family and POST /api/v1/task endpoint. Select seedream-5-pro or seedream-5-lite with task_type for strict generation. Their inputs overlap, but size options, sequential-generation behavior, pricing, and reference-image charges differ, so review those fields before switching task types. ## Final Verdict: Seedream 5 Pro or Seedream 5 Lite? In this Seedream 5 Pro vs Seedream 5 Lite comparison, Pro made fewer errors on exact typography, numerical data, and tightly constrained multi-object prompts. Lite combines lower documented 2K pricing with a 3K option, sequential generation, and competitive visual quality across the three examples. There is no universal winner. The best model is the one that produces usable outputs at the lowest total workflow cost after review, corrections, and retries. Take one prompt that represents your real workload, run it through both the Seedream 5 Pro playground and Seedream 5 Lite playground under the same 2K settings, then compare exactness as carefully as appearance. If your decision extends beyond the Seedream family, the Seedream 5 vs Nano Banana 2 comparison covers a separate cross-model choice. When you are ready to integrate the selected model, verify the latest parameters and pricing in the Seedream 5 API documentation and create your key in the PiAPI workspace . ## Seedream 5 Pro API Examples: Multi-Reference Product Images, Text Rendering, and Precision Editing See Seedream 5 Pro API examples for multi-reference product images, accurate short text, and precision editing, with prompts, outputs, and a live playground. Seedream 5 Pro is PiAPI's premium Seedream 5 image-generation tier for high-quality text-to-image and reference-guided workflows. This guide documents three practical API examples: combining multiple references into a product hero image, rendering short marketing text, and changing one visual detail while preserving the rest of an image. The goal is not to claim perfect control from a single prompt. Each section shows the inputs, planned request settings, supplied output, and observed trade-offs so developers can decide whether Seedream 5.0 Pro fits an ecommerce or creative-production workflow. Direct answer In the supplied tests, Seedream 5 Pro combined three reference roles into one product image, rendered a correct two-line English headline, and created a controlled cap-color variation. These are observed results from the supplied assets, not guarantees for every prompt or request. Evidence note The visual evaluations below are based on the supplied PNG files. Exact task payloads, task IDs, final API responses, and confirmed output settings were not available at review time, so this article does not present the displayed assets as authenticated task records. In this guide - Multi-reference product images - Text rendering - Precision editing - Production workflow - FAQ - Mini playground ## Results at a Glance Workflow Visible result Best fit Important caveat Multi-reference product image Product identity, material detail, and campaign direction formed one coherent hero image. Ecommerce hero images and campaign variants. Identity retention is an observed result, not a guarantee. Text rendering The two-line headline used the correct spelling, capitalization, and line order. Short campaign headlines and poster concepts. Confirm the actual aspect ratio and manually review all production copy. Precision editing The cap assembly changed from silver to deep red while the bottle and scene stayed highly consistent. Controlled color variants of an approved asset. The dropper bulb turned red too. ## Test Method and Evidence This article uses a simple fictional skincare bottle so the examples test reference guidance and editing behavior without depending on a third-party brand. The multi-reference workflow assigns three separate roles—product identity, material finish, and campaign art direction—then checks whether those signals remain distinct in one final image. The six supplied source and output assets were reviewed on July 13, 2026. Every supplied file is a 1312 × 736 PNG. The article records only what is visibly present, including the text-format mismatch: the supplied text-rendering output is landscape even though the planned request called for a 4:5 vertical image. Claim type Evidence used Publication boundary API identifiers, inputs, ratios, formats, sizes, and pricing PiAPI Seedream 5 documentation, reviewed July 13, 2026. Recheck the docs before implementation because product details can change. Visual behavior in these examples Six supplied source/output PNG files and the visible comparisons below. Describe only these assets; do not turn one result into a universal guarantee. Task delivery and response handling No authenticated task record was available. Do not publish polling, timing, or final-response-path instructions until a real response is captured. ## What Does the Seedream 5 Pro API Support? PiAPI's Seedream API uses seedream as the model identifier. The Pro tier uses seedream-5-pro for strict moderation or seedream-5-pro-less-restriction for the less-restriction variant. Pro supports 1K and 2K output sizes, up to ten optional public reference-image URLs, and common aspect ratios from square through 21:9 . The examples in this tutorial use the strict seedream-5-pro task type. The first Pro reference image is included in the image price, while each additional reference has a separate charge. Check the current Seedream 5 API documentation before using these values in production. If your workflow prioritizes 3K output instead of Pro's 1K and 2K options, review the Seedream 5 Lite API before choosing a tier. For a prompt-by-prompt quality and cost review, see the Seedream 5 Pro vs Seedream 5 Lite comparison . ## Create a Seedream 5 Pro Task A Pro request accepts a prompt, optional public image URLs, an aspect ratio, output format, and output size. The following example illustrates the documented input shape for the multi-reference workflow; it is not the missing authenticated payload for the supplied output. ``` curl -X POST 'https://api.piapi.ai/api/v1/task' \ -H 'X-API-Key: YOUR_API_KEY' \ -H 'Content-Type: application/json' \ -d '{ "model": "seedream", "task_type": "seedream-5-pro", "input": { "prompt": "Create a premium ecommerce hero image using the supplied reference images...", "image_urls": [ "https://your-public-cdn.example/product-reference.webp", "https://your-public-cdn.example/material-reference.webp", "https://your-public-cdn.example/campaign-reference.webp" ], "aspect_ratio": "16:9", "output_format": "webp", "size": "2K" } }' ``` Response handling still needs live verification The current documentation describes synchronous delivery, while a visible example also shows a pending task response. Do not copy a polling sequence or assume an output path until an authenticated Pro response confirms the current behavior. ## Example 1: Multi-Reference Product Images Product teams often have separate references for the physical product, its material finish, and campaign art direction. This example uses three images to create one new ecommerce hero image. What can Seedream 5 Pro do with reference images? In this supplied test, three references contributed distinct product-identity, material-finish, and campaign-direction cues to one coherent hero image. The result shows a useful multi-reference workflow, but it does not prove that every request will preserve identity equally well. ## Input references 1. Product identity: cobalt-blue bottle, silver dropper cap, copper ring, and blank cream label. 2. Material detail: frosted glass, brushed metal, and polished copper. 3. Campaign direction: travertine pedestal, warm light, and palm-leaf shadows. ## Prompt Create a premium 16:9 ecommerce hero image using the three supplied reference images. Treat reference image 1 as the exact product-design reference: preserve the tall frosted cobalt-blue serum bottle, brushed-silver dropper cap, thin copper ring, and blank cream label. Use reference image 2 only for material and finish. Use reference image 3 only for campaign art direction. Show one bottle, slightly right of center, with clean negative space. Do not add readable text, logos, extra bottles, people, hands, flowers, or unrelated objects. ## Planned request settings ``` task_type: seedream-5-pro aspect_ratio: 16:9 size: 2K output_format: webp reference images: 3 ``` Supplied multi-reference output. The file is 1312 × 736; the actual task settings still need confirmation. ## Evaluation The output combines the product identity, material cues, and campaign direction into a coherent ecommerce composition. The cobalt-blue bottle, silver cap, copper ring, and blank cream label remain recognizable, while the stone pedestal, warm sand background, and palm-leaf shadows follow the campaign reference. The one-product composition also holds: the bottle is fully visible, slightly right of center, and placed beside useful negative space. No readable text, extra bottles, people, hands, logos, or unrelated props are visible. Working verdict: Strong multi-reference product-image result. It demonstrates a practical way to blend product identity, material treatment, and campaign direction in one request, subject to normal brand review. ## Example 2: Seedream 5 Pro Text Rendering For marketing visuals, the practical question is whether an image model can render short, controlled copy that a human can verify and use as a candidate campaign asset. Can Seedream 5 Pro render text in images? In this supplied test, it rendered SUMMER DROP and LIMITED EDITION with correct spelling, capitalization, and line order. The result supports short-copy experimentation, but production use still requires manual copy review and confirmation of the actual task settings. ## Prompt Create a premium vertical 4:5 skincare launch poster with a single fictional cobalt-blue serum bottle on a travertine pedestal. Render exactly two uppercase lines in the upper third: SUMMER DROP and LIMITED EDITION. Use modern sans-serif typography, generous negative space, and no other readable words, logos, watermarks, badges, or numbers. ## Planned request settings ``` task_type: seedream-5-pro aspect_ratio: 4:5 size: 2K output_format: png reference images: none ``` Supplied full output. It is 1312 × 736 and landscape, not the planned 4:5 format. 672 × 160 pixel-for-pixel crop with no resizing or generative alteration. ## Evaluation The output contains both requested lines with correct spelling, uppercase styling, and the intended order. The lettering is readable against the warm background, with no visible extra words, badges, logos, or watermarks. The format still needs verification: the supplied file is landscape rather than the planned 4:5 vertical canvas. Confirm the task settings or export behavior before describing this displayed asset as a 4:5 or 2K result. Production tip: Keep generated copy short, specify casing and line breaks, reserve negative space, generate multiple candidates, and manually verify every final character before publishing. ## Example 3: Precision Editing Image editing is most useful when a team has an approved composition and needs one controlled variation. This test uses Example 1 as the reference and requests a single cap-color change. Can Seedream 5 Pro make a targeted image edit? In this supplied before/after test, the cap assembly changed from silver to deep red while the bottle, label, pedestal, crop, lighting, and background stayed highly consistent. The dropper bulb also turned red, so the edit was controlled but not limited to the metal cap alone. ## Prompt Edit the supplied product image. Keep the bottle shape, cobalt-blue frosted glass, copper ring, blank cream label, camera angle, crop, pedestal, background, palm-leaf shadows, lighting direction, contact shadow, and overall composition unchanged. Change only the brushed-silver dropper cap to matte deep red. Do not add text, logos, extra products, people, or other objects. Before: silver cap assembly. After: deep-red cap assembly, including the dropper bulb. ## Preservation check Check Observed result Cap color Pass—the silver cap and dropper assembly are visibly deep red. Bottle and glass color Pass—the cobalt-blue frosted bottle remains recognizable. Blank label Pass—the label remains in the same general position and form. Camera angle and crop Pass—bottle placement and scene framing closely match. Background and lighting Pass—the sand, pedestal, and palm-shadow setup remain consistent. Edit isolation Partial—the red treatment extends to the dropper bulb, not only the metal cap. Working verdict: Strong precision-edit result. It demonstrates a useful color-variant workflow while showing why a human must review the exact component scope. ## Prompt Patterns for More Controlled Outputs - Assign each reference a role. State whether an image supplies product identity, material detail, or scene direction. - List what must not change. Name the product shape, label, angle, crop, and lighting when they matter. - Describe only the requested edit. Avoid combining unrelated changes. - State exclusions. Rule out unwanted text, logos, people, extra products, and clutter. - Review every candidate. Treat generated assets as candidates, not deterministic source files. ## What Should a Production Seedream API Workflow Store? A production workflow should store the source URLs, prompt, task type, input settings, task ID, complete response, generation date, output URL, approval decision, and visible limitations for every selected asset. This creates a reproducible record for review, variants, and troubleshooting. ``` source image URLs prompt task type and input settings task ID and complete response generation date output URL human approval decision visible artifacts or brand deviations ``` Generate multiple candidates, route them through visual and brand review, and preserve the approved prompt and inputs with the final asset. If you are still selecting a model, see the Seedream 5 vs Nano Banana 2 comparison for additional product, layout, API, and pricing context. ## Seedream 5 Pro API FAQ ## How many reference images can I send to the Seedream 5 Pro API? PiAPI's Seedream 5 documentation allows up to ten public reference-image URLs. This article's multi-reference example uses three images with distinct roles: product identity, material detail, and campaign art direction. ## Can Seedream 5 Pro render text in images? In the supplied test, Seedream 5 Pro rendered the requested two-line uppercase copy— SUMMER DROP and LIMITED EDITION —with correct spelling and line order. Keep copy short, then manually verify the final lettering and intended aspect ratio before publishing a campaign asset. ## Can Seedream 5 Pro make a targeted image edit? In the supplied test, the editing workflow changed the silver cap assembly to deep red while keeping the blue bottle, blank label, pedestal, sand background, and palm-shadow composition highly consistent. The red extended to the dropper bulb too, so review whether the model changed the exact component intended. ## Which Seedream 5 Pro output sizes are available? The Pro task types support 1K and 2K output sizes. The planned test setting was 2K, but the supplied PNG exports are 1312 × 736, so confirm the actual task settings before describing any displayed example as a 2K result. ## Try Seedream 5 Pro Through PiAPI Start by testing the model in the Seedream 5 Pro playground , then use the Seedream API documentation to confirm the latest request schema and pricing. When you are ready to connect requests, create or retrieve your API key in the PiAPI workspace . These examples suggest Seedream 5 Pro is especially useful when a workflow needs to combine a product reference with campaign direction or create a controlled color variation of an approved asset. The short-text result is promising for concise headlines, but it still needs manual copy review and confirmed output settings before it becomes a production claim. ## How to Use Seed Audio 1.0 API for AI Voice Generation Learn how to use Seed Audio 1.0 API through PiAPI: create tasks, poll results, add voice references, understand pricing, and test the generator. PiAPI PiAPI July 10, 2026 Seed Audio 1.0 API through PiAPI lets you create an asynchronous speech-generation task with model: byteaudio and task_type: seed-audio-1.0 , then poll the task until it returns an audio URL. It supports text input, optional voice references, output controls, and per-second billing for AI voice generation workflows. This guide walks through the practical API flow: what you need before you start, how to create a task, how to poll the result, how to use voice references, and how to estimate pricing. If you want to test the workflow before writing code, open the Seed Audio 1.0 API playground and try a script in PiAPI Signal Studio first. ## In this guide To use Seed Audio 1.0 API through PiAPI, create a task at POST https://api.piapi.ai/api/v1/task with your X-API-Key , model: byteaudio , task_type: seed-audio-1.0 , and a text input. PiAPI returns a task ID. Poll GET /api/v1/task/{task_id} until the task is completed , then use output.audio_url to play or download the generated audio. Seed Audio 1.0 through PiAPI is an asynchronous BytePlus speech-generation API for turning text into spoken audio. Developers create a PiAPI task with model: byteaudio and task_type: seed-audio-1.0 , then poll the task until PiAPI returns an audio URL. The important word is speech. PiAPI's current Seed Audio 1.0 workflow is for spoken audio from text, with optional reference audio or image guidance. If your project needs music generation, use ACE-Step; if you need video-matched sound effects or action-timed audio, use the MMAudio API instead of treating Seed Audio 1.0 as a full sound-scene generator. These are the core Seed Audio 1.0 API facts to keep nearby while building. API details were checked against the PiAPI Seed Audio documentation on July 10, 2026. Model byteaudio Task type seed-audio-1.0 Processing Asynchronous Create task POST /api/v1/task Get task GET /api/v1/task/{task_id} Successful result output.audio_url Default output WAV, 24 kHz Text limit 2,048 Unicode characters Maximum output duration 120 seconds Audio references Up to three Image references Up to one Pricing unit $0.002 per successful output second - A PiAPI account and API key. - A short script to synthesize. - Output settings, such as format and sample rate. - Optional reference audio or image guidance if you want the result to follow a specific voice or style. Seed Audio 1.0 through PiAPI is not a streaming API. You submit a task, wait for it to process, then retrieve the generated audio URL when the task is complete. The core request uses PiAPI's standard task endpoint. This is the only full cURL example in the guide, so treat it as the base pattern. curl --request POST \ --url https://api.piapi.ai/api/v1/task \ --header "Content-Type: application/json" \ --header "X-API-Key: YOUR_API_KEY" \ --data '{ "model": "byteaudio", "task_type": "seed-audio-1.0", "input": { "text": "Hello from PiAPI. This is a Seed Audio 1.0 test.", "format": "mp3", "sample_rate": 24000 } }' Keep your API key on the server side. Do not expose it in frontend code, public repositories, browser console snippets, or client-side apps. Seed Audio 1.0 API tasks are asynchronous. Instead of receiving the final audio immediately, your app should poll the get-task endpoint until the task reaches a terminal status. - Submit the task. - Store the returned task ID. - Poll GET /api/v1/task/{task_id} every few seconds. - Continue while the task is staged , pending , or processing . - Stop when the task is completed or failed . - On success, read output.audio_url and output.duration . You do not need a different API request for every use case. In most cases, the important part is the script, the selected settings, and whether you add references. Seed Audio 1.0 can use reference inputs when you want the generated speech to follow a voice or style instead of relying on a default voice. According to PiAPI's Seed Audio documentation, one request can include up to three audio references. - Use up to three audio references per request. - Assign them with @Audio1 , @Audio2 , and @Audio3 . - Use one image reference only when using image guidance. - Do not mix audio and image references in one request. - Keep reference files up to 10 MB. - Keep URL audio references public HTTPS and 30 seconds or shorter. Setting Supported values Default Format WAV, MP3, PCM, OGG Opus WAV Sample rate 8000, 16000, 24000, 32000, 44100, 48000 24000 Speech rate -50 to 100 0 Pitch rate -12 to 12 0 Loudness rate -50 to 100 0 PiAPI lists Seed Audio 1.0 pricing by successful output duration: $0.002 per successful output second, which is approximately $0.12 per generated minute. Output duration Estimated cost 10 seconds $0.02 30 seconds $0.06 60 seconds $0.12 120 seconds $0.24 For the latest product details, check the Seed Audio 1.0 API pricing and limits section on the product page before launching production usage. Seed Audio 1.0 can behave like a text-to-speech API for simple scripts, but PiAPI exposes more controls than a basic preset-voice TTS workflow. If you are comparing reference-audio speech models, keep F5-TTS API open as a related option. - Reference audio: up to three audio references. - Multi-speaker script markers: @Audio1 , @Audio2 , and @Audio3 . - Image guidance: one image reference, separate from audio references. - Output controls: format, sample rate, speech rate, pitch, and loudness. - Processing model: async task submission and polling. If you are still testing scripts, voices, or formats, you do not need to start with code. Open the Seed Audio 1.0 generator in PiAPI Signal Studio, paste one of the example scripts above, choose the output settings, and listen to the result. - The task is asynchronous, not streaming. - One request can generate up to 120 seconds of audio. - Text input is limited to 2,048 Unicode characters. - Returned audio URLs are temporary, so save results you need to keep. - Audio references and image references cannot be mixed in the same request. - URL audio references must be public HTTPS files and no more than 30 seconds. - PiAPI's current Seed Audio endpoint should not be described as a music, ambience, or full sound-scene generator unless that behavior is separately documented and tested. Most failed tests come from input shape, reference handling, account state, or output handling rather than the script idea itself. - Empty or failed task: confirm input.text is present and moderation-safe. - Reference ignored: use the documented input.references shape and public HTTPS URLs. - Upload rejected: keep reference files under 10 MB and use supported formats. - Audio URL expired: download or persist the result soon after completion. - Unexpected voice: match @AudioN markers to reference order. - Insufficient credits: add credits or shorten the test output. If you still cannot identify the issue, contact the PiAPI team at support@piapi.ai with the task ID, request time, error message, and a short description of what you were trying to generate. Do not send API keys or private reference files over email. Now you know how to use Seed Audio 1.0 API through PiAPI: create a task, poll the result, download the audio, and add reference voices when the workflow needs more control. For a fast first pass, test your script in the Seed Audio 1.0 API playground . For production integration, use the PiAPI Seed Audio documentation to confirm the latest request fields, limits, and response shape. If you want evidence before choosing a script, you can also hear Seed Audio 1.0 handle difficult scripts across names, numbers, punctuation, long narration, and one transparently reported reference-audio failure. Disclaimer: Use reference voices only when you have the rights or consent needed to use that voice, and review the applicable PiAPI, BytePlus, content, and jurisdictional terms before publishing or monetizing generated audio. ## AI Video Ad Generator: How to Make Product Ads for TikTok, Reels, YouTube, and E-commerce Learn how to create AI product video ads from images for TikTok Shop, Instagram Reels, YouTube Shorts, Amazon, Etsy, eBay, and other ecommerce platforms. PiAPI PiAPI An AI video ad generator helps ecommerce sellers turn product assets into short video ads without planning a full shoot. Instead of starting with cameras, editors, and a production team, you can start with a product image, a short script, an optional logo, and a clear idea of where the ad will run. For product sellers, the best workflow is simple: prepare your product image, write one focused benefit, choose the right format for the platform, generate a draft, then review the final video for product accuracy, readability, and claims. Quick answer: An AI video ad generator turns product assets such as images, scripts, logos, and optional actor references into short video ad drafts. For ecommerce sellers, the strongest workflow is to prepare one clear product benefit, choose the platform format, generate a draft, then review the final ad for product accuracy, claims, and mobile readability. If you want to test the workflow directly, PiAPI's AI advertisement video generator lets you upload a product image, add an optional actor or logo, write a short script, and generate a product ad video. An AI video ad generator is a tool that uses generative AI to create short advertising videos from inputs such as product images, scripts, logos, product details, and platform preferences. It is different from a traditional video editor because it can help build the first version of the ad instead of only editing footage you already filmed. Definition: An AI video ad generator creates short ad drafts from product assets and instructions. In an ecommerce workflow, those inputs usually include a product image, a short script, a call to action, a target platform, and optional brand or actor references. For ecommerce sellers, this matters because product video is often the hardest creative asset to produce at scale. A seller may already have product photos, listing copy, customer benefits, and a logo, but not enough time or budget to film new ad creatives for every product and platform. An AI video ad generator can help turn those existing assets into video drafts for testing. You can use it to explore different hooks, product benefits, visual styles, and ad formats before investing in a larger production workflow. That does not mean AI removes the need for judgment. The seller still needs to check whether the product is shown correctly, whether the ad script is truthful, whether text is readable on mobile, and whether the final video matches the platform where it will run. Most AI product video ad workflows follow the same basic pattern: choose the product, prepare the assets, write the message, generate a draft, and review the output. - Choose the product and campaign goal. - Prepare a clear product image. - Write the product benefit, offer, and call to action. - Add an optional logo or actor reference if the ad needs branding or a presenter-style look. - Choose the aspect ratio and duration. - Generate the first ad draft. - Review the video for accuracy, claims, readability, and platform fit. - Adapt the best version for TikTok, Instagram, YouTube, or ecommerce placements. The important part is that the same product should not always use the same ad. A TikTok product ad may need a faster hook and a more casual style. An Instagram ad may need stronger lifestyle visuals. A YouTube ad can usually explain the product with a little more structure. PiAPI's AI Advertisement Video Generator is built around this type of workflow: product image first, optional actor or logo, a written ad script, and a short video format suited for testing product creative. If you want more background on AI marketing video workflows, the Seedance 2.0 AI marketing video guide explains how AI video generation can fit into broader ad and ecommerce creative production. Best-practice workflow: Start with one product, one buyer problem, one benefit, and one platform. Generate the first ad draft, review it for accuracy, then adapt the script and format for the next channel instead of forcing one video to work everywhere. The quality of the output depends heavily on the quality of the input. Before using an AI video ad generator, prepare the core assets and message so the model has a clear direction. Asset Why it matters Product image Anchors the visual output and helps the ad stay product-focused. Script Controls the message, benefit, and CTA. Logo Helps brand the ending or CTA frame. Actor reference Useful for presenter-style, creator-style, or UGC-inspired ads. Platform target Determines aspect ratio, pacing, and framing. Product benefit Keeps the ad focused on one reason to care. Proof or detail Adds credibility through a feature, material, use case, or demonstration. Use a product image where the item is easy to identify. Avoid images that are blurry, too dark, heavily cropped, or cluttered with unrelated objects. If the product has packaging, texture, buttons, labels, or a special shape, make sure those details are visible. For the script, keep it short. A good AI product video ad does not need to explain every feature. It needs one clear idea that can be understood quickly on a small screen. If you are comparing model options for broader video generation work, you can also review the Seedance 2.0 page. TikTok product ads need to earn attention quickly. The first few seconds matter, so the ad should show the product early and open with a clear problem, desire, or visual moment. For TikTok, use a vertical 9:16 format and keep the script direct. A useful structure is: - Call out the problem. - Show the product. - Explain one benefit. - End with a simple CTA. This format works especially well for TikTok Shop products, impulse-buy items, accessories, beauty products, small gadgets, home goods, and products that can be understood visually. Avoid making the TikTok version too polished if the product would work better with a creator-style or UGC-inspired feel. Many TikTok ads perform better when they look native to the feed rather than like a traditional commercial. Instagram product ads depend heavily on visual polish. Reels can behave like short-form video, while feed-style posts often need a cleaner product or lifestyle composition. For Instagram Reels, keep the pacing quick but slightly more polished than TikTok. Show the product in a lifestyle context, use readable captions, and avoid packing too much copy into the frame. A beauty, fashion, wellness, home, or gift product may work well with a softer visual style and a less aggressive CTA. For Instagram feed-style ads, consider square or vertical formats where the product remains centered and easy to understand. The message should be simple enough to read without sound. - Open with a visual mood or product moment. - Show the benefit. - Add one sensory or lifestyle detail. - End with a soft CTA. This is also where branding can help. If you have a logo, use it near the end rather than covering the product throughout the entire ad. YouTube gives you more room to explain the product, especially if the ad is closer to 10 or 15 seconds. The video still needs a strong opening, but it can use a clearer problem-solution-proof structure than a TikTok ad. For YouTube Shorts, use a vertical format and keep the rhythm close to short-form social video. For a standard YouTube ad, use a horizontal 16:9 format and make sure the product, benefit, and CTA are visible even if the viewer is watching on a smaller screen. - State the problem. - Introduce the product as the solution. - Add one proof point, detail, or use case. - End with a clear CTA. YouTube is a good fit for products that need a little more explanation: organizers, tools, kitchen products, apps, SaaS products, fitness accessories, or anything where the value becomes clearer through a short demonstration. Even if the main examples focus on TikTok, Instagram, and YouTube, the same product ad workflow can support broader ecommerce campaigns. The key is to adapt the message for the buyer's context. For Amazon, product clarity matters. The video should show what the product is, what problem it solves, and why the shopper should trust it. Avoid unsupported claims and keep the product easy to identify. For Etsy, the video can lean into material, handmade details, personalization, scale, packaging, or gift use. Etsy shoppers often care about texture, story, and uniqueness, so the ad can feel warmer and less like a hard performance ad. For eBay, the video can focus on condition, size, features, or practical demonstration. This is useful for refurbished products, collectibles, used items, and products where buyers want to understand the exact item before purchasing. Always check the current rules of the platform where you plan to upload or run the video. AI can help create the ad draft, but the seller is responsible for final claims, compliance, and product accuracy. Ecommerce rule: Treat an AI-generated ad as a draft, not a finished claim. The seller should confirm the product visuals, features, benefits, price language, and platform requirements before using the video in a listing or campaign. A strong product ad script does not need to be complicated. ``` Hook: [Problem, desire, or attention-grabbing product moment] Benefit: [What the product helps with] Proof/detail: [Feature, material, use case, review-style detail, or demonstration] CTA: [Shop now / Try it / See the product / Get yours today] ``` ``` Tired of messy cables on your desk? This compact organizer keeps chargers, earbuds, and adapters in one pouch. Soft dividers protect small accessories while the slim shape fits in a backpack. Pack your tech faster before your next trip. ``` When you use this script with an AI advertisement video generator, keep the message specific. "This product saves time" is weaker than "pack your chargers in one pouch before your next trip." The more concrete the benefit, the easier it is to create a useful ad. The biggest mistake is asking AI to fix an unclear product message. If the image is weak and the script is vague, the generated ad will usually feel vague too. Also avoid making the ad too long for the platform. A viewer on TikTok or Reels may only give you a few seconds. If the first visual does not show the product or the script takes too long to reach the point, the ad may lose attention before the benefit appears. PiAPI's AI Advertisement Video Generator is designed for product ad testing. You can upload a product image, add an optional actor image or logo, write a short ad script, choose a format, and generate a product ad video draft. The best way to start is with one clear product image and one short script. Generate a first draft, review it, then adjust the script or format for the next platform. For a model-specific API example, see how to create product ads with the Seedance 2.5 API . If you are planning repeated ad tests, check the current PiAPI pricing before building a larger creative workflow, or contact the PiAPI team at support@piapi.ai to discuss pricing questions. ## クレイアニメとは？AIでクレイアニメ風画像・動画を作る方法 クレイアニメの意味や粘土アニメとの違い、AIで画像や動画をクレイアニメ風に変換する方法を解説。PiAPIで写真・動画を粘土風コンテンツにできます。 クレイアニメは、粘土で作られたキャラクターや物体を少しずつ動かして撮影する、手作り感のあるアニメーション表現です。最近はクレイアニメAIを使って、写真を粘土アニメ風の画像に変換したり、動画をクレイアニメ風動画に変換したりする方法も広がっています。 この記事では、クレイアニメの基本、粘土アニメとの違い、AIで画像・動画をクレイ風に変換する方法、素材をアップロードするときの考え方まで解説します。 クイック回答: クレイアニメとは、粘土で作ったキャラクターや物体を少しずつ動かして撮影し、連続した映像に見せるストップモーション表現です。AIを使うと、写真や動画に粘土の質感、手作り感、コマ撮り風の雰囲気を加えて、クレイアニメ風に変換できます。 この記事での定義: クレイアニメAIとは、写真や動画などの素材をAIで解析し、粘土で作られたような質感、ミニチュア感、ストップモーション風の雰囲気を加えて変換する画像・動画生成ワークフローです。 ## 要点まとめ - クレイアニメは、粘土素材の手作り感とコマ撮りの動きが特徴です。 - 粘土アニメはクレイアニメと近い意味で使われることが多い言葉です。 - クレイアニメAIでは、写真や動画に粘土風の質感、ミニチュア感、ストップモーション風の雰囲気を加えられます。 - PiAPIのClaymation AI Generatorでは、画像をクレイ風画像に変換し、動画をクレイ風動画に変換できます。 - SNS動画、広告、キャラクター案、絵コンテ、商品ビジュアルなどに活用しやすい表現です。 ## 用語の早見表 用語 意味 この記事での使い方 クレイアニメ 粘土素材を使ったストップモーション表現 伝統的な制作方法や映像表現を説明するときに使います。 粘土アニメ 粘土で作るアニメーションの日本語表現 クレイアニメと近い意味の検索語として扱います。 クレイアニメAI AIで写真や動画を粘土アニメ風に変換する方法 画像-to-クレイ画像、動画-to-クレイ動画の文脈で使います。 Claymation AI Generator PiAPIが提供するクレイアニメ風変換ツール 画像や動画をアップロードしてクレイ風に変換する導線として紹介します。 ## クレイアニメ風画像・動画をすぐに試す 写真や動画をクレイアニメ風に変換したい場合は、PiAPIの Claymation AI Generator で試せます。 画像をアップロードすれば、人物、商品、キャラクター、背景などを粘土風の画像に変換できます。動画を使えば、既存の映像にクレイアニメ風の質感や手作り感を加えた動画表現を作れます。 まずは「すでにある写真や動画を、クレイアニメ風に変換する」用途から考えると、AIクレイアニメーション効果を使う目的がはっきりします。 ## クレイアニメとは？ クレイアニメとは、粘土で作ったキャラクターや物体を少しずつ動かし、1コマずつ撮影して動いているように見せるアニメーション手法です。英語では「clay animation」や「claymation」と呼ばれ、ストップモーションの一種として扱われます。 一般的なアニメーションが絵やCGで動きを作るのに対して、クレイアニメは実物の粘土素材を撮影します。そのため、表面のへこみ、指で作ったような質感、少し不完全な形などが魅力になります。 この手作り感は、デジタル映像が増えた今でも強い個性になります。広告、短編映像、子ども向けコンテンツ、キャラクター表現などで、温かく親しみやすい印象を出したいときに使われます。 ## クレイアニメと粘土アニメの違い クレイアニメと粘土アニメは、ほとんど同じ意味で使われることが多い言葉です。どちらも粘土で作ったキャラクターや物体を動かすアニメーションを指します。 ただし検索や記事の文脈では、少しニュアンスが変わることがあります。この記事では、従来の手作りアニメーションを説明するときは「クレイアニメ」や「粘土アニメ」を使い、AIで画像や動画を粘土風に変換する文脈では「クレイアニメAI」を使います。 ## クレイアニメ風動画・画像が人気の理由 クレイアニメ風の画像や動画が人気なのは、見た瞬間に「手で作られた感じ」が伝わるからです。粘土の柔らかい質感、丸みのある形、ミニチュアのような世界観は、普通の写真やCGとは違う印象を作れます。 特にSNSや広告では、最初の数秒で目を止めてもらうことが重要です。クレイアニメ風動画は、リアルすぎないのに存在感があり、商品紹介やキャラクター演出にも使いやすい表現です。 また、粘土アニメの雰囲気には少し懐かしい印象があります。完璧すぎない形や、コマ撮りのような動きが、親しみやすさやユーモアを生みます。 - 商品をミニチュアの粘土モデルのように見せる広告 - キャラクターやマスコットのビジュアル案 - TikTok、Instagram Reels、YouTube Shorts向けの短い動画 - 絵コンテやコンセプトムービー - イベント告知やキャンペーン用の目立つビジュアル ## クレイアニメAIとは？ クレイアニメAIとは、AIを使って写真や動画をクレイアニメ風に変換する方法やツールのことです。AIが実際に粘土をこねるわけではありません。入力された画像や動画をもとに、粘土の質感、手作り感、ミニチュア感、ストップモーション風の雰囲気を再現します。 クレイアニメーションAIを使うと、従来なら造形、撮影、照明、編集が必要だった表現を、より短い時間で試せます。もちろん、本物の粘土作品ならではの物理的な魅力とは違いますが、アイデア出しやSNS用コンテンツ制作には便利です。 できること / できないこと: クレイアニメAIは、写真や動画を粘土アニメ風の見た目に変換するための方法です。実際の粘土造形や手作業の撮影工程を置き換えるものではなく、既存素材にクレイ風の視覚効果を加えるための制作補助として使うのが適しています。 ## AIで画像・動画をクレイアニメ風に変換する2つの方法 クレイアニメAIを使うときは、最初から新しい映像を作るよりも、すでにある画像や動画を変換するほうが目的を決めやすくなります。 AIでクレイアニメ風コンテンツを作る基本手順は、次の3ステップです。 - 変換したい写真、画像、または動画を選びます。 - 画像-to-クレイ画像、または動画-to-クレイ動画の変換モードを使います。 - 必要に応じて、動画の動きだけを短く補足して結果を生成します。 ## 写真や画像をクレイアニメ風に変換する 1つ目は、写真や画像をクレイアニメ風画像に変換する方法です。人物写真、商品画像、ペット、キャラクター案、背景イラストなどを入力し、粘土で作られたような見た目に変換します。 この方法は、静止画のビジュアルを作りたいときに向いています。広告バナー、SNS投稿、サムネイル、プロフィール用のユニークな画像、商品コンセプト画像などに使えます。 画像を変換するときは、元画像の構図が重要です。被写体がはっきり写っていて、背景が複雑すぎない画像のほうが、クレイ風の質感をきれいに出しやすくなります。 ## 動画をクレイアニメ風に変換する 2つ目は、動画をクレイアニメ風動画に変換する方法です。既存の動画を使い、人物の動き、商品紹介、短いシーン、ダンス、カメラワークなどを粘土アニメ風の見た目に変換します。 この方法は、SNS動画や広告素材に向いています。通常の動画をそのまま出すよりも、粘土風の質感を加えることで、柔らかく楽しい印象になります。 動画を変換するときは、短めのクリップから始めるのがおすすめです。被写体が大きく動きすぎる映像や、細かい要素が多すぎる映像よりも、主役がわかりやすい動画のほうがクレイアニメ風の効果を確認しやすくなります。 ## 素材アップロード時のコツ PiAPIのClaymation AI Generatorでは、クレイアニメ風に変換するための基本プロンプトはプレイグラウンド側に組み込まれています。そのため、ユーザーが最初から長いプロンプトを書く必要はありません。 基本は、素材をアップロードして変換するだけです。画像なら「クレイ風画像」に、動画なら「クレイ風動画」に変換する流れになります。 - 被写体がはっきり写っている画像を使う - 暗すぎる写真や動画を避ける - 背景が複雑すぎない素材を選ぶ - 動画は短めのクリップから試す - 変換後に見せたい主役が画面内で大きく見える素材を使う 動画の場合は、必要に応じて追加のモーション指示を入れることもできます。たとえば「ゆっくり歩く」「商品を中心にカメラが近づく」「キャラクターが手を振る」のように、動きの方向だけを短く補足すると使いやすくなります。 ただし、粘土風にするための説明を毎回書く必要はありません。Claymation AI Generatorの役割は、アップロードされた素材をクレイアニメ風に変換することです。 ## 従来のクレイアニメ制作とAI変換の違い 従来のクレイアニメ制作とAIによるクレイアニメ風変換は、目的も作り方も違います。どちらが優れているというより、使う場面が違うと考えるのが自然です。 項目 従来のクレイアニメ AIクレイアニメ変換 制作方法 粘土を造形し、1コマずつ撮影 既存の画像や動画を粘土アニメ風に変換 必要なもの 粘土、撮影環境、照明、編集ソフト AIツール、素材画像、素材動画、必要に応じた短い動きの指示 制作時間 長い 短い 向いている用途 本格作品、手作り作品、教育制作 SNS、広告案、絵コンテ、コンセプト制作 注意点 撮影と編集の手間が大きい 結果の一貫性や細部調整が必要 本格的な作品を作りたいなら、実際に粘土を使った制作には大きな価値があります。一方で、短時間で見た目を試したい、複数の案を比較したい、既存の映像を別スタイルにしたい場合は、AI変換のほうが向いています。 ## PiAPI Claymation AI Generatorでできること PiAPIの クレイアニメAIジェネレーター では、写真や画像をクレイアニメ風画像に変換したり、動画をクレイアニメ風動画に変換したりできます。 要約: PiAPI Claymation AI Generatorは、画像や動画をアップロードしてクレイアニメ風に変換するためのAIツールです。画像では粘土風の静止画を作成し、動画では既存クリップに粘土アニメ風の質感と雰囲気を加えられます。 特に便利なのは、すでにある素材を活かせる点です。ゼロから粘土キャラクターを作る必要はありません。手元の写真、商品画像、人物動画、短いクリップを使って、粘土アニメ風の見た目を試せます。 - 画像からクレイ風画像へ: 商品写真、人物写真、キャラクター画像を粘土風に変換する。 - 動画からクレイ風動画へ: 既存の短い動画を、クレイアニメ風の質感と雰囲気に変換する。 ## まとめ クレイアニメは、粘土素材の手作り感とストップモーションの動きが魅力のアニメーション表現です。粘土アニメとも近い意味で使われ、温かく親しみやすいビジュアルを作りたいときに向いています。 AIを使えば、従来のように粘土を造形して1コマずつ撮影しなくても、写真や動画をクレイアニメ風に変換できます。特に、画像からクレイ風画像へ、動画からクレイ風動画へ変換する使い方は、SNS、広告、商品ビジュアル、キャラクター案に活用しやすい方法です。 クレイアニメAIを試したい場合は、PiAPIの 写真や動画を粘土アニメ風コンテンツに変換 できるClaymation AI Generatorから始めてみてください。 ## LinkedIn AI Headshot: How to Create a Professional Profile Photo with AI Create a LinkedIn AI headshot from a clear portrait. Learn what photo to upload, which outfit and background to choose, and try PiAPI's AI headshot generator. PiAPI PiAPI A LinkedIn AI headshot is a professional profile photo generated from a clear portrait using AI. It can help you create a polished LinkedIn photo without booking a studio shoot. A good LinkedIn photo should make you look recognizable, professional, and current. But booking a studio shoot can feel like too much work if you only need a clean profile picture, resume photo, or team bio image. Instead of scheduling an in-person photoshoot, you can start from a clear portrait and use an AI headshot generator to create a LinkedIn-ready image with a better outfit, cleaner background, and stronger profile crop. In this guide, you will learn what photo to upload, which styles work best, and how to try the workflow directly with PiAPI's AI Headshot Generator. A LinkedIn AI headshot is a professional profile photo generated from a clear portrait using AI. The workflow can update your outfit, clean up the background, and create a polished image for LinkedIn, resumes, team pages, or executive bios. For LinkedIn, the goal is not to look like a different person. The goal is to look like a polished version of yourself: clear face, realistic expression, appropriate outfit, and a background that does not distract. LinkedIn's profile photo guidelines say your photo should reflect your likeness, even when using an illustration or artistic rendering. That matters for AI headshots too: the final result should still look like you. A LinkedIn AI headshot is useful when your current LinkedIn photo is outdated, too casual, poorly lit, or no longer fits your role. The main advantage is speed. A traditional photoshoot can require scheduling, travel, studio time, editing, and delivery. A LinkedIn AI headshot workflow is simpler: upload a suitable portrait, choose a professional style, and generate options you can review immediately. You still need judgment, though. Choose a realistic image that fits your industry and looks appropriate for LinkedIn. The best photo for a LinkedIn AI headshot is a clear, recent portrait with your full face visible, natural lighting, and minimal obstruction. You do not need a perfect studio photo as the input. You just need enough facial detail for the AI to work with. Your outfit should match your role, industry, and personal brand. A lawyer, startup founder, designer, and doctor may each need a different style. The best background for a LinkedIn headshot is clean, simple, and not distracting. These examples use the same source portrait direction and show how outfit and background choices can change the professional signal of a virtual headshot. A LinkedIn headshot generator is usually enough for a clean LinkedIn profile picture. An AI photoshoot may be better if you want many styles for different platforms. A traditional photoshoot still makes sense when you need custom lighting or campaign-level brand photography. The best LinkedIn AI headshot should look professional and still believable. A LinkedIn AI headshot is one of the simplest ways to refresh your LinkedIn profile without booking a studio photoshoot. Start with a clear portrait, choose an outfit that matches your professional role, pick a simple background, and review the result for realism before uploading it. Start testing PiAPI's AI Headshot Generator and create a LinkedIn AI headshot today! Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## Seedance 2 Real Face Tutorial: Generate Human Video Assets with PiAPI Learn how Seedance 2 real face workflows work, how to prepare human-face assets, when verification may be required, and how to generate realistic face videos with PiAPI. Seedance real face generation is useful when you want a human reference image to become a realistic short video: a presenter speaking to camera, a creator-style clip, or a product ad where a person and product both need to stay recognizable. This tutorial focuses on the practical PiAPI playground workflow. You will use the Seedance 2.0 playground, choose Less Restriction, choose Face Reference, upload a consented real-person image, and write prompts with `@image1` so Seedance understands what the uploaded reference represents. Definition: Seedance real face generation means using a consented human face or person image as a reference for Seedance video generation, so the output keeps the person's general identity, expression, pose, and scene role more consistently than a text-only prompt. ## Key Takeaways - Use the Seedance 2.0 playground first when you want to test real face or real people generation quickly. - Choose Less Restriction and Face Reference for the playground workflow shown in this guide. - Upload a clear, consented portrait or human reference image directly in the playground. - Use `@image1`, `@image2`, and similar labels in the prompt to explain what each uploaded image represents. - Use Private Asset Library only when you need a reusable, verified, or API-managed reference workflow. - Judge results by identity consistency, facial realism, natural motion, lighting, and scene fit. ## What Is Seedance 2 Real Face Generation? Seedance 2 real face generation is an image-to-video workflow where a face or full person image guides the generated video. Instead of asking for "a realistic spokesperson" from text alone, you upload a human reference and tell Seedance how that person should move, speak, pose, or interact with the scene. The goal is not classic face swap or pixel-perfect face replacement. A better expectation is reference-guided video generation. The uploaded person helps Seedance preserve the look and role of the subject while creating a new video clip around that reference. This makes the workflow useful for: - creator avatar tests; - talking-head spokesperson clips; - profile-to-video demos; - product ads with a real person reference; - internal creative review before building an API workflow. ## Can Seedance 2 Use Real People? Yes, Seedance 2 can use real-person references in the PiAPI playground when the workflow supports face reference generation. For this tutorial, use a consented image of the person and test in the Seedance 2.0 playground . Quick answer: To try Seedance real face generation in PiAPI, open the Seedance 2.0 playground, choose Less Restriction, choose Face Reference, upload a consented person image, set duration and quality, then write a prompt that uses `@image1` to describe the uploaded face or person reference. Use this responsibly. Only upload people, faces, creators, employees, actors, or customers when you have the right to use that reference for generation. ## Prepare A Good Real Face Reference Image The face reference does most of the heavy lifting. A better input image usually gives you a more stable output. Use a reference image with: - one main person clearly visible; - sharp eyes and visible facial features; - natural lighting without heavy shadows; - minimal filters, beauty effects, or motion blur; - a useful crop, such as head-and-shoulders or upper body; - clothing and background that do not distract from the face; - permission or ownership for the person shown. Avoid images where the face is tiny, angled too far away, blocked by hands or sunglasses, heavily stylized, or mixed with too many people. ## When Private Asset Library Is Needed For this playground tutorial, you do not need to pre-upload an Active private asset. The playground lets you upload the real-person reference directly for the Face Reference workflow. Private Asset Library becomes useful when you are moving from manual testing into a repeatable API workflow. Use it when: - the same face or person reference will be reused across many future videos; - your app needs asset IDs, lifecycle control, or backend-managed references; - a verified or reusable face asset is required for your production workflow; - you want to combine a managed person reference with changing prompts or product references. Playground vs private asset: use the playground for quick real face tests with direct uploads. Use the Seedance Private Asset Library guide when you need reusable API references, asset status, or `asset://` IDs. ## Example Workflow: Generate A Real Face Video In The Playground Follow this workflow when you want a fast first test without writing API code. - Open the Seedance 2.0 playground . - Choose Less Restriction mode. - Choose Face Reference generation mode. - Upload a clear, consented portrait or human reference image. - Set duration, such as 5 seconds for the first test. - Set output quality, such as 720p for quick iteration. - Set aspect ratio, such as 16:9 for a standard horizontal video. - Write a prompt that uses `@image1` to describe the uploaded person reference. - Generate the video and review face consistency, expression, motion, lighting, and background quality. Here is a simple prompt structure: "@image1 is the consented person reference. Generate a realistic 5-second video of this person speaking calmly to camera in a modern studio. Keep the face, hairstyle, and overall identity consistent with @image1. Use natural facial expression, subtle head movement, soft lighting, and a professional creator-video style." ## Example 1: Professional AI Product Presenter This example uses a clean headshot-style reference for a professional presenter clip. It is useful for testing business, SaaS, education, or AI product explainer content. Prompt: "@image1 is the consented presenter reference. Generate a polished 5-second video of this person presenting an AI software product in a clean creator studio. Keep the face, hairstyle, outfit style, and friendly professional expression consistent with @image1. Add natural blinking, slight hand movement, and a slow camera push-in. The scene should feel like a realistic B2B product video, with soft daylight and no exaggerated expressions." Why this example works: the portrait is simple and frontal, so the model can focus on facial consistency. The prompt also describes the role of the person instead of only describing the background. ## Example 2: Talking Presenter Video This example turns a clean portrait into a short speaking video. It is the simplest way to test whether Seedance can preserve a real face while adding natural motion. Prompt: "@image1 is the consented presenter reference. Generate a realistic 5-second medium-shot video of this person speaking calmly to camera in a clean professional studio. Keep the face, hairstyle, clothing, and overall identity consistent with @image1. Add subtle head movement, natural blinking, small mouth movement, and soft neutral lighting. Do not change the person's age, gender, or facial structure." Why this example works: the input has one centered person, clear lighting, visible facial details, and a scene that already looks like a creator recording setup. The prompt asks for restrained motion instead of dramatic action, which usually helps face consistency. ## Example 3: Real Face Plus Product Advertisement The third example adds a product reference. This is useful when you want a person-led ad video while also keeping the product recognizable. Prompt: "@image1 is the consented spokesperson reference. @image2 is the black wireless earbuds product reference. Generate a realistic 5-second product advertisement where the person from @image1 presents the earbuds from @image2 in a premium studio setting. Keep the person's face and hairstyle consistent with @image1. Keep the earbuds shape, black color, charging case, and product details consistent with @image2. Use smooth camera movement, clean lighting, and a modern tech-ad style." Why this example works: the prompt assigns a clear role to each upload. `@image1` is the person reference, while `@image2` is the product reference. This is much easier for the model to follow than saying "use these images" without labels. ## Prompt Tips For Better Human-Face Consistency Good prompts for Seedance real face generation are specific, but not overloaded. The model needs to know what the person should preserve and what can change. Use prompts that mention: - what `@image1` represents; - the type of shot, such as close-up, medium shot, or upper-body; - the motion level, such as subtle head movement or slow turn; - the expression, such as calm, friendly, confident, or neutral; - the environment and lighting; - what should stay consistent, such as face, hairstyle, outfit, and age; - what should not happen, such as face morphing, extra people, or distorted hands. For most real face tests, start with restrained motion. Speaking, blinking, nodding, and small hand movement are easier to evaluate than fast dancing, spinning, or extreme camera motion. ## Common Problems And Fixes ## The face changes too much Use a clearer face reference, reduce action intensity, and explicitly ask Seedance to keep the face, hairstyle, age, and facial structure consistent with `@image1`. ## The person looks realistic but not like the input Make the prompt more reference-driven. For example, write "`@image1` is the person reference" near the start of the prompt and describe the person role clearly. ## The motion looks unnatural Use a shorter duration and simpler movement. Ask for subtle blinking, small head movement, and natural posture before testing more dramatic camera motion. ## The product changes shape in the ad example Label the product image separately with `@image2` and describe the product details that must stay stable, such as color, case shape, logo area, and material. ## The output feels too generic Add a concrete use case. "Product launch video," "creator intro," "B2B software demo," and "premium earbud ad" give Seedance more useful direction than "make this realistic." ## Responsible Use For Real Face Video Assets Real face video generation needs stricter judgment than generic scenery or product animation. Use references only when you have permission, ownership, or a legitimate production agreement. Practical safeguards include: - document consent for people shown in uploaded references; - avoid misleading impersonation or undisclosed synthetic endorsements; - review outputs before publishing; - remove failed or distorted generations from customer-facing workflows; - separate internal tests from public campaign assets. ## Conclusion The fastest way to test Seedance real face generation is through the PiAPI Seedance 2.0 playground. Choose Less Restriction, choose Face Reference, upload a clear consented person image, set a short test duration, and write a prompt that labels the uploaded reference with `@image1`. Once the result quality is strong enough, you can decide whether the workflow should stay as a manual playground process or move into an API/private asset setup for reusable face and product references. Start testing Seedance 2.0 and get your API access via PiAPI today! Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## FAQ ### What is Seedance real face generation? Seedance real face generation is a reference-guided video workflow where a human face or person image helps Seedance generate a realistic video while preserving the person's general appearance and role. ### Do I need Private Asset Library for the playground workflow? No. For the playground workflow in this tutorial, you can upload a real-person reference directly in Face Reference mode. Private Asset Library is mainly for reusable, verified, or API-managed references. ### How should I reference uploaded images in the prompt? Use labels such as `@image1` and `@image2`. For example, write "`@image1` is the consented spokesperson reference" or "`@image2` is the earbuds product reference." Clear labels help Seedance understand each uploaded image's role. ### What settings should I start with? Start with Less Restriction, Face Reference, 5 seconds, 720p, and 16:9. These settings are simple enough for quick review before testing longer duration, higher quality, or more complex motion. ### Can I use a product image and a face image together? Yes. Upload the person as one reference and the product as another reference, then explain both in the prompt. The ad example in this tutorial uses `@image1` for the person and `@image2` for the earbuds. ### Is this the same as face swap? No. Treat this as reference-guided AI video generation, not exact face swap or pixel-perfect compositing. The face image guides the generated result, but the output is still a new AI-generated video. ## AI 외모 평가란? 얼굴 분석과 AI 얼굴 평가 점수 이해하기 AI 외모 평가가 무엇인지, AI 안면 분석이 어떤 얼굴 특징을 보는지, AI 얼굴 평가 점수를 안전하게 해석하는 방법을 알아보세요. Meta Description : AI 외모 평가가 무엇인지, AI 안면 분석이 어떤 얼굴 특징을 보는지, AI 얼굴 평가 점수를 안전하게 해석하는 방법을 알아보세요. AI 외모 평가는 셀피나 프로필 사진을 업로드했을 때 AI가 얼굴 특징을 분석하고 점수나 리포트 형태로 보여주는 방식입니다. 최근에는 얼평 , AI 얼굴 평가 , 종합 얼굴 테스트 처럼 가볍게 결과를 확인하는 테스트도 많아졌지만, 점수를 그대로 믿기보다는 어떤 요소가 반영되는지 이해하는 것이 더 중요합니다. 빠른 답변 : AI 외모 평가는 사진 속 얼굴 특징을 AI가 분석해 점수나 리포트 형태로 보여주는 방식입니다. 보통 얼굴 비율, 대칭, 눈과 코, 턱선, 표정, 사진 품질 등을 참고하지만, 결과는 절대적인 미의 기준이나 개인의 가치를 의미하지 않습니다. 핵심 요약 - AI 외모 평가는 얼굴 사진의 보이는 특징을 분석하는 참고용 피드백입니다. - AI 얼굴 평가 점수는 조명, 각도, 표정, 이미지 품질, 도구별 기준에 따라 달라질 수 있습니다. - AI 안면 분석은 얼굴 비율, 대칭, 이목구비 배치, 턱선, 표정, 사진 선명도 같은 요소를 볼 수 있습니다. - 점수보다 카테고리별 설명을 보는 것이 더 유용합니다. - AI 외모 평가 결과는 개인의 가치, 건강, 정체성, 성격을 판단하는 기준이 아닙니다. ## AI 얼굴 평가 바로 체험하기 셀피를 바로 테스트해보고 싶다면 이 위치에 PiAPI AI Face Rater의 compact blog playground를 삽입합니다. 사용자는 프롬프트를 직접 작성하지 않고 얼굴이 잘 보이는 사진을 업로드한 뒤, AI 얼굴 평가 리포트 흐름을 미리 체험할 수 있습니다. 플레이그라운드 안내 문구 : 결과는 사진 속 보이는 얼굴 특징과 이미지 품질을 바탕으로 한 참고용 피드백입니다. 개인의 가치, 건강, 정체성, 성격을 판단하는 용도가 아닙니다. ## 목차 - AI 얼굴 평가 바로 체험하기 - AI 외모 평가란 무엇인가요? - AI 얼굴 평가는 어떻게 작동하나요? - AI 안면 분석은 어떤 얼굴 특징을 보나요? - AI 외모 평가, AI 얼굴 평가, 종합 얼굴 테스트의 차이 - 외모 평가 점수가 달라지는 이유 - AI 얼굴 평가기를 사용할 때 좋은 사진 조건 - AI 외모 평가 점수를 안전하게 해석하는 방법 - PiAPI AI Face Rater로 얼굴 평가 리포트 만들어보기 - AI 외모 평가 FAQ ## AI 외모 평가란 무엇인가요? AI 외모 평가는 얼굴 사진을 바탕으로 보이는 특징을 분석하고, 그 결과를 점수나 설명으로 정리하는 AI 기반 평가 방식입니다. 어떤 도구는 단순히 100점 만점의 얼굴 점수를 보여주고, 어떤 도구는 얼굴 비율, 대칭, 표정, 사진 품질 같은 항목별 피드백을 제공합니다. 정의 : AI 외모 평가는 사용자가 업로드한 얼굴 사진에서 보이는 얼굴 특징과 이미지 품질을 AI가 분석해 점수, 설명, 리포트 형태로 보여주는 방식입니다. 이 평가는 사진 기반 피드백이며, 사람의 가치나 정체성을 판정하는 기준이 아닙니다. 한국 검색 결과에서는 외모 평가 , 얼굴평가 , AI 얼굴 평가 , 얼평 테스트 같은 표현이 함께 쓰입니다. 모두 비슷한 관심사에서 출발하지만, 목적은 조금씩 다를 수 있습니다. 어떤 사용자는 재미로 점수를 확인하고, 어떤 사용자는 프로필 사진이나 셀피가 어떻게 보이는지 참고하려고 합니다. 중요한 점은 AI 얼굴 평가가 사람의 매력이나 가치를 객관적으로 판정하는 시스템이 아니라는 것입니다. AI는 사진 안에서 확인할 수 있는 시각적 단서만 분석합니다. 그래서 결과는 참고용 피드백으로 보는 것이 가장 안전합니다. ## AI 얼굴 평가는 어떻게 작동하나요? AI 얼굴 평가는 보통 사진 업로드, 얼굴 특징 분석, 점수 또는 리포트 생성, 결과 확인의 순서로 작동합니다. 사용자가 셀피를 업로드하면 AI가 사진 속 얼굴과 이미지 품질을 읽고, 정해진 기준에 따라 결과를 구성합니다. 일반적인 흐름은 다음과 같습니다. - 얼굴이 보이는 사진을 업로드합니다. - AI가 얼굴 위치와 주요 특징을 분석합니다. - 얼굴 비율, 대칭, 표정, 선명도 같은 요소를 참고합니다. - 점수, 설명, 카테고리별 피드백 또는 리포트가 생성됩니다. - 사용자는 결과를 보고 사진이나 표정을 바꿔 다시 테스트할 수 있습니다. 다만 모든 AI 얼굴 평가기가 같은 방식으로 작동하는 것은 아닙니다. 어떤 도구는 엔터테인먼트 테스트에 가깝고, 어떤 도구는 얼굴 특징 리포트나 프로필 사진 피드백에 더 가깝습니다. 그래서 같은 사진을 넣어도 도구마다 점수와 설명이 달라질 수 있습니다. ## AI 안면 분석은 어떤 얼굴 특징을 보나요? AI 안면 분석은 사진에 보이는 얼굴의 구조와 이미지 상태를 함께 봅니다. 도구마다 표현은 다르지만, AI 얼굴 평가에서 자주 언급되는 항목은 다음과 같습니다. 분석 항목 의미 얼굴 비율 얼굴 길이, 폭, 이목구비 배치가 전체적으로 어떻게 보이는지 좌우 대칭 양쪽 얼굴 특징이 얼마나 균형 있게 보이는지 눈, 코, 입의 배치 주요 이목구비의 위치와 조화 턱선과 얼굴 윤곽 턱, 볼, 얼굴형의 선명도와 균형 표정 자연스러움, 긴장감, 친근한 인상 사진 품질 조명, 선명도, 해상도, 얼굴 가림 여부 전체적인 얼굴 조화 여러 특징이 함께 보일 때의 균형감 여기서 사진 품질은 특히 중요합니다. 흐릿한 사진, 강한 그림자, 과한 필터, 옆모습에 가까운 각도는 AI가 얼굴 특징을 안정적으로 읽기 어렵게 만들 수 있습니다. 이런 경우 점수가 낮게 나오거나 설명이 실제 인상과 다르게 느껴질 수 있습니다. ## AI 외모 평가, AI 얼굴 평가, 종합 얼굴 테스트의 차이 AI 외모 평가, AI 얼굴 평가, 종합 얼굴 테스트는 검색 의도가 겹치지만 완전히 같은 표현은 아닙니다. AI 외모 평가는 가장 넓은 개념이고, AI 얼굴 평가는 얼굴 사진 기반 평가 도구에 더 가깝고, 종합 얼굴 테스트는 재미용 테스트나 유형 결과까지 포함하는 경우가 많습니다. 표현 주된 의미 사용자가 기대하는 결과 AI 외모 평가 사진 속 보이는 외모 특징을 AI가 분석하는 넓은 개념 점수, 설명, 카테고리별 피드백 AI 얼굴 평가 얼굴 사진을 중심으로 한 AI 기반 평가 얼굴 점수, 얼굴 분석 리포트, 셀피 피드백 AI 안면 분석 얼굴 특징을 더 기술적으로 분석하는 표현 얼굴 비율, 대칭, 이목구비, 윤곽 분석 종합 얼굴 테스트 재미용 테스트 또는 여러 항목을 합친 얼굴 테스트 종합 점수, 유형 결과, 공유 가능한 결과 PiAPI AI Face Rater는 이 중에서 AI 얼굴 평가 와 AI 안면 분석 에 가까운 흐름입니다. 사용자가 셀피를 업로드하면 사진 속 보이는 얼굴 특징을 바탕으로 AI 얼굴 평가 리포트를 생성하는 경험을 제공합니다. ## 외모 평가 점수가 달라지는 이유 AI 외모 평가 점수는 조명, 각도, 표정, 이미지 품질, 필터, 얼굴 가림, 그리고 도구별 평가 기준에 따라 달라질 수 있습니다. 같은 사람이라도 사진 조건이 바뀌면 AI가 읽는 정보도 달라집니다. 예를 들어 정면에서 밝게 찍은 사진과 어두운 실내에서 비스듬히 찍은 사진은 얼굴 비율이나 윤곽이 다르게 보일 수 있습니다. 웃는 표정과 무표정도 결과에 영향을 줄 수 있습니다. 카메라 렌즈나 촬영 거리 때문에 얼굴 일부가 왜곡되어 보이는 경우도 있습니다. 또한 AI 얼굴 평가 도구마다 목표가 다릅니다. 어떤 도구는 재미있는 얼굴 점수에 초점을 맞추고, 어떤 도구는 얼굴형이나 스타일 조언을 강조하며, 어떤 도구는 사진 속 특징을 리포트처럼 정리합니다. 따라서 하나의 점수를 절대적인 결과로 받아들이기보다, 여러 조건이 반영된 참고값으로 보는 것이 좋습니다. 사진 조건에 따라 점수가 바뀌는 이유를 더 자세히 보고 싶다면 AI 얼굴 평가 점수가 달라지는 이유 를 참고할 수 있습니다. ## AI 얼굴 평가기를 사용할 때 좋은 사진 조건 AI 얼굴 평가기를 사용할 때는 AI가 얼굴을 쉽게 읽을 수 있는 사진을 선택하는 것이 좋습니다. 좋은 입력 사진은 점수 자체보다도 결과 설명의 품질을 높이는 데 도움이 됩니다. 추천 조건은 다음과 같습니다. - 얼굴이 정면에 가깝게 보이는 사진 - 눈, 코, 입, 턱선이 가려지지 않은 사진 - 조명이 충분하고 그림자가 너무 강하지 않은 사진 - 흐림이나 흔들림이 적은 고해상도 사진 - 과한 보정, 필터, 스티커가 없는 사진 - 단체 사진보다 한 사람의 얼굴이 중심에 있는 사진 - 지나치게 과장되지 않은 자연스러운 표정 종합 얼굴 테스트 나 AI 얼굴 평가기 를 사용할 때도 이 원칙은 비슷합니다. AI는 사진 안의 정보를 바탕으로 결과를 만들기 때문에, 입력 이미지가 명확할수록 더 일관된 리포트를 기대할 수 있습니다. 사진이 흐리거나 해상도가 낮다면 이미지 업스케일러 로 선명도를 높인 뒤 테스트하는 것도 방법입니다. ## AI 외모 평가 점수를 안전하게 해석하는 방법 AI 외모 평가 점수는 개인의 가치, 성격, 건강, 정체성을 판단하는 기준이 아니라 사진 속 보이는 얼굴 특징과 이미지 품질을 바탕으로 한 참고용 피드백입니다. 점수를 볼 때는 숫자 하나에 너무 집중하기보다, 어떤 항목에서 어떤 설명이 나왔는지 확인하는 것이 좋습니다. 예를 들어 "사진이 어둡다", "얼굴 각도가 비스듬하다", "표정이 경직되어 보인다" 같은 피드백은 다음 사진을 찍을 때 바로 활용할 수 있습니다. 반대로 AI 외모 평가를 다른 사람을 놀리거나 비교하거나 순위를 매기는 용도로 사용하는 것은 피해야 합니다. 특히 본인의 사진이 아닌 이미지를 허락 없이 업로드하거나, 결과를 근거로 누군가를 괴롭히는 방식은 책임 있는 사용이 아닙니다. 가장 건강한 사용법은 간단합니다. AI 얼굴 평가는 셀피나 프로필 사진을 더 잘 이해하기 위한 보조 피드백으로 사용하고, 개인의 자존감이나 사회적 가치를 판단하는 기준으로 사용하지 않는 것입니다. ## PiAPI AI Face Rater로 얼굴 평가 리포트 만들어보기 AI 얼굴 평가를 직접 체험해보고 싶다면 PiAPI의 AI Face Rater 를 사용할 수 있습니다. 셀피를 업로드하면 얼굴 비율, 대칭, 표정, 전체적인 조화 등을 바탕으로 AI 얼굴 평가 리포트를 생성해 볼 수 있습니다. PiAPI AI Face Rater는 PiAPI가 제공하는 AI 얼굴 평가 도구입니다. 단순히 숫자만 보여주는 것보다, 사진 속 보이는 얼굴 특징을 리포트 형태로 이해할 수 있도록 돕는 방향에 가깝습니다. 프로필 사진, 셀피, 소셜 미디어용 이미지처럼 얼굴이 잘 보이는 사진을 사용할 때 더 좋은 결과를 기대할 수 있습니다. 개발자나 서비스 운영자라면 이런 얼굴 분석형 리포트 경험을 앱이나 웹사이트에 접목하는 방식도 생각해볼 수 있습니다. 이 경우에는 사용자의 동의, 이미지 처리 방식, 결과 표현의 안전성까지 함께 설계하는 것이 중요합니다. ## AI 외모 평가 FAQ ## AI 외모 평가는 무엇인가요? AI 외모 평가는 사진 속 얼굴 특징을 AI가 분석해 점수, 설명, 리포트 형태로 보여주는 방식입니다. 얼굴 비율, 대칭, 표정, 사진 품질 같은 시각적 단서를 참고하지만, 개인의 가치나 매력을 객관적으로 판단하는 기준은 아닙니다. ## AI 얼굴 평가는 정확한가요? AI 얼굴 평가는 참고용 피드백으로는 유용할 수 있지만 완전히 객관적이거나 항상 정확한 결과는 아닙니다. 조명, 카메라 각도, 표정, 필터, 이미지 품질, 사용하는 도구의 기준에 따라 결과가 달라질 수 있습니다. ## AI 안면 분석은 어떤 특징을 분석하나요? AI 안면 분석은 보통 얼굴 비율, 좌우 대칭, 눈과 코와 입의 배치, 턱선, 얼굴 윤곽, 표정, 사진 선명도 등을 봅니다. 도구에 따라 분석 항목과 결과 표현 방식은 다를 수 있습니다. ## AI 얼굴 평가 점수는 왜 사진마다 달라지나요? 사진마다 조명, 각도, 표정, 해상도, 흐림, 필터, 얼굴 가림 정도가 다르기 때문입니다. AI는 사진 속 보이는 정보를 바탕으로 판단하므로 같은 사람이라도 입력 사진이 달라지면 얼굴 평가 점수도 달라질 수 있습니다. ## AI 얼굴 평가기를 사용할 때 어떤 사진이 좋나요? 정면에 가깝고 얼굴이 선명하게 보이는 사진이 좋습니다. 눈, 코, 입, 턱선이 가려지지 않고 조명이 충분하며 과한 필터가 없는 사진을 사용하면 AI 얼굴 평가 리포트가 더 일관되게 나올 가능성이 높습니다. ## 종합 얼굴 테스트와 AI 외모 평가는 같은 뜻인가요? 완전히 같은 뜻은 아니지만 검색 의도는 자주 겹칩니다. 종합 얼굴 테스트는 재미있는 점수나 유형 결과에 가까운 경우가 많고, AI 외모 평가는 사진 속 얼굴 특징을 AI가 분석해 점수나 리포트로 보여주는 더 넓은 표현입니다. ## AI 외모 평가 점수로 사람의 가치를 판단할 수 있나요? 아니요. AI 외모 평가 점수는 사람의 가치, 성격, 건강, 정체성, 사회적 매력을 판단하는 기준이 아닙니다. 사진 속 보이는 특징과 이미지 품질에 기반한 참고용 결과로만 해석하는 것이 안전합니다. ## 결론 AI 외모 평가는 얼굴 사진을 바탕으로 AI가 보이는 특징을 분석하고 점수나 리포트로 정리하는 방식입니다. AI 얼굴 평가 , AI 안면 분석 , 얼굴평가 , 종합 얼굴 테스트 같은 표현은 모두 비슷한 관심사와 연결되지만, 결과를 해석할 때는 사진 조건과 도구별 기준을 함께 봐야 합니다. 가장 중요한 것은 점수를 개인의 가치로 받아들이지 않는 것입니다. AI 얼굴 평가는 셀피나 프로필 사진을 이해하는 참고용 피드백으로 활용할 때 가장 유용합니다. ## AI Age Filter Online: How to Make Yourself Look Older or Younger Use an AI age filter online to make yourself look older or younger. Learn how age progression works, what photo to upload, and how to try PiAPI's AI Age Filter. PiAPI PiAPI An AI age filter online lets you upload a portrait and generate a version of the same person at a different age. You can use it to make yourself look older, create a younger-looking portrait, test a future-self concept, or build age transformation into a creative workflow. If you want to try it now, open PiAPI's AI age filter online , upload a clear portrait, choose a target age, and generate an older or younger version in the browser. Quick answer: An AI age filter online is a browser-based image tool that edits a portrait to make the person look older or younger. The best age filters preserve the person's recognizable identity while changing age-related details such as skin texture, facial fullness, hair, jawline, and eye area. An AI age filter is an image editing tool that changes the apparent age of a person in a photo while trying to preserve their recognizable identity. Instead of placing a simple wrinkle overlay on top of the image, a realistic AI age filter adjusts age-related details across the face and head, such as skin texture, facial fullness, hair color, hairline, eye area, jawline, and expression. In practical terms, age progression is useful for future-self portraits, character aging, storytelling, and concept images. Age regression is useful for younger-looking portraits, de-aging edits, and creative before-and-after comparisons. The best results still look like the same person. The face should not become a random stranger, and the image should keep the original pose, lighting, camera angle, clothing, and background whenever possible. Using an AI age filter online is usually simple: upload a portrait, choose the target age, generate the result, and compare the before-and-after image. The quality of the result depends heavily on the input photo and how clear the target age instruction is. - Upload a clear portrait. - Select a target age or age range. - Generate the age-filtered image. - Compare the result with the original photo. - Download or reuse the image in your creative workflow. For PiAPI's tool, start with the AI Age Filter page. It is built around a practical upload-and-generate flow, so you can test the result first and then think about whether you want to reuse the same type of generation in a product, app, or content workflow. These examples show how different portrait types can pass through the AI age filter: a clean studio headshot, a casual phone selfie, and a three-quarter outdoor portrait. Together they show the range of input photos users may bring to the tool. To make yourself look older with AI, choose a clear face photo and select an older target age that fits the result you want. For example, you might try a future-self portrait at 40, 50, 60, or 70 years old. The strongest results usually come from portraits where the face is visible, well-lit, and not blocked by sunglasses, masks, heavy shadows, or extreme angles. AI can create a believable older portrait, but it should not be treated as a real prediction of how you will age. An AI age filter can also make a person look younger. This is sometimes called age regression or de-aging. The goal is not just to smooth the face until it looks artificial. A good younger version should keep the same identity while adjusting age-related details in a natural way. It should avoid over-smoothed skin, plastic-looking faces, or changes that make the person look like someone else. If the result looks too generic, try a clearer photo, a more realistic target age, or a more specific age instruction. A realistic AI age filter does not only add wrinkles or remove them. It changes the portrait in a way that feels biologically consistent while keeping the person recognizable. Realism rule: A believable AI age filter should change age-related facial details while preserving identity, pose, lighting, and background. If the result looks like a different person, a beauty filter, or a wrinkle overlay, it is not a strong age transformation. The most important quality signal is identity preservation. The result should still feel like the same person at a different age. The face, hair, neck, and visible skin should all match the target age, while lighting and background should stay consistent so the edit feels like one real photograph. The input photo matters. If the AI cannot clearly see the face, it has less information to preserve identity and apply a believable age transformation. Avoid photos where the face is too small, heavily cropped, covered, or distorted by an extreme camera angle. If the portrait is useful but low-resolution, an AI image upscaler can help improve detail before you test an age transformation. AI age progression is not an exact prediction of the future. It is a creative image transformation that estimates how a person might look at another age based on visual patterns learned by the model. That means an AI-generated older or younger portrait can be useful for entertainment, concept art, social content, storytelling, character design, or product prototyping. It should not be used as proof of how someone will actually age. PiAPI's AI Age Filter lets you upload a portrait, choose a target age, and generate an older or younger version online. It is designed for users who want a quick browser-based age filter and for builders who want to understand how AI image editing can fit into a product workflow. If you are building with AI image generation, PiAPI can also help turn this type of playground workflow into an API-backed feature using models such as GPT Images 2 . ## Seedance 2.5 API Is Live: Model, Pricing & Playground Guide Seedance 2.5 is live on PiAPI. Explore its API task type, 4-15 second duration, 480p/720p/1080p pricing, generation modes, references, and playground. Seedance 2.5 is ByteDance's latest Seedance AI video model, built for creators, developers, and AI video teams that need controllable text-to-video and reference-guided generation. The important API note is simple: Seedance 2.5 is now live on PiAPI. Explore the Seedance 2.5 model page , generate through the Seedance Workspace , or call the API with model \ seedance\ and task type \ seedance-2.5\ . This guide explains the live PiAPI request contract, supported modes, limits, pricing, and how Seedance 2.5 compares with Seedance 2.0 . ## Key Takeaways - Seedance 2.5 is live in the PiAPI API and playground. - Use model \ seedance\ with task type \ seedance-2.5\ . - The API supports \ text_to_video\ , \ first_last_frames\ , and \ omni_reference\ modes. - Output duration is 4-15 seconds at 480p or 720p, with 720p as the default. - Pricing is $0.30 per output second at 480p and $0.60 per output second at 720p. ## In This Guide - Is Seedance 2.5 available through PiAPI? - What is new in Seedance 2.5? - What Seedance 2.5 means for production workflows - Seedance 2.5 vs Seedance 2.0 - How to prepare for Seedance 2.5 API workflows - FAQ ## Quick Answer: What Is Seedance 2.5? Seedance 2.5 is ByteDance's latest Seedance AI video model. On PiAPI it supports text-to-video, first/last-frame generation, and omni-reference generation with image, video, and audio inputs. Definition: Seedance 2.5 is a live ByteDance AI video model available through PiAPI for controllable text- and reference-guided video generation. For creators and developers, the important question is not only whether the model can generate an impressive clip. It is whether teams can maintain characters and products, coordinate multiple references, revise part of a scene, and reduce the amount of stitching and manual post-production required to deliver the final video. ## Is Seedance 2.5 Available Through PiAPI? Yes. Seedance 2.5 is live in the PiAPI Workspace and API. Create a task with \ POST /api/v1/task\ , model \ seedance\ , task type \ seedance-2.5\ , and an input object containing the selected mode and generation parameters. The public contract supports: - integer durations from 4 to 15 seconds - 480p and 720p output, with 720p as the default - text-to-video, first/last-frame, and omni-reference modes - image, video, and audio references in omni-reference mode - up to 3 video references and 3 audio references - optional generated audio Seedance 2.5 has no fast, mini, less-restriction, or private \ asset://\ variant. ## What Is New In Seedance 2.5? Seedance 2.5 brings a single, focused task type to PiAPI with three generation modes, multimodal guidance, optional audio, and clear resolution and duration controls. ## Longer Video Generation And Continuation Longer generation could give scenes more room for setup, action, camera movement, and resolution. Continuation could also help teams develop a sequence without rebuilding each segment from scratch. The practical value is less fragmentation. Instead of planning every idea as a collection of disconnected short clips, creators may be able to develop a more complete scene and spend less time selecting, extending, and stitching separate outputs. ## Stronger Scene Continuity Continuity is one of the hardest parts of AI video. Characters, products, lighting, wardrobe, and spatial relationships can drift as a scene develops. Seedance 2.5 is expected to improve this part of the workflow. Better continuity would help when a product must stay recognizable, several characters need to remain distinct, or a camera move must preserve the relationship between foreground and background elements. ## Richer Multimodal Reference Control Richer reference support could help teams coordinate characters, products, visual style, camera direction, motion, environments, and audio within one creative workflow. For developers, the value is practical: a structured reference workflow can make it easier to preserve brand and story elements across an output. This is relevant to advertising, e-commerce, fashion, recurring characters, and other applications where consistency matters. PiAPI supports image, video, and audio inputs for Seedance 2.5 omni-reference tasks, including up to 3 video references and 3 audio references. Private \ asset://\ references are not supported by Seedance 2.5; they remain specific to eligible Seedance 2.0 less-restriction workflows. ## More Flexible AI Video Editing AI video production is rarely a one-shot process. Teams need to revise actions, products, backgrounds, timing, or other details without losing the parts of a scene that already work. Seedance 2.5 is expected to move toward more targeted editing while preserving broader scene consistency. Whether these capabilities appear as API modes, parameters, or task types on PiAPI is not confirmed yet. ## What Seedance 2.5 Means For Production Workflows The most useful way to understand Seedance 2.5 is through the work it could remove from a production pipeline. Production takeaway: Longer scenes, richer references, stronger continuity, and targeted editing could reduce stitching and manual rework while making AI video easier to direct. For film, short-drama, and previsualization teams, that could mean more coherent ensemble scenes and faster iteration on blocking, action, and camera direction. For advertising and e-commerce teams, stronger product and character control could make it easier to adapt the same creative concept for different formats, audiences, and markets without rebuilding every asset from the beginning. For application developers, these workflows may require better asset organization, prompt templates, moderation, and review tools. The opportunity is not simply a longer output; it is a more structured system for moving from concept to deliverable video. For a current production example, see the Seedance AI marketing video workflow , which shows how Seedance 2.0 can support product and ad-style video generation today. ## Confirmed PiAPI Contract Detail Current status What it means Availability Live API and playground access are available now Task type \ seedance-2.5\ Use with model \ seedance\ Duration 4-15 seconds Integer values Resolution 480p or 720p 720p is the default; no 1080p Modes Three modes \ text_to_video\ , \ first_last_frames\ , \ omni_reference\ Pricing $0.30/s or $0.60/s Based on 480p or 720p output Private assets Not supported No Seedance 2.5 less-restriction variant This distinction keeps the article useful without turning early model discussion into an API promise. ## Seedance 2.5 vs Seedance 2.0 Seedance 2.5 and Seedance 2.0 are both available on PiAPI, with different model variants and output options. Area Seedance 2.5 Seedance 2.0 PiAPI availability Live API and playground Live API and playground Main workflow direction Longer, more controllable, and more editable production workflows Current generation, reference, extension, and editing workflows Best use today Latest single-model multimodal generation Multiple speed, mini, and less-restriction variants Duration 4-15 seconds 4-15 seconds Resolution 480p or 720p Up to 1080p on supported variants Reference control Images plus up to 3 videos and 3 audio files Existing reference and private-asset patterns vary by task type Editing workflow More targeted, continuity-preserving editing direction Current editing and extension options depend on task mode Best action Try the live Seedance 2.5 playground Choose a Seedance 2.0 variant for your workflow The key distinction is workflow. Seedance 2.5 is expected to put more emphasis on duration, multimodal controllability, and editability. Seedance 2.0 remains the available option for current PiAPI integrations. ## How To Build With Seedance 2.5 Start in the live playground, confirm the mode and resolution that fit your workflow, then reuse the same request contract in your application. Use this checklist: - Audit where longer scenes or continuation could reduce editing and stitching. - Map the products, characters, faces, environments, motion references, and brand assets your application uses. - Prepare asset storage and metadata so reference-heavy generation can be managed cleanly. - Separate prompt templates by scene and use case, not only by model. - Add review steps for continuity, product accuracy, and longer narrative outputs. - Use \ seedance-2.5\ as the task type and validate duration as an integer from 4 to 15. - Test Seedance 2.5 in the Seedance Workspace before scaling API traffic. This preparation keeps your team ready without creating technical debt around guesses. ## FAQ ## What is Seedance 2.5? Seedance 2.5 is ByteDance's latest Seedance AI video model and is live on PiAPI with three generation modes, multimodal references, and 480p, 720p, or 1080p output. ## Is Seedance 2.5 available through an API? Yes. Use model \ seedance\ and task type \ seedance-2.5\ through PiAPI's task API, or try the live playground. ## What makes Seedance 2.5 important for AI video? Seedance 2.5 points toward a more controllable production workflow. Longer scene development, richer references, better continuity, and targeted editing could help teams reduce stitching and manual rework. ## Are the reported Seedance 2.5 specifications confirmed for PiAPI? Yes. PiAPI supports 4-15 second output, 480p or 720p resolution, three generation modes, up to 3 video and 3 audio references, and optional generated audio. ## When is the Seedance 2.5 release date? Seedance 2.5 is available on PiAPI now. ## Is Seedance 2.5 available on PiAPI? Yes. Seedance 2.5 is live in the PiAPI API and playground. ## How is Seedance 2.5 different from Seedance 2.0? Seedance 2.5 is expected to place more emphasis on longer workflows, multimodal controllability, continuity, and targeted editing. Seedance 2.0 remains the available PiAPI option for current Seedance workflows. ## Can I use Seedance 2.5 for free? New PiAPI accounts can use available trial credits in the playground. Paid usage is $0.30 per output second at 480p and $0.60 per output second at 720p. ## Should I wait for Seedance 2.5 or use Seedance 2.0 now? Use Seedance 2.5 for the latest single task type and multimodal workflow. Choose Seedance 2.0 when you need its fast, mini, 1080p, or less-restriction variants. ## Try Seedance 2.5 On PiAPI Seedance 2.5 is worth watching because it points toward longer, more controllable, and more editable AI video workflows. For developers, the right move is to separate expected model direction from confirmed integration details. Open the Seedance Workspace to test the live Seedance 2.5 model, then use the same task contract in your application. Seedance 2.0 remains available for workflows that need its additional variants. - Primary next step: Open the canonical Seedance 2.5 model page and use its live playground or API entry points. - Build today: Generate with Seedance 2.5 in the PiAPI Workspace . For legacy variants, explore Seedance 2.0 on PiAPI . ## Sources And Further Reading - PiAPI Seedance 2.5 update page - ByteDance Seedance 2.0 page - BytePlus Seedance 2.0 API reference ## How to Create a Korean Baseball AI Trend Video From Your Photo Use an AI sports video generator to turn a photo into a Korean baseball-style image, review it, then animate it into an AI baseball trend video. An AI sports video generator can turn a portrait into a Korean baseball-style fan-cam image, then animate that approved image into a short AI baseball video. The useful part is the two-step workflow: generate the still image first, check the face and baseball styling, then create the final video only when the image is worth animating. If you want to try the workflow directly, open the AI Sports Video Generator and choose Korean Baseball mode, or go straight to the Korean Baseball AI Video Generator . ## Quick Answer: How Do You Create a Korean Baseball AI Video? Direct answer: To create a Korean baseball AI trend video, upload a clear portrait, choose Korean Baseball mode, generate a Korean baseball-style fan-cam image, review the still result, then animate the approved image into a short AI baseball video. - Upload a clear portrait photo. - Choose Korean Baseball mode. - Select a team-inspired or color direction. - Generate the baseball fan-cam image. - Review the still image before video. - Animate the approved image into a short sports clip. This workflow is better than jumping straight to video because most quality issues are visible in the starting image. If the face, jersey direction, or stadium scene is wrong, regenerate the image before moving on. ## Key Takeaways - The Korean baseball AI trend works best as photo to image to video, not one-click video generation. - A clear portrait improves identity preservation, outfit quality, and final video stability. - Team-inspired styling should be treated as creative direction, not official jerseys, logos, league marks, or broadcaster assets. - The embedded demo below is for the still-image step. Use the full generator when you are ready to animate. ## Try Step 1: Generate a Korean Baseball-Style Image Use the embedded demo below to test the first step inside this article. Upload a portrait and generate a Korean baseball-style image with stadium lighting, baseball jersey direction, and a sports broadcast composition. ## What Is an AI Sports Video Generator? An AI sports video generator turns a photo into a sports-themed image and then animates that image into a short video. For Korean baseball content, the still image usually carries the most important decisions: face preservation, jersey-inspired styling, stadium background, crowd energy, and broadcast-style framing. Quotable definition: An AI sports video generator is a photo-to-video workflow that first creates a sports-themed still image from a portrait, then uses that approved image as the starting frame for a short AI-generated video. On PiAPI, Korean Baseball mode sits inside the broader AI sports video generator. The mode is built for baseball-inspired fan visuals: jersey-style clothing, stadium lighting, crowd context, and a polished fan-cam feel. ## What Is the Korean Baseball AI Trend? The Korean baseball AI trend is a social visual style where a portrait is transformed into a baseball fan-cam or player-photo look. The output often combines a clean face, team-inspired colors, jersey-style clothing, stadium lighting, and a sports broadcast mood. Direct answer: The Korean baseball AI trend turns a portrait into a Korean baseball-style fan image, often with team-inspired colors, a jersey look, stadium atmosphere, and a broadcast-style frame. It is a creative style, not official team or league branding. You may also see people search for Korean baseball ai , Korean Baseball Video , AI Baseball Video , or baseball AI video generator . In practice, those searches point to the same need: create a baseball-style still image from a photo, then animate it. ## How the Korean Baseball AI Video Workflow Works ## Step 1: Upload a Clear Portrait Start with a portrait where the face is large enough to recognize. A front-facing or slightly angled photo usually works better than a distant full-body image. The model needs enough facial detail to preserve the person while changing the scene into a baseball fan-cam moment. Good input photos usually have: - one main person - a visible face - balanced lighting - minimal blur - no heavy sunglasses or face covering - enough detail for identity preservation ## Step 2: Generate the Korean Baseball-Style Image Next, generate the still image. The goal is to create a Korean baseball-style scene with a jersey-inspired outfit, stadium atmosphere, baseball fan energy, and a frame that can become a video. Before animation, check: - whether the face still looks like the source person - whether the baseball outfit direction fits the intended style - whether the background looks like a natural stadium or fan-cam scene - whether any scoreboard or broadcast-style detail is too distracting - whether the image is strong enough to animate ## Step 3: Review the Image Before Video This review step is the quality-control layer. If the still image is weak, video animation usually will not fix it. Regenerate the image first if the face changed too much, the jersey-style direction is off, or the sports frame feels cluttered. ## Step 4: Animate the Image Into an AI Baseball Video Once the still image looks right, use it as the video input. The video step should keep the approved frame while adding subtle camera motion, facial movement, and crowd energy. For social posts, a short polished clip is usually more useful than a long video. ## Korean Baseball AI Template and Jersey-Style Tips A Korean baseball AI template should give the model enough direction without forcing exact protected marks. The safer wording is style-based: Korean baseball-style, team-inspired colors, baseball jersey look, stadium fan-cam, and broadcast-style lighting. Template definition: A Korean baseball AI template is a reusable prompt direction for turning a portrait into a Korean baseball-style fan-cam image, usually covering face preservation, jersey-style clothing, stadium background, and broadcast composition. A good template direction usually asks the model to: - preserve the subject identity - use baseball jersey-style clothing - add stadium lighting and crowd context - keep the face clean and natural - use team-inspired colors only as creative direction - avoid promising exact official jerseys, logos, or league marks ## AI Jersey Generator vs AI Baseball Video Generator Some people search for an ai jersey generator or baseball jersey generator when they actually want the whole trend video. The difference is scope. Short comparison: An AI jersey generator focuses on outfit styling. An AI baseball video generator handles the full creative workflow: portrait input, baseball-style still image, review, and final animated video. Search intent Best match Output AI jersey generator Outfit or jersey-style image Still image AI baseball video generator Image-to-video sports workflow Short video Korean baseball AI trend Portrait to fan-cam image to video Trend-ready image and video ## Troubleshooting Korean Baseball AI Results If the first result is not ready, diagnose the still image before trying video. Most issues come from the source photo, the amount of face detail, or a style direction that is too specific. - If the face changes too much, use a brighter portrait with the face larger in frame. - If the outfit looks too generic, choose a clearer team-inspired color direction. - If the background feels cluttered, regenerate the still image before video. - If the video looks unstable, pick a cleaner still image with fewer small text details. ## Try the Korean Baseball AI Trend Video Generator Ready to make the full clip? Open the AI Sports Video Generator, choose Korean Baseball mode, upload your portrait, generate the still image, then animate the approved result. For the best first test, use a clean portrait and treat Korean baseball or KBO-style terms as creative direction. The output can be useful for social content, examples, and trend testing, but it should not be presented as official league, team, broadcaster, or licensed merchandise content. ## FAQ ## How to Create a FIFA World Cup AI Video From Your Photo Create a FIFA World Cup-style AI video from your photo. Learn the photo-to-fan-cam image workflow, video animation step, prompt tips, and how to try it on PiAPI. A FIFA World Cup AI video generator lets you turn a portrait into a World Cup-style football fan-cam image, then animate that image into a short AI video. Instead of trying to generate the final video in one step, PiAPI uses a guided photo-to-image-to-video workflow so you can review the football fan-cam image before creating the final clip. If you want to try the workflow directly, open the FIFA World Cup AI Video Generator and choose the FIFA World Cup mode. ## Quick Answer: How Do You Create a FIFA World Cup AI Video? Quick answer: To create a FIFA World Cup AI video, upload a clear portrait, choose FIFA World Cup mode, select the country style you want, generate a World Cup-style fan-cam image, review the result, then animate the approved image into a short football AI video. The basic workflow is: - Upload a clear portrait photo. - Choose FIFA World Cup mode. - Select the country style you want. - Generate a World Cup-style fan-cam image. - Review the still image. - Animate the approved image into a short AI video. This two-step flow gives you more control because you can regenerate the still image before spending time or credits on video generation. ## Key Takeaways - A FIFA World Cup AI video generator works best as a two-step workflow: create the fan-cam image first, then animate the approved image. - The most important input is a clear portrait with a visible face, balanced lighting, and one main subject. - Country style selection helps guide the visual direction, but it should be treated as creative inspiration rather than official jersey or logo generation. - The embedded blog demo is for Step 1 only. Use it to test the World Cup-style image, then open the full generator when you are ready to create the video. ## Try Step 1: Generate a World Cup-Style Image Use the embedded demo below to test Step 1 inside this article. Upload a portrait, select the country style you want, and generate a World Cup-style football fan-cam image. The blog demo stays compact and focuses only on the image-to-image step. ## What Is a FIFA World Cup AI Video Generator? A FIFA World Cup AI video generator turns a user photo into a World Cup-style football fan-cam image, then animates that approved image into a short AI video. For this article, the useful distinction is control: the still image is generated first, so you can check the face, country styling, and stadium scene before creating motion. Definition: A FIFA World Cup AI video generator is a photo-to-video workflow that first transforms a portrait into a World Cup-style football fan-cam image, then uses that image as the starting frame for a short AI-generated video. On PiAPI, the FIFA mode is part of the broader AI sports video generator. The tool is designed for World Cup-style, football-inspired fan visuals: stadium lighting, national-team-inspired colors, crowd energy, and broadcast-style framing. ## How the Two-Step World Cup AI Video Workflow Works The PiAPI workflow separates image creation from video animation. That may feel like one extra step, but it solves a real creative problem: video generation is slower and more expensive than reviewing a still image. ## Step 1: Upload a Portrait Photo Start with a clear portrait where the face is visible. A front-facing or slightly angled photo usually works better than a distant full-body shot. The tool needs enough facial detail to preserve the subject while changing the scene into a football fan-cam moment. Good input photos usually have: - a visible face - balanced lighting - minimal motion blur - no heavy sunglasses or face covering - one main subject - enough image detail for identity preservation ## Step 2: Generate a World Cup-Style Fan-Cam Image After the photo is uploaded, select the country style you want to guide the jersey colors, supporter styling, flags, and fan merchandise direction. Then generate the World Cup-style fan-cam frame. The result should look like the subject is sitting in a packed football stadium, wearing national-team-inspired colors, surrounded by crowd energy and broadcast-style visual cues. Before you animate anything, check: - whether the face still looks like the source person - whether the jersey or country styling feels appropriate - whether the stadium background looks natural - whether the broadcast-style overlays are not too distracting - whether the image is strong enough to become a video ## Step 3: Review the Image Before Video This is the main advantage of the two-step workflow. If the still image is not good enough, you can adjust the country style or try a better photo before moving to video. For many AI video workflows, the starting frame shapes the entire clip. A clean still image gives the video model a stronger reference for face, clothing, lighting, and background. A weak still image can make the final video harder to control. ## Step 4: Animate the Image Into a Football AI Video Once the still image looks right, use it as the input for the video step. The animation turns the approved fan-cam image into a short football AI video with subtle motion, natural facial movement, and a live-broadcast feeling. The goal is not to replace real sports footage. The goal is to create a short social-style AI fan-cam clip from your own portrait. ## How to Make a Football AI Video From a Photo If you are searching for a football AI video or an AI football video generator, the same workflow applies: start with a photo, create the football-themed image, then animate the image into a video. Short answer: To make a football AI video from a photo, use the photo as a reference image, generate a football-themed still image first, then animate that still image into a short video. This works best when the still image already has the right face, outfit direction, and stadium scene. The FIFA World Cup angle is more specific than a generic football video generator because it focuses on a fan-cam look: national colors, stadium lighting, crowd atmosphere, and a televised sports frame. For soccer AI video searches, the intent is similar. Some users call the sport football, while others search for soccer AI video or soccer AI video generator. In this article, football and soccer refer to the same style of fan-cam AI video workflow. If you want the baseball version of this workflow, see the Korean Baseball AI trend video guide , which uses the same review-before-video pattern for KBO-style fan-cam content. ## FIFA World Cup AI Video Prompt Tips You do not need to write a full football AI video prompt from scratch when using PiAPI's guided FIFA mode. The tool uses preset image and video prompts so the workflow is easier for beginners. A good World Cup-style prompt direction usually asks the model to: - preserve the subject's identity - use national-team-inspired colors - place the person in a packed football stadium - use realistic night-match lighting - create a broadcast-style fan-cam frame - keep overlays subtle and believable - avoid adding new text or distracting graphics during animation Use style language as inspiration, not a guarantee of official branding. Safer terms include World Cup-style , country-inspired , national-team-inspired , and football fan-cam . Avoid asking for exact official kits, logos, tournament marks, or official team branding. AI-generated outputs can vary, and the tool is not an official FIFA, federation, club, or tournament asset generator. ## Best Photos for World Cup-Style AI Videos The best input for a World Cup AI photo generator or video generator is a clean portrait. The model needs enough visual information to preserve the face while changing the outfit, background, lighting, and sports setting. Use this checklist before generating: - Choose a clear portrait with one main person. - Keep the face visible and not heavily cropped. - Avoid heavy blur, extreme filters, or dark shadows. - Use a photo where the subject is looking toward the camera or slightly off-camera. - Avoid crowded group shots for the first test. - Use a portrait crop if you want a stronger fan-cam result. If the first image looks unstable, try a brighter photo or one where the face is larger in the frame. A better input photo usually improves both the still image and the final football AI video. ## Can You Choose a Country Style? Yes. The FIFA World Cup mode is designed around country style selection or random national-team styling. That means you can guide the look toward a country-inspired football fan style, or leave it random if you want the tool to choose the direction. Direct answer: Yes, you can choose a country style to guide colors, supporter styling, and fan-cam mood. The result should be understood as country-inspired creative styling, not as official kit, federation, or tournament branding. Use this feature for: - country-inspired colors - football supporter styling - fan merchandise direction - stadium atmosphere - national-team-inspired visual mood This does not mean the output will reproduce official jerseys, logos, or tournament marks exactly. Treat the country style as creative direction, not official merchandise generation. ## Why Review the Image Before Generating the Video? Reviewing the image first helps you avoid wasting video generation on a weak starting frame. If the World Cup-style image does not preserve the face, country styling, or stadium look well, the final video usually will not fix those issues. That is why this blog lets you test Step 1 first. Generate the fan-cam image here, choose the result you like, then open the full FIFA World Cup AI Video Generator to animate it. ## Try the FIFA World Cup AI Video Generator If you have a portrait ready, you can try the FIFA World Cup AI Video Generator on PiAPI. Upload your photo, choose the FIFA World Cup mode, generate the fan-cam image, then animate the approved image into a short AI sports video. For the best first test, use a clear portrait and start with a country style that matches the kind of football fan-cam look you want. If the still image is not right, regenerate the image before moving to video. ## FAQ ## How to Use Seedance Private Assets with the Seedance API Learn how Seedance private assets work in the Seedance API. Upload reusable face, character, or product references and generate videos with asset IDs. Seedance Private Assets API lets developers upload a face, character, product, scene, video, or audio reference once, wait for Active , and reuse it in future Seedance video tasks with asset://<asset_id> . Use this workflow when you need consistent AI video characters, recurring product references, or reusable brand assets across multiple Seedance API generations. This guide shows the asset upload flow, Active status check, supported task types, and cURL examples for using private assets in production. ## Key Takeaways - Use Seedance private assets when a face, character, product, or other subject needs to appear across multiple future video generations. - Use raw URLs for one-time scenery, backgrounds, props, or reference images that do not need long-term consistency. - Use auto_upload_assets: true when a one-off user-provided reference should be temporarily ingested for a single task. - Private assets must reach Active before you reference them in a Seedance task. - asset:// references work with seedance-2-less-restriction and seedance-2-fast-less-restriction , not the strict Seedance task types. ## Quick Answer Seedance private assets are best for reusable references in the Seedance API. Upload the reference once, wait for Active , then call it in future video tasks with asset://<asset_id> . Use them for recurring faces, characters, products, scenes, video clips, or audio references. ## What Are Seedance Private Assets? Seedance private assets are reusable files that your application uploads to PiAPI before video generation. After upload, PiAPI returns an asset_id . Once the asset becomes Active , your Seedance API requests can reference it with this format. For endpoint details, quotas, and lifecycle rules, use the Private Asset Library docs . ``` asset:// ``` Instead of passing the same face photo, character image, product shot, video clip, or audio reference again and again, you upload it once and reuse the private asset ID across multiple tasks. This is especially useful for backend applications that need a stable library of reusable references. For example, an AI video app might store a main character, recurring spokesperson, product catalog item, or brand mascot as a managed asset. Future Seedance video generations can then reference the same asset without re-uploading and re-processing it on every request. The important mental model is simple: A private asset is a reusable reference for Seedance video generation. It is not a separate image editor. It is not a Playground-only mode. It is mainly an API workflow for developers building repeatable video generation systems. Definition: Seedance Private Asset Library is PiAPI's managed asset workflow for Seedance. It lets developers upload reusable reference files, verify that each asset is Active , and reference those files in later Seedance video tasks with asset://<asset_id> . ## What Problem Do Private Assets Solve? AI video generation often struggles when a user wants the same subject to appear across multiple prompts, scenes, or creative variations. A raw image URL can help for one task, but it is not ideal when the same subject needs to be reused many times. Seedance private assets solve this workflow problem by giving developers a reusable reference layer. For a broader model overview before going deeper into private assets, start with the Seedance 2.0 API guide . Common use cases include: - keeping the same person or character consistent across several generated videos; - building avatar, creator, or influencer-style video products; - reusing a product reference across ad creative variations, such as the Seedance AI marketing video workflow ; - keeping a stable cast for short films or story-driven scenes; - combining a reusable person or product with changing backgrounds; - reducing repeated upload and ingestion work in backend systems. For example, if a user uploads a photo of themselves and wants to generate many videos with that person in different scenes, the face or person reference can become a managed private asset. Each new scene can still be passed as a normal image URL if it is only used once. ## What Private Assets Are Not This part matters because the feature can sound like image editing at first. Seedance Private Asset Library should not be described as classic face swap, inpainting, or exact photo compositing. If you upload a photo of a person and a scenic background, the Seedance task uses those inputs as generation references. It does not guarantee that the person will be inserted into the background with pixel-level control. A better description is: Seedance private assets enable reference-guided video generation with reusable subjects. So yes, you can upload a face or person reference and use it in later Seedance video tasks. But the expected result is generated video guided by that reference, not a precise edit of an existing photo. This distinction helps set the right expectation for developers and end users. If your product needs exact image replacement, masking, or face-swap behavior, a dedicated image editing or face swap API may be a better fit. If your product needs reusable people, characters, products, or scenes in generated videos, private assets are the right Seedance workflow to evaluate. ## Private Assets vs Raw URLs vs Auto-Upload Mode Seedance private assets are one part of a larger reference workflow. In practice, most applications will use a mix of managed private assets, raw URLs, and auto-uploaded ephemeral assets. Pattern Best for Example input When to use Managed private asset Reusable people, characters, products, or recurring subjects asset://asset-123 Use when the same reference will appear in multiple future tasks. Raw URL One-time scenery, background, prop, or ambient reference https://your-cdn.com/scene.jpg Use when the reference only matters for the current task and does not need asset lifecycle management. Auto-uploaded ephemeral asset One-off user references that should be temporarily ingested Raw URL plus auto_upload_assets: true Use when the reference is a one-time subject or character and you want PiAPI to handle temporary ingestion. Managed cast + raw scenery Stable person or product with changing backgrounds asset://person , https://.../scene.jpg Use when the subject is recurring but the background is not. Managed cast + ephemeral guest Stable main character with one-time guest reference asset://main , raw URL, auto_upload_assets: true Use when one reference is permanent and another should be temporary. The simplest rule: If a reference will be reused across future tasks, make it a private asset. If it is only needed once, keep it as a raw URL or use auto-upload mode. ## When Should You Use Each Input Method? Use this decision rule when designing a Seedance API workflow: If your reference is... Use this method Why A recurring person, character, product, scene, video, or audio file Private asset It can be reused with asset://<asset_id> across future tasks. A one-time background, prop, or scene image Raw URL It avoids unnecessary asset management. A temporary user upload for one task Auto-upload mode PiAPI can ingest it briefly without making it a long-term managed asset. A reusable person plus a changing background Private asset + raw URL Keep the person stable while changing the scene per task. ## How the Seedance Private Asset Workflow Works The private asset flow has five parts: - Upload the reusable reference. - Wait for the asset status to become Active . - Submit a Seedance task with asset://<asset_id> in image_urls , video_urls , or audio_urls . - Refer to the inputs in the prompt as Image 1 , Image 2 , Video 1 , or Audio 1 . - Poll the Seedance task until the output video is complete. The prompt reference order matters. If your image_urls array has a private asset first and a scenic background second, then Image 1 means the private asset and Image 2 means the background URL. For example: ``` { "image_urls": [ "asset://asset-20260607154123-aaaa1", "https://your-cdn.com/scenic-background.jpg" ] } ``` In the prompt, you can write: ``` Image 1 is the person reference. Image 2 is the scenic background. ``` That makes the request easier for Seedance to interpret and easier for developers to debug. ## Example: Use a Person Asset with a Scenic Background A common question is: Can I upload a photo of myself, use a scenic photo, and ask Seedance to add me into the scene? The practical answer is yes, with the right expectation. You can upload the person photo as a private asset, pass the scenic photo as a raw URL, and prompt Seedance to generate a video using both references. Direct answer: Yes, you can upload a photo of a person as a Seedance private asset and combine it with a scenic image URL in a Seedance task. The private asset acts as the person reference, while the scenic URL acts as the scene reference. The result is generated video, not exact photo compositing. In that setup: - Image 1 is the uploaded person or face reference. - Image 2 is the scenic image URL. - The prompt describes how the person should appear in the scene. - The result is reference-guided video generation, not exact photo compositing. This is the pattern: ``` { "image_urls": [ "asset://asset-person-reference", "https://your-cdn.com/scenic-background.jpg" ], "prompt": "Image 1 is the person reference. Image 2 is the scenic background. Generate a cinematic 5-second video of the person from Image 1 standing naturally in the location from Image 2." } ``` For many AI video products, this is the right workflow. The person can be reused across future tasks, while the scene can change each time. ## API Example: Upload and Use a Seedance Private Asset The following examples show the basic cURL flow. Replace URLs, asset IDs, and API keys with your own values. ## Step 1: Upload a Private Asset Use this request to upload a reusable image reference, such as a face, person, character, product, or scene. The source URL must be publicly reachable while PiAPI fetches and ingests the file. Keeping it reachable for at least 24 hours is the safest default. ``` curl --request POST "https://api.piapi.ai/api/v1/asset/upload" \ --header "X-API-Key: $PIAPI_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "url": "https://your-cdn.com/person-reference.jpg", "asset_type": "Image", "name": "main-character" }' ``` The response returns an asset_id and an initial status such as Processing . ``` { "asset_id": "asset-20260607154123-aaaa1", "status": "Processing", "upload_at": "2026-06-07T15:41:23Z", "expires_at": "2026-06-22T15:41:23Z" } ``` ## Step 2: Check Asset Status Before using the asset in a Seedance task, poll until the asset becomes Active . ``` curl --request GET "https://api.piapi.ai/api/v1/asset/list?status=active,processing,failed" \ --header "X-API-Key: $PIAPI_API_KEY" ``` If the asset is still Processing , wait and check again. If the asset is Failed , inspect the error message and upload a corrected file. ## Step 3: Submit a Seedance Task with asset:// Once the asset is Active , reference it in the Seedance task with asset://<asset_id> . ``` curl --request POST "https://api.piapi.ai/api/v1/task" \ --header "X-API-Key: $PIAPI_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "seedance", "task_type": "seedance-2-less-restriction", "input": { "prompt": "Image 1 is the person reference. Image 2 is the scenic background. Generate a cinematic 5-second video of the person from Image 1 standing naturally in the location from Image 2.", "image_urls": [ "asset://asset-20260607154123-aaaa1", "https://your-cdn.com/scenic-background.jpg" ], "aspect_ratio": "16:9", "duration": 5, "resolution": "720p" } }' ``` This example uses seedance-2-less-restriction , which supports asset:// references. You can also use seedance-2-fast-less-restriction for the fast variant. Strict task types such as seedance-2 and seedance-2-fast do not support private asset references. See the Seedance less-restriction mode and Seedance 2.0 API docs for the full task reference. After submission, poll the task endpoint until the status becomes completed . When the task completes, download or store the output video promptly because generated video URLs are temporary. ## Auto-Upload Mode for One-Off References Private assets are best for reusable references. If a reference is only needed for one task, use auto-upload mode instead. With auto_upload_assets: true , PiAPI can temporarily ingest non- asset:// URLs in the request, use them for the task, and clean them up after the retention window. ``` curl --request POST "https://api.piapi.ai/api/v1/task" \ --header "X-API-Key: $PIAPI_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "seedance", "task_type": "seedance-2-less-restriction", "input": { "prompt": "Image 1 is a one-time character reference. Generate a 5-second cinematic video with consistent appearance.", "image_urls": [ "https://your-cdn.com/one-time-character.jpg" ], "auto_upload_assets": true, "asset_retention_hours": 3, "aspect_ratio": "16:9", "duration": 5, "resolution": "720p" } }' ``` This is useful for on-demand user uploads, one-off guest characters, A/B variants, or references that should not be stored as part of a long-term asset library. Auto-upload mode also requires a less-restriction task type. asset_retention_hours defaults to 3 and can be set within the supported retention window when you need short follow-up reuse. ## Best Practices for Better Results Use private assets for references that matter across multiple future tasks. A face, main character, product, or recurring brand subject is usually a good private asset candidate. Keep one-time backgrounds as raw URLs. If a scenic image only appears in one task, it usually does not need to become a managed private asset. Use clear prompt labels. Instead of saying "put this person in that image," write prompts that refer to Image 1 , Image 2 , Video 1 , or Audio 1 based on the order of the input arrays. Wait for Active before generation. New uploads begin in Processing , and tasks should not reference the asset until it is ready. Use the supported less-restriction task types. For private assets, use seedance-2-less-restriction or seedance-2-fast-less-restriction . Plan your asset lifecycle. Private assets have plan-based quotas and TTL behavior, so applications should list, refresh, and delete assets intentionally. Use consented or owned references. If your app allows face, person, or character uploads, make sure users have the right to use those inputs and understand how long assets may be retained. ## Common Mistakes ## Mistake 1: Using a Strict Task Type This will not work with private asset references: ``` { "model": "seedance", "task_type": "seedance-2", "input": { "image_urls": [ "asset://asset-20260607154123-aaaa1" ] } } ``` Use seedance-2-less-restriction or seedance-2-fast-less-restriction instead. ## Mistake 2: Referencing the Asset Too Early Do not upload an asset and immediately submit a Seedance task before checking status. Use this flow: ``` Upload asset -> poll asset status -> wait for Active -> submit Seedance task ``` ## Mistake 3: Uploading Every Background as a Managed Asset Not every image should become a private asset. If a background, prop, or ambient image is only used once, pass it as a raw URL. Save private asset slots for references that need consistency across tasks. ## Mistake 4: Treating Private Assets as Exact Image Editing Private assets guide video generation. They do not guarantee pixel-perfect insertion of a person into a photo. Set the product expectation around reusable references and character consistency, not exact editing. ## Mistake 5: Letting the Source URL Disappear Too Soon PiAPI needs to fetch and ingest the uploaded URL asynchronously. If the URL expires, requires authentication, or becomes unreachable too soon, the asset may fail. Use a stable public URL during ingestion. ## FAQ ### What is a Seedance private asset? A Seedance private asset is a reusable reference uploaded to PiAPI for Seedance video generation. Once the asset becomes Active , you can reference it in future tasks with asset://<asset_id> instead of passing the same raw file URL repeatedly. ### Can I upload a face photo and reuse it in Seedance? Yes. You can upload a face or person photo as a private asset and reference it in Seedance video generation. This is useful for reusable person or character workflows, but it should be treated as reference-guided generation rather than guaranteed face swap or exact image editing. ### Is Seedance Private Asset Library the same as face swap? No. Seedance Private Asset Library is not the same as a face swap tool. It lets Seedance use uploaded assets as reusable generation references. The goal is character or subject consistency in generated video, not exact replacement of one face in an existing image or video. ### Can I combine a private person asset with a scenic image? Yes. A common pattern is to put the person asset first in image_urls and the scenic image URL second. Then prompt with labels like Image 1 is the person reference and Image 2 is the scenic background so Seedance understands the role of each input. ### Which Seedance API task types support private assets? Private assets work with seedance-2-less-restriction and seedance-2-fast-less-restriction . Strict variants such as seedance-2 and seedance-2-fast do not support asset:// references and should not be used for this workflow. ### What is the difference between private assets and auto-upload mode? Private assets are managed references that you upload once and reuse across tasks. Auto-upload mode temporarily ingests raw URLs for one-off tasks when auto_upload_assets: true is set. Use private assets for recurring references and auto-upload for temporary user-provided inputs. ### Can I test private assets in the Playground? Private assets are mainly an API and backend workflow. Unless the Playground exposes asset upload, asset status, asset:// references, and less-restriction task type selection, testing is usually done with cURL, Postman, or backend code. ## Conclusion Seedance Private Asset Library gives developers a cleaner way to build reusable reference workflows with the Seedance API. Instead of passing the same face, character, product, or scene file into every request, you can upload it once, wait for it to become Active , and call it later with asset://<asset_id> . The best use case is not exact image editing. It is consistent, reference-guided video generation for recurring people, characters, products, and creative subjects. To start, upload one reusable reference, confirm it is Active , and submit a seedance-2-less-restriction task that uses the asset alongside your prompt and any one-time raw URLs. You can test Seedance from the Seedance workspace when you are ready to connect an API key. Sources: - PiAPI Private Asset Library docs: https://piapi.ai/docs/seedance-api/seedance-2 - Seedance 2.0 arXiv page: https://arxiv.org/abs/2604.14148 ## AI Kissing Video from Image: Examples of What Different Photos Generate See AI kissing video examples from different image types, including couple photos, character images, wedding portraits, casual selfies, and low-light beach photos. An AI kissing video from image can look very different depending on the photo you upload. A clear close-up of two people may create a smoother result, while a casual selfie, stylized character image, or low-light photo may require more pose conversion from the model. This guide shows several AI kiss video examples from different image types so you can understand what to expect before generating your own clip. The examples are designed around the kinds of photos people actually try: romantic close-ups, casual selfies, wedding portraits, anime-style images, and low-light beach photos. If you already have a two-person image ready, you can create an AI kiss video from your image with the PiAPI Kling AI Kiss Generator. Quick answer: An AI kissing video from image usually works best when the image already contains two clear, close subjects. In the examples below, romantic close-ups, wedding portraits, casual selfies, anime-style images, and even a low-light beach photo all produced usable outputs when the faces and subject placement were readable. ## Key Takeaways - An AI kissing video from image works best when both faces are clear, close, and easy to identify. - Romantic close-ups and wedding-style portraits usually give the model a cleaner scene to animate. - Casual selfies can work, but camera angle, crop, and lighting can affect stability. - Anime and character images can create interesting results, but stylized faces may behave differently from realistic photos. - Low-light or flash-lit photos can still work when the couple is close together and the faces remain readable. ## What Is an AI Kissing Video from Image? An AI kissing video from image is a short generated video where an AI model turns a still image into a kiss-style motion effect. Instead of filming a real video, the user uploads an image and the model animates the people or characters in that scene. Definition: An AI kissing video from image is an image-to-video output where a model animates a still image into a short kiss-style scene. The result depends heavily on the uploaded image, especially face visibility, subject distance, lighting, and whether the intended pair is easy to identify. For best results, the source image usually needs clear visible faces, stable framing, and enough space for the model to move the subjects naturally. The image does not need to be professionally shot, but the model has an easier time when the faces are not cropped, hidden, heavily blurred, or lost in poor lighting. The PiAPI Kling AI Kiss playground uses a preset kiss effect, so users do not need to write a prompt. The basic workflow is simple: upload a suitable image, generate the video, and review the result. ## Which Photo Types Worked Well? In this test, every photo type produced a usable AI kiss video. The strongest pattern was not one specific image style, but whether the two subjects were close together, easy to identify, and readable enough for the model to animate. ## AI Kissing Video Examples from Different Image Types The examples below are organized by input type. Each section explains what the source image gives the model, how the generated video behaves, and what that example teaches about creating an AI kiss video from a photo. If a low-light result looks unstable, compare your image with the AI kiss generator troubleshooting guide . ## Do You Need One Image or Two Photos? For this kind of AI kissing video from image workflow, the simplest input is usually one image that already contains two visible people or characters. A single shared image gives the model matching lighting, scale, background, and subject placement. Some users search for two-photo AI kiss generators, but upload rules vary by tool. For PiAPI Kling AI Kiss, follow the playground upload flow and choose the clearest two-subject image you have. ## What These AI Kiss Video Examples Show The examples are not just a showcase. They are meant to help you choose better input images before using an AI kiss generator. Main finding: The most reliable AI kiss video examples came from images where the model could clearly identify two subjects and understand their relationship in the frame. Subject placement mattered as much as image style: a clear selfie or low-light couple photo could still work when the people were close together. The clearest pattern is that face visibility matters most. When both faces are large, sharp, and unobstructed, the model has more useful detail to preserve during motion. Romantic close-ups and wedding portraits often work well because the subjects are already positioned in a way that supports the kiss effect. Lighting is another major factor. Bright but natural lighting usually gives the model a cleaner source image. Very dark scenes, harsh flash, heavy filters, and strong blur can still generate interesting results, but they are more likely to produce unstable faces or unnatural transitions. Composition also matters. Two people close together, facing each other, and placed near the center of the frame usually create a clearer setup than crowded scenes or distant full-body shots. Stylized images can work too, but the result should be judged by style consistency rather than pure realism. ## Try Your Own AI Kiss Video from Image If you want to test your own image, open the PiAPI Kling AI Kiss Generator , upload a two-person image, and generate the video from the preset effect. For the smoothest first test, choose an image where both people are clearly visible, close enough to each other, and not heavily cropped. If the first result looks unstable, try a brighter image, a simpler background, or a photo where both faces are easier to see. For a broader overview of the tool, read the AI Kiss Generator guide . ## FAQ ## Why Your AI Face Rating Changes: Photo Tips for More Consistent Face Scores AI face ratings can change based on lighting, camera angle, expression, filters, blur, and face visibility. Learn the best selfie tips for more consistent AI face scores. AI face rating changes can feel confusing. You upload one selfie, get a face score, upload another photo later, and the result changes. That does not mean the AI found a fixed "true" score for you and then changed its mind. Most of the time, the score changes because lighting, camera angle, expression, blur, filters, or face visibility gave the AI different visual information. Quick answer: AI face ratings can change because the AI is analyzing the uploaded image, not a fixed personal identity score. Lighting, camera angle, expression, blur, filters, face coverage, and image clarity can all affect the report. For more consistent face scores, upload clear, front-facing selfies under similar lighting and compare reports from similar photo conditions. This guide explains why AI face rating scores change, what AI face rating accuracy can and cannot tell you, and how to choose a better selfie for a more consistent face rating. If you want the broader background on score language, see our guide to what AI face rating and PSL scores mean . If you are ready to test a clearer selfie, you can use PiAPI's AI Face Rater . ## AI Face Rating Is Image-Dependent, Not a Fixed Identity Score An AI face rating tool can only analyze what is visible in the uploaded image. It does not see your face in every possible lighting condition, from every angle, or across every expression. It receives one photo and generates a report from that specific input. That is why two photos of the same person can produce different face scores or different category notes. One image may show the full face clearly. Another may be dim, tilted, filtered, cropped, or partially covered. The AI is not comparing your entire identity across all real-world contexts. It is reacting to one image at a time. The healthiest way to read an AI face score is as photo and appearance feedback. It can be useful for noticing image clarity, expression, face visibility, and profile-photo presentation. It should not be treated as a measure of personal worth, health, identity, personality, or objective attractiveness. ## Lighting: Why Dark or Uneven Light Changes Face Scores Lighting is one of the easiest reasons an AI face rating can change. A clear selfie with soft, even light gives the AI more visible detail to work with. A dark selfie, harsh side light, or strong backlight can hide parts of the face and change how the report reads visible features. Low light can blur edges around the eyes, jawline, cheekbones, and face contour. Harsh lighting can create shadows that make one side of the face look heavier or less balanced. Backlighting can make the face less readable because the camera exposes for the background instead of the face. For a more consistent AI face rating, use light that is: - Soft and even - In front of you, not behind you - Bright enough to show facial features clearly - Similar between photos if you want to compare reports ## Camera Angle: Why Low, Tilted, or Close-Up Selfies Affect Ratings Camera angle can also change an AI face score. A selfie taken at eye level usually gives the AI a more straightforward view of facial balance, proportions, and symmetry. A low-angle selfie, high-angle selfie, tilted selfie, or very close selfie can distort the way features appear. A low angle can emphasize the lower face and jawline. A high angle can make the forehead and eyes appear larger relative to the lower face. A tilted photo can make left-right balance harder to read. A very close selfie can stretch the center of the face and change perceived spacing between features. If you are comparing two face rating reports, the angle matters. A front-facing eye-level selfie and a low-angle selfie are not equivalent inputs. The changed report may reflect the changed camera position, not a stable difference in the person. ## Example: Why the Same Person Can Get Different Face Scores These two selfies show the same person, but the input conditions are different. The second photo changes lighting and camera angle, so the AI has different visual information to analyze. That can change the report even when the person is the same. In this example, the two reports stay in the same broad rating band, but the score shifts slightly from \ ## AI Face Rating Explained: What Face Scores and PSL Ratings Mean Learn what AI face rating means, how PSL face scores became a social-media trend, what face rating AI tools measure, and how to interpret scores safely. AI face rating has become one of those internet trends that sounds simple at first: upload a face photo, get a score, and see how the AI describes your facial features. But the language around it can get confusing fast. People search for face rating AI tools, PSL ratings, AI PSL ratings, face scores, and "how to rate my face with AI" as if they all mean exactly the same thing. They are closely related, but the context matters. AI face rating is the broader idea. PSL rating and blackpill rating are face rating AI trends that spread through social platforms like TikTok and Instagram , usually with a stronger focus on perceived sexual appeal. Quick answer: AI face rating is the use of AI to analyze a face photo and generate a score or report based on visible facial features such as symmetry, proportions, eye area, jawline, expression, and overall facial harmony. PSL rating is a social-media face-rating term that usually focuses more directly on perceived sexual appeal. This guide explains what AI face rating means, why PSL face scores are trending, what these scores usually measure, and how to interpret them without treating a number as personal worth. ## Key Takeaways - AI face rating is the broad term for using AI to generate a face score or visual report from a photo. - PSL rating and blackpill rating are social-media face scoring terms that spread through platforms like TikTok and Instagram. - PSL and blackpill scores usually focus more directly on perceived attractiveness or sexual appeal. - Face scores are subjective and can change based on image quality, camera angle, expression, and the tool's scoring system. - The healthiest way to use face rating AI is as appearance or profile-photo feedback, not as a measure of personal worth. ## Try the AI Face Rater ## What Is AI Face Rating? AI face rating is a way to use artificial intelligence to review a face photo and return a score, breakdown, or visual report. A face rating AI tool might give one overall number, but stronger tools usually include category-level feedback as well. For example, an AI face score may look at visible features such as: - Facial symmetry - Facial proportions - Eye area - Jawline and chin shape - Cheekbones and facial contour - Expression - Overall facial harmony Different AI face rating tools use different prompts, scoring systems, and visual styles. That means two tools can rate the same image differently. A score is not a universal truth; it is the output of a specific model, photo, and scoring approach. ## What Is a PSL Rating? A PSL rating is social-media language for rating a face, usually in terms of perceived sexual appeal. It is part of the same face rating AI trend that appears across TikTok, Instagram, and other short-form social platforms. In practice, it overlaps heavily with general face rating. People use terms like PSL score, PSL face score, face rating PSL, and AI PSL rating when they want a number or ranking that describes how attractive a face appears. The key difference is the framing. General AI face rating can be broader and more constructive, especially when it focuses on facial features, profile-photo feedback, or visual presentation. PSL rating tends to be more blunt because the trend often reduces a face to a score. That is why it is important to treat PSL-style scores carefully. They can describe how a photo or face is perceived in a certain online context, but they should never be treated as a complete judgment of a person. ## AI Face Rating vs PSL Rating vs Blackpill Rating These terms overlap, but they are usually used with different framing. Term What it means Common context Best way to interpret it AI face rating AI-generated score or report based on visible facial features in a photo. Face rating AI tools, profile-photo feedback, selfie review. Treat it as appearance feedback from one image and one scoring system. PSL rating Social-media face rating term focused on perceived attractiveness or sexual appeal. TikTok, Instagram, looks-focused communities, PSL score discussions. Treat it as subjective trend language, not objective truth. Blackpill rating Face-rating term often used in the same communities as PSL, usually with a more negative tone. Social-media rating content and blackpill/looks discussions. Treat it carefully; the framing can be fatalistic and should not define personal worth. ## Why Are AI Face Ratings and PSL Scores Trending? AI face rating, PSL scores, and blackpill rating are trending because they fit the way TikTok, Instagram, and other social platforms spread quick, visual, score-based content. A face score is easy to share, easy to compare, and easy to turn into a short video, screenshot, or reaction post. There are a few reasons people search for this topic: - They saw a PSL rating trend and want to know what it means. - They want to check how a selfie or profile photo might be perceived. - They are curious about facial symmetry, proportions, or "facial harmony." - They want a glow-up, looksmax, or profile-photo feedback angle. - They are looking for a simple way to rate their face with AI. The AI face rating trend in 2026 is partly entertainment and partly self-review. Some users treat the score as a game. Others use it as feedback before changing a profile photo, dating app picture, creator avatar, or social media image. ## What Does an AI Face Score Usually Measure? An AI face score usually measures visible appearance signals in the uploaded image. The exact criteria depend on the tool, but most face rating systems focus on a similar set of categories. Category What it usually means Facial symmetry Whether the left and right sides of the face appear balanced in the photo. Facial proportions How the spacing and size of visible features relate to each other. Eye area Eye visibility, balance, expression, and visual impact. Jawline and chin Lower-face shape, jaw definition, and chin balance. Cheekbones and contour Facial structure, cheekbone visibility, and contour. Expression Whether the expression looks natural, confident, tense, neutral, or unclear. Overall facial harmony How the visible features work together as a whole. Photo clarity Whether the face is clear enough for the AI to analyze. Photo clarity matters because a blurry, dark, filtered, or heavily angled image can change the result. But photo quality is not the same thing as facial appearance. A better-lit photo may receive a better score because the AI can read the face more clearly, not because the person has changed. ## Example: What an AI Face Rating Report Can Look Like Here is a simple example of the product flow: one input selfie and one output image showing the generated AI face rating report. ## Are AI Face Ratings Accurate? AI face ratings can be useful for quick appearance feedback, but they are not perfectly accurate or objective. Results can change based on lighting, camera angle, expression, image quality, filters, and the scoring system used by the tool. If you want to compare reports more fairly, see our guide to why AI face rating scores change . This is especially true for PSL-style ratings. A PSL face score often tries to compress many subjective signals into one number. That makes the result easy to understand, but also easy to overvalue. The better way to read an AI face rating is to look beyond the headline score. Category notes are usually more useful than the number itself. If a report says the image has poor lighting, a tense expression, or an unclear angle, that is actionable. If it only gives a score with no explanation, it is mostly entertainment. ## How to Interpret a Face Rating Without Overthinking It A face score should never be treated as a measure of personal worth, health, identity, personality, or social value. It is a visual feedback signal, not a statement about who someone is. Use AI face rating as a photo and appearance feedback tool: - Look for patterns across the report, not just the final score. - Treat the result as subjective, especially if it uses PSL language. - Avoid comparing yourself harshly against other people. - Do not use face scores to shame, rank, or harass anyone. - Use your own photos or images you have permission to analyze. If you want practical value from a face rating, focus on the parts you can actually use: clearer photos, better expression, cleaner framing, and a more readable profile image. ## How to Try an AI Face Rating Report If you want to try the face-rating trend with an AI-generated visual report, use PiAPI's AI Face Rater . PiAPI's AI Face Rater is designed to turn a selfie into a visual face rating report. Instead of only returning a number, the report can include a face score, feature ratings, strengths, and improvement notes. Use it as facial feature feedback, not as a final judgment of attractiveness or personal value. For the best result, upload one clear selfie where your face is visible. Avoid sunglasses, masks, heavy filters, group photos, and extreme angles so the AI can read the image more clearly. ## AI Face Rating FAQ ## What is AI face rating? AI face rating is the use of AI to analyze a face photo and generate a score or report based on visible facial features such as symmetry, proportions, eye area, jawline, expression, and overall facial harmony. ## What does PSL rating mean? PSL rating is social-media language for rating a face, usually with a focus on perceived sexual appeal. It is closely related to general face rating, but the framing is often more direct and score-focused. ## Is blackpill rating the same as PSL rating? Blackpill rating and PSL rating are often used in the same online face-rating communities and social-media face rating AI trends. Both usually describe a face score focused on perceived attractiveness or sexual appeal, and both can show up in TikTok or Instagram-style rating content. The difference is mostly framing: PSL sounds like a scoring scale, while blackpill often carries a more fatalistic or negative tone. Either way, the score should be treated as subjective appearance feedback, not personal worth. ## Is PSL rating the same as AI face rating? Not exactly. PSL rating is a specific social-media face-rating trend, while AI face rating is the broader use of AI to score or analyze a face photo. A tool can generate a PSL-style score, but AI face rating can also include more constructive feature feedback. ## What does a PSL face score measure? A PSL face score usually tries to summarize perceived facial attractiveness or sexual appeal. It may consider visible features such as symmetry, facial proportions, jawline, eye area, cheekbones, expression, and overall facial harmony. ## Is AI face rating accurate? AI face rating can provide quick feedback, but it is not perfectly accurate or objective. Lighting, camera angle, image quality, expression, filters, and the tool's scoring system can all affect the result. ## How can I rate my face with AI? You can rate your face with AI by uploading a clear selfie to an AI face rating tool and generating a score or report. For a visual report with feature feedback, try PiAPI's AI Face Rater . ## Can an AI face score judge my personal worth? No. An AI face score should never be treated as a measure of personal worth, identity, health, personality, or social value. It is only a visual feedback signal based on a specific image and scoring system. ## New Working Dance Presets for PiAPI's Kling 2.6 Dance Generator PiAPI's Kling 2.6 Dance Generator now includes new working dance presets while still supporting custom reference video uploads for your own motion style. PiAPI's Kling 2.6 Dance Generator now includes a new set of working dance presets. The update brings preset dance references back into the playground, while keeping the custom reference video option available for users who want to guide motion with their own clip. This blog is a short product update and workflow guide. If you want to create a dance video now, the main playground is still the best place to start: try the Kling 2.6 Dance Generator . Quick answer: PiAPI's Kling 2.6 Dance Generator now supports new working dance presets. You can choose a preset from the dropdown for a ready-made motion reference, or select custom reference video to upload your own dance clip. Definition: A dance preset is a ready-made motion option that guides how an uploaded image should move. In PiAPI's Kling 2.6 Dance Generator, presets help users create image-to-dance videos without preparing a separate reference video first. Update summary: The dance preset feature was temporarily removed while the earlier preset flow was unreliable. The updated playground restores presets with a new working set and keeps custom reference video upload available for users who want a specific dance motion. ## Try the Updated Kling 2.6 Dance Generator The simplest workflow is: - Open the Kling 2.6 Dance Generator . - Upload the image you want to animate. - Choose a dance preset, or keep custom reference video selected. - Generate the dance video. - Try another preset or reference if you want a different vibe. This blog should make it easy to jump into the playground. A compact demo or CTA near the top gives readers a quick path to try the update while keeping the full experience on the main dance generator page. ## Dance Presets Are Back Dance presets were temporarily removed from the playground because the previous preset flow was not reliable enough for a smooth user experience. Rather than leaving users with options that could fail or create confusion, the page stayed focused on custom reference video uploads while the preset workflow was being reworked. The new update restores presets with a fresh working set of dance references. Users can now choose from preset motion styles directly in the playground, or keep the default custom reference video mode when they want to upload their own motion clip. The goal is simple: make the dance generator easier to test, easier to understand, and more useful for casual image-to-dance experiments. ## What Changed in the Playground The updated playground now includes a dance preset dropdown. By default, the page opens in custom reference video mode, so users can still upload their own dance reference video just like before. When a preset is selected, the playground uses that preset to guide the dance motion. Users do not need to upload or prepare a separate reference video unless they want a specific custom movement. In practical terms, users now have two clear paths: - Choose a preset when they want a ready-made dance motion. - Choose custom reference video when they want to upload their own dance clip. Option Best For What You Need Dance preset Quick experiments, social clips, avatar tests, and trying different motion styles An image to animate Custom reference video Specific choreography, a particular rhythm, or a dance clip you already own An image and a reference video ## Why Presets Matter AI dance generation often depends on the quality and clarity of the reference motion. A custom reference video gives users the most control, but it also adds setup work: they need to find or create a suitable clip, upload it, and make sure the motion is easy for the model to follow. Dance presets reduce that friction. They give users a starting point without requiring them to prepare a motion clip first. This is especially useful for quick tests, demos, playground exploration, and early creative experiments. Presets are also helpful when you want to compare how different images respond to the same motion. For example, you can upload different character images and reuse the same preset to see how identity, outfit, pose, and body framing affect the final video. Practical takeaway: Use presets when you want to start quickly. Use custom reference video when you already know the exact dance movement you want. If there is a dance preset you would like to see added to the generator, you can contact the PiAPI team at support@piapi.ai . Preset suggestions help us understand which dance styles users want to try next. ## Example Preset 1: Scuba Dance Example structure: - Input: [PLACEHOLDER: describe image type, such as full-body character, mascot, portrait, or stylized avatar] - Preset: Scuba Dance - Best for: [PLACEHOLDER: e.g. playful social clips, character demos, fun avatar motion] - Result notes: [PLACEHOLDER: mention motion clarity, body framing, identity consistency, and any limitation] ## Example Preset 2: Phonk Dance Example structure: - Input: [PLACEHOLDER: describe image type] - Preset: Phonk Dance - Best for: [PLACEHOLDER: e.g. energetic motion, trend-style edits, bolder character animation] - Result notes: [PLACEHOLDER: describe what users should expect] ## When to Use a Dance Preset Use a dance preset when you want to move quickly. Presets are a good fit when you are testing the playground for the first time, trying a fun image, or seeing which dance style gives the best result. They are also useful when you do not have a reference video ready. Instead of searching for a clip first, you can pick a preset, generate a result, and decide whether the image works well for dance animation. For best results, start with an image where the subject is easy to read. Full-body or mostly full-body images usually give the model more motion information to work with than tight face crops or heavily obscured poses. Good preset inputs usually have: - One clear main subject - A visible body or mostly visible body - Good lighting - Minimal background clutter - A pose that is easy to understand ## When to Upload a Custom Reference Video Use custom reference video mode when you already have a dance clip you want the image to follow. This is the better choice when a preset is not quite the right move, rhythm, or mood. Custom reference videos also give you more control over rhythm, pose changes, and movement style. If you already have a clean motion clip that you are allowed to use, uploading it can be the most direct way to guide the output. In short, presets are best for convenience and exploration. Custom references are best for control. Simple rule: If you want to try a dance quickly, choose a preset. If you want the output to follow a specific clip, use custom reference video. ## Why This Update Makes the Tool Easier to Enjoy The dance generator is meant to feel easy and playful. You should not need to hunt for a reference video just to see what your image looks like in motion. With presets back in the playground, you can upload a photo, pick a dance style, and experiment quickly. If the first result is not the right mood, try another preset. If you already have a specific dance in mind, switch back to custom reference video and upload your own clip. That makes the tool better for casual experiments, funny character animations, social posts, avatar tests, and quick "what would this image look like dancing?" moments. The main Kling 2.6 Dance Generator page remains the best place to try it. ## FAQ ### Can I still upload my own dance reference video? Yes. The updated playground keeps custom reference video mode. By default, the page opens on custom reference video, so users can upload their own motion clip if they do not want to use a preset. ### What is the difference between a dance preset and a custom reference video? A dance preset is a built-in motion option you can select from the playground. A custom reference video is a clip you upload yourself when you want the generated dance to follow a specific movement. ### Why were dance presets removed before? The previous preset flow was temporarily removed because it was not working reliably enough for the page experience. The new update brings presets back with a working set of dance references. ### Do presets replace custom reference videos? No. Presets and custom reference videos are two different paths. Presets are useful for quick starts and repeatable testing. Custom reference videos are better when you need a specific choreography or owned motion reference. ### Where can I try the new dance presets? You can try them in PiAPI's Kling 2.6 Dance Generator . Upload an image, choose a preset from the dropdown, and generate your dance video. ## Editorial Notes Before Implementation - Keep this article as supporting content for /kling-2-6/dance-generator . - Internal linking direction: link only to /kling-2-6/dance-generator unless implementation reveals a strong reason to add another target. - Avoid optimizing the H1 for the broad head term AI dance generator ; that should remain the main page's target. - Add one or two tested preset examples before publishing. - Use these local example files when building the final page: - Scuba example: C:\Users\lixin\Downloads\scuba dance.mp4 - Phonk example: C:\Users\lixin\Downloads\phonk output.mp4 - Use a lightweight CTA/demo block instead of embedding the full playground. - Do not expose or name source reference videos from third-party creators. ## How to Consistently Animate Ghibli-Style AI Images Into Cinematic Videos Learn a Ghibli image-to-video workflow for consistent AI videos: stylize your photo first, then animate the still image with simple motion prompts. May 29, 2026 Ghibli-style AI images can look beautiful as stills, but the video step is where consistency usually breaks. A face may shift, the background may melt, the colors may become too generic, or the motion may feel more like a filter than a cinematic scene. The most reliable workflow is simple: create a clean Ghibli-style image first, then animate that finished image with an image to video AI generator. Separating style creation from motion generation gives the video model a stronger reference for the character, colors, lighting, and composition. With PiAPI's Ghibli Style AI Generator , the playground already includes preset prompts for the Ghibli-style photo workflow. You can upload a photo, generate a stylized image without writing a prompt, then use Animate Image mode to turn that still into a short cinematic clip. Motion prompts are optional and should stay simple. Quick answer: To animate a Ghibli-style AI image consistently, first convert your photo into a Ghibli-style still image, then pass that image into an image-to-video AI generator. Add only simple motion notes, such as gentle wind, a slow camera push, or drifting clouds, so the model preserves the original character, colors, and composition. Definition: Ghibli image-to-video is an AI workflow that turns one Ghibli-inspired still image into a short animated clip. The best results usually come from locking the visual style in the still image first, then adding restrained camera or environmental motion. Style note: "Ghibli-style" here refers to a warm, hand-drawn, storybook anime look inspired by classic animated-film aesthetics. PiAPI is not affiliated with Studio Ghibli. ## Why Ghibli-Style AI Videos Often Lose Consistency Animating an image is harder than restyling a still photo because the model has to preserve several things at the same time: the subject, the pose, the background, the lighting, the color palette, and the motion. If the input is a normal photo, the model may also have to convert the style while creating motion. That is why direct image-to-video can work when the source image is visually readable, but become inconsistent when the model has too much to interpret. A clear portrait, pet photo, or well-composed travel image may animate well right away. A crowded scene, messy background, tiny face, or detailed object scene is more likely to drift. The main goal is not just movement. The goal is controlled movement that keeps the original image recognizable. ## The Most Consistent Workflow: Stylize First, Animate Second For the most consistent Ghibli-style video, use a two-step workflow. - Upload your original photo. - Convert it into a Ghibli-style image first . - Review the still image for subject accuracy, lighting, and background quality. - Pass the generated still image into Animate Image mode. - Add optional motion notes only if you want a specific camera or environmental movement. - Generate one or more takes and keep the best version. This workflow works because the video model receives a finished visual reference. It does not need to invent the Ghibli-style look and the motion in the same step. It can focus on animating the already-stylized character and scene. Use this workflow when the original photo is complex, when the person or pet needs to stay recognizable, or when the final result needs a soft cinematic look rather than a generic anime filter. If you need more detail on the still-image step, start with this guide on how to create the Ghibli-style still image first . ## When Direct Image-to-Video Can Work You can also upload the original image straight into the Ghibli video workflow. This shortcut can work when the source image has a readable subject, strong lighting, and a composition the model can understand. Direct image-to-video is most likely to work when the image has: - one clear subject or a simple group composition - an uncluttered or readable background - good lighting - a simple pose - no tiny faces or hard-to-read hands - a composition that already feels close to an animated scene If the first result is not satisfying, regenerate once or switch to the stylize-first workflow. Regeneration can fix small issues, but if the style keeps drifting, the input probably needs a stronger Ghibli-style still image before animation. ## How to Prepare Your Image Before Animation The input image still matters, even when the AI handles the style. A cleaner image gives the model fewer decisions to make. Use a source image with a clear subject and enough space around the subject for motion. A tight face crop can work for a portrait animation, but a cinematic camera push or pan needs more room. For landscape and travel photos, choose images with a readable foreground, middle ground, and background. Avoid source images with heavy blur, low light, crowded people, confusing limbs, reflective glass, busy signage, or tiny important details. These issues can become more noticeable once the image moves. If the image needs cleanup before animation, use an image editing workflow first, then animate the cleaner result. Shot type Best source image Best motion direction Portrait Clear face, simple background subtle blinking, gentle breeze, slow push-in Pet Visible eyes, ears, fur shape soft head movement, breathing, background breeze Travel scene Strong landmark or landscape slow pan, drifting clouds, parallax Room or product Clean composition, stable lighting warm light flicker, subtle camera move Story scene Clear subject and environment gentle environmental motion ## How to Write Motion Prompts Without Breaking the Style Motion prompts should describe movement, not rewrite the whole image. If the prompt asks for a new outfit, new location, new action, and new mood all at once, the model has more chances to change the subject or lose the Ghibli-style look. Use this simple image to video prompt formula: preserve the original image + simple subject motion + simple camera motion + soft atmosphere ``` Maintain the original Ghibli-style character, colors, and composition. Add a slow cinematic camera push-in, gentle wind moving the hair and clothes, and soft clouds drifting in the background. ``` For a no-prompt workflow, you can leave the motion field empty and use the preset animation behavior. Add a motion prompt only when you want to guide the movement. For deeper image-to-video testing after the Ghibli-style still is ready, use an advanced image-to-video model workflow. ## Best Motion Ideas for Ghibli-Style Videos Ghibli-style AI videos usually look better with restrained movement. The painterly look is easier to preserve when the motion feels like a quiet cinematic moment. Good motion ideas include a slow camera push-in, a gentle side pan, subtle parallax between foreground and background, drifting clouds, grass moving in the wind, hair or clothing moving softly, warm sunlight flicker, blinking, or a slight smile. ## Example 1: Photo to Ghibli Image, Then Ghibli Video This example uses the recommended consistency workflow. The original photo is converted into a Ghibli-style still first, then the generated still is animated. The still image gives the video model a cleaner reference. The final video can focus on motion while keeping the subject, colors, and storybook atmosphere more stable. ## Example 2: Original Image Straight to Ghibli Video This example uses the shortcut workflow. The original image is sent directly into the Ghibli image-to-video mode without first creating a separate still image. Direct image-to-video can work when the input has a readable composition. If the output changes the subject too much or looks less stable than expected, regenerate or use the stylize-first workflow. ## Stylize First vs Animate Directly Use this decision guide when choosing a workflow. Input situation Best workflow Why Complex real photo Stylize first, then animate The still image locks the style before motion begins. Clear portrait Direct can work, stylize first is safer Simple faces are easier, but style drift can still happen. Pet photo Stylize first, then animate Fur markings and body shape are easier to check in the still image. Travel photo Stylize first, then animate The scene becomes more cinematic before motion is added. Already anime-style image Direct image-to-video can work The image already gives the model a style reference. Unsatisfying direct result Regenerate or stylize first Repeated drift means the input needs a stronger still reference. For most users, the decision is practical: if the source image is visually readable, test direct animation first; if the source image is crowded, unclear, or the output must stay recognizable, stylize first and animate the generated still. ## Common Mistakes and How to Fix Them Most failed outputs come from asking the model to do too much at once. Problem Likely cause Simple fix The video does not look Ghibli-style enough The original photo was animated directly and the style was weak Convert the photo into a Ghibli-style image first, then animate. The face changes during motion The source image is unclear or the motion is too strong Use a clearer input, reduce motion, or regenerate. The background melts The scene is too crowded or the camera movement is too aggressive Crop the image, simplify the scene, or use slower motion. The video feels chaotic The motion prompt includes too many actions Use words like slow, gentle, subtle, and cinematic. The result looks like generic anime The image lacks a strong storybook look Use the preset Ghibli image workflow before animation. The first take is close but imperfect Normal image-to-video variation Regenerate and compare multiple takes. ## How to Use PiAPI for This Workflow PiAPI's Ghibli Style AI Generator gives you two paths inside the same playground. Use Photo to Image when you want the most consistent result. Upload your original photo first, generate a Ghibli-style still image, and check whether the subject, colors, and scene feel stable. If the image looks good, use that generated image as the input for Animate Image . Use Animate Image directly when your original image is already readable. This works best for clear portraits, pets, and clean scenes with an obvious subject. You do not need to write a prompt for the basic demo. If you want more control, add a short motion note such as slow camera push-in , gentle breeze , or clouds drifting in the background . The main advantage is that you do not have to write a full Ghibli-style prompt from scratch. The style workflow is already preset, so your job is to choose the right input image, pick the right mode, and regenerate when a take is close but not quite right. ## Final Takeaway If you want the most consistent Ghibli-style AI video, do not make the video model solve everything at once. First create a clean Ghibli-style still image, then animate that image with subtle motion. Direct image-to-video can still work when the input is simple or clearly composed. But when the result drifts, loses the style, or changes the subject, switch back to the stronger workflow: stylize first, animate second. Start with the Ghibli Style AI Generator, generate a still image you like, then animate it into a short cinematic scene. Related reading: see the Ghibli-style image generation API guide for an older API-focused look at Ghibli-style image generation. ## Ghibli Style Images: Convert Photos With AI Upload a photo and turn it into Ghibli-style AI art with PiAPI's preset playground. Learn source-photo tips, edit notes, examples, and fixes. May 28, 2026 Ghibli style images work best when the AI has a strong photo to preserve and a clear creative direction for what should change. A good result usually keeps the subject, pose, outfit, pet shape, room layout, or travel scene recognizable, while changing the image style into softer linework, warm light, painterly color, and a nostalgic storybook mood. This guide focuses on the practical part: how to choose the right photo, what the preset Ghibli-style prompt is already doing, when to add optional edit notes, how to fix weak outputs, and how to decide when the image is ready to animate. If you already have a photo ready, you can turn your photo into Ghibli-style art with PiAPI's photo-first workflow. The playground already includes the Ghibli-inspired style prompt, so you can upload an image and generate without writing a prompt yourself. Quick answer: To convert a photo to Ghibli style, start with a clear image and use a workflow that preserves the subject and composition while applying soft hand-drawn linework, warm lighting, painterly backgrounds, expressive details, and storybook color. With PiAPI, the core style prompt is preset; optional notes are only needed when you want edits like removing background clutter, changing the mood, or protecting a specific detail. Definition: A Ghibli-style image is an AI-generated or AI-edited picture that uses warm animated-film-inspired traits such as soft linework, painterly color, gentle lighting, expressive features, and storybook-like scenery. It should be described as Ghibli-inspired, not official Studio Ghibli artwork. ## What Makes a Good Ghibli-Style Image? A strong Ghibli-style AI image is not just a photo with an anime filter. The best results usually combine a recognizable subject, a clear scene or emotional moment, and a visual direction that feels hand-drawn, warm, and cinematic. For photo-to-image workflows, the most important part is balance. You want the AI to change the style without losing the person, pet, object, or place that made the original photo worth using. PiAPI's playground handles the preset style conversion, while the source photo determines how much useful detail the model has to preserve. Good results usually have soft linework instead of harsh outlines, warm daylight or sunset tones, painterly textures, and natural background details like clouds, trees, flowers, rooms, kitchens, streets, or fields. The image should feel like a quiet story moment, not a generic cartoon filter. Use the phrase "Ghibli-inspired" when writing optional notes or publishing outputs. PiAPI is not affiliated with Studio Ghibli, and the goal is to describe a warm animated-film-inspired visual direction rather than claim an official style or relationship. ## Choose the Right Photo Before You Generate The best photo for Ghibli style AI is usually simple, clear, and emotionally readable. The AI can do more with a clean source image than with a crowded, blurry, or heavily filtered one. Strong source photos usually show one main person, pet, object, or scene. The subject should be well lit, the pose should be easy to understand, and the background should add context without taking over the image. A portrait with a visible face, a pet photo with clear eyes and markings, or a travel shot with a strong landmark will usually convert better than a dark, cropped, or heavily filtered image. Weak source photos tend to hide the subject in shadow, crop off important details, or include too many people and background distractions. If the original photo is already hard to read, the generated image will often struggle too. If you want a profile picture, start with a portrait where the face is visible. If you want a pet portrait, use a photo where the animal's eyes, ears, and body shape are clear. If you want a travel scene, choose a photo with a strong landmark, street, mountain, sea view, garden, or skyline. ## How to Turn a Photo Into Ghibli-Style Art The simplest workflow is upload first, generate second, refine only when needed. - Upload one clear photo. - Click generate. The Ghibli-inspired style prompt is already preset in the playground. - Review the output for identity, composition, and atmosphere. - Add optional notes only if you want something removed, something added, or a specific detail protected. - Regenerate if you want a cleaner version or a more specific edit. The preset workflow already handles the Ghibli-inspired art direction. Optional notes are for extra control, such as removing a background object, keeping a face closer to the original photo, adding warmer lighting, or making the background less cluttered. If you want to understand the model behind broader image editing workflows, PiAPI also has a Qwen Image API page for image generation and editing. When you do add notes, it helps to separate what should stay the same from what should change. For example, you might want the face, pose, outfit, pet markings, room layout, or travel scene structure to stay close to the original. At the same time, you may want the lighting, color palette, texture, linework, and background mood to become softer and more cinematic. The preset prompt already handles the Ghibli-inspired look. Most users can skip writing anything and only add a short note when they want a specific edit. ## When to Add Optional Edit Notes Optional notes are useful when you want to change something specific in the uploaded image. Keep them short and practical. Good optional notes sound like normal edit requests: remove the person in the background, keep the original outfit and pose, make the lighting warmer, add more flowers and greenery, remove the logo on the shirt, make the room less cluttered, or keep the pet's fur pattern the same. You do not need to describe the full Ghibli style yourself. The playground already does that part. ## Common Problems and How to Fix Them If the result looks wrong, identify the one thing that failed first. Many issues can be fixed by choosing a clearer photo or adding one short optional note. Problem Likely Cause Simple Fix The face no longer looks recognizable The face is small, blurry, or partly covered Use a clearer portrait, or add a note to keep the face and hairstyle close to the original. The background changed too much The scene is crowded or unclear Use a cleaner photo, or add a note to keep the original room, street, landmark, or landscape. The pet looks different The photo does not show markings or body shape clearly Use a sharper pet photo, or add a note to keep the fur pattern and body shape. The image feels too busy The source photo has too many distractions Crop the photo or add a note to remove background clutter. The output is close but not perfect The first generation needs refinement Regenerate once, or add one short edit note. One short note usually works better than a long instruction list. ## Want to Animate the Image Next? Once you have a Ghibli-style still image you like, you can use it as the starting frame for a short cinematic video. A clean still image usually gives the animation model a better base than an ordinary photo, especially when the subject, lighting, and background already look consistent. For the full next step, read how to animate Ghibli-style AI images into cinematic videos . The important idea is simple: create the still image first, then animate the result with an image-to-video workflow such as Kling 3 Omni. ## Final Takeaway The best Ghibli style images start with a good source photo and a workflow that protects the subject before changing the style. Choose a clear image, use the preset conversion, add optional notes only when needed, and fix one problem at a time. When you are ready to try the workflow, start with a still image first: turn your photo into Ghibli-style art , then animate the result once the image looks stable. Related reading: PiAPI previously tested Ghibli-style image generation with GPT-4o in this Ghibli style image generator API article . This guide is more focused on the no-prompt photo playground workflow. ## Clay Animation Maker: Create Clay-Style Videos and Images with AI Use an AI clay animation maker to create clay-style videos from prompts, transform existing footage, and generate claymation-style images from text or uploaded images. An AI clay animation maker helps you create clay-style videos and images without building physical sets, sculpting figures, or shooting frame by frame. Instead of starting from a traditional stop-motion setup, you can use text prompts, existing videos, or uploaded images to create visuals with rounded clay shapes, handmade texture, miniature-set lighting, and a stop-motion-inspired look. Quick answer: The easiest way to make clay-style visuals with AI is to choose a mode based on your input: use Text to Video for new clay animations, Video to Video for existing footage, Text to Image for still clay concepts, and Image to Image to restyle an uploaded image as clay. PiAPI's Claymation AI Generator supports four clay-style workflows in one place: - Text to Video - Video to Video - Text to Image - Image to Image That means you can create a clay animation from a prompt, turn existing footage into claymation style, generate still clay concepts, or restyle an image as a clay object. ## What Is a Clay Animation Maker? A clay animation maker is a tool for creating visuals that look like clay animation. Traditional clay animation, also called claymation, usually involves sculpting characters, building miniature sets, adjusting lighting, and capturing small movements frame by frame. Definition: An AI clay animation maker is software that creates claymation-style images or videos from text prompts, uploaded images, or existing videos. It simulates handmade clay texture, rounded forms, miniature-set lighting, and stop-motion-inspired visuals without requiring physical clay models. An AI clay animation maker gives creators a faster way to explore the style. You can describe a scene, upload footage, or provide a reference image, then generate clay-style output for social posts, product concepts, character ideas, short videos, ads, or creative tests. The goal is not to replace every detail of handmade stop-motion production. The goal is to make the clay animation look easier to prototype, iterate, and adapt across image and video formats. ## What Can You Create with an AI Clay Animation Maker? With PiAPI's Claymation AI Generator, you can create both clay-style videos and clay-style images. The best mode depends on what you already have. Starting point Best mode Best for Written idea Text to Video New clay-style animation clips Existing video Video to Video Turning footage into a claymation look Written visual concept Text to Image Still clay-style concepts and thumbnails Existing image Image to Image Restyling a photo, product, logo, or character as clay Use Text to Video when you want to create a new clay-style animation from a written prompt. Use Video to Video when you already have footage and want to transform it into claymation style. Use Text to Image when you want a still clay-style concept, thumbnail, character, product idea, or scene. Use Image to Image when you want to restyle an existing image, logo, product shot, or character as a clay object. This is the main advantage of using one clay animation maker instead of separate tools for clay images and clay videos. ## Text to Video: Make a Clay-Style Animation from a Prompt Text to Video is the most direct way to create a clay animation from scratch. You write a prompt that describes the subject, action, scene, and claymation look you want. For broader AI video generation beyond clay-style outputs, PiAPI also supports models such as Kling 3.0 . For example, a prompt can describe a miniature character, the movement, the camera framing, and the handmade texture: ``` A tiny handmade clay astronaut walks across a miniature moon set, turns toward Earth in the sky, and waves slowly. Soft stop-motion movement, visible clay fingerprints, rounded clay shapes, warm studio lighting, shallow depth of field. ``` Example output - Mode: Text to Video - Input: prompt above - Output: /images/blogs/clay-animation-maker/text-to-video-clay-astronaut.mp4 The result shows the value of Text to Video for starting from an idea alone. The clay-style scene, miniature setting, and simple character motion make it easy to understand how a written prompt can become a short clay animation. This mode is useful when you do not have a starting video or image. It works well for quick creative ideas, short scenes, character concepts, and clay-style motion tests. ## Video to Video: Turn Existing Footage into Claymation Style Video to Video is useful when you already have motion and want to convert that footage into a clay animation look. Instead of writing a full style prompt, you upload a source video and use the preset claymation workflow in the playground. The additional prompt field is optional. Use it only when you want to guide specific changes, such as removing an unwanted object, adding a small detail, or clarifying what should stay important from the source video. Example output - Mode: Video to Video - Input: /images/blogs/clay-animation-maker/video-to-video-input.mp4 - Output: /images/blogs/clay-animation-maker/video-to-video-clay-output.mp4 The output keeps the original video structure while applying the clay animation preset. This example shows the simplest Video to Video workflow: upload a source clip, use the preset clay animation style, and generate a clay-style version without writing a full prompt. For best results, use short footage with a clear subject, stable motion, and simple backgrounds. The cleaner the input, the easier it is for the clay animation effect to preserve the action. ## Text to Image: Create Clay-Style Concepts Text to Image is useful when you want a still clay-style visual before making a video. You can use it for thumbnails, character references, storyboards, product concepts, campaign visuals, or social content. If you are comparing image models for still concepts, Seedream 5 Lite is another PiAPI image model worth understanding. Here is an example prompt: ``` A cheerful claymation food truck selling tiny cupcakes on a miniature street, handmade clay texture, rounded toy-like shapes, colorful stop-motion set, soft studio lighting, detailed but playful. ``` Example output - Mode: Text to Image - Input: prompt above - Output: /images/blogs/clay-animation-maker/text-to-image-clay-cupcake-truck.png The generated image shows a clear clay-style cupcake truck scene with rounded handmade shapes, soft miniature-set lighting, and playful character details. This is useful for testing the visual direction of a clay animation before creating video. This mode is good for exploring style direction quickly. If you are planning a clay-style video, still images can help you test the look before moving into video generation. ## Image to Image: Restyle an Image as Clay Image to Image is useful when you already have a visual reference and want to convert it into clay style. In the playground, the claymation style prompt is already preset, so you can upload an image and hit generate. The additional prompt field is optional. Use it only when you want to guide a specific change, such as keeping a brand shape clear, removing an unwanted detail, adding a small prop, or preserving the main subject more strongly. Example output - Mode: Image to Image - Input: /images/blogs/clay-animation-maker/image-to-image-basketball-input.png - Output: /images/blogs/clay-animation-maker/image-to-image-basketball-clay-output.png The output keeps the basketball scene recognizable while changing the player, hoop, court, and crowd into a handmade clay-style scene. This is a good example of Image to Image because the source composition stays clear, but the final look feels much more tactile and stop-motion inspired. This mode is strongest when the input image has a clear subject and simple composition. If the source image is too busy, crop it around the main object before generating. ## Try an Image to Clay Demo Upload an image and generate a clay-style version with the embedded playground below. The claymation style is already preset, so you can start with a simple image and generate without writing a full prompt. For the full playground with Text to Video, Video to Video, Text to Image, and Image to Image modes, open the Claymation AI Generator . ## Which Claymation Mode Should You Use? Choose the mode based on your starting point. If you only have an idea, use Text to Video for animation or Text to Image for a still concept. If you already have a video, use Video to Video to turn the existing motion into clay animation style. If you already have an image, use Image to Image to restyle it as a clay object. For most creators, the easiest workflow is: - Start with Text to Image to explore the clay look. - Use Text to Video when you want motion from a prompt. - Use Video to Video when you have footage that already has the right action. - Use Image to Image when you need a clay-style version of an existing asset. Mode recommendation: If your goal is a clay animation video, start with Text to Video when you only have an idea and Video to Video when you already have footage. If your goal is a still clay visual, use Text to Image for new concepts and Image to Image for existing assets. ## Tips for Better Clay Animation Results Keep the subject clear. Clay animation works best when the viewer can immediately understand the main character, object, or action. Use tactile style words. Phrases like handmade clay , visible fingerprints , rounded clay shapes , miniature set , soft studio lighting , and stop-motion style help define the look. Avoid overloading the scene. Too many characters, fast motion, or complicated backgrounds can make the result less readable. For Video to Video, start with short clips. A simple five-second clip with one clear subject is easier to transform than a long, busy scene. ## Try PiAPI's Claymation AI Generator You can try the full workflow on PiAPI's Claymation AI Generator . The playground lets you switch between text-based, image-based, and video-based claymation workflows from one page. If you are comparing tools first, you can also read the Best Claymation AI Generator for Images and Videos guide. That article focuses on tool selection, while this guide focuses on how to use each clay animation workflow. Start testing the Claymation AI Generator through PiAPI today. Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## FAQ ### What is a clay animation maker? A clay animation maker is a tool that helps create visuals in a clay animation or claymation style. An AI clay animation maker can generate clay-style videos or images from text prompts, existing videos, or uploaded images. ### What is the best AI clay animation maker for videos? The best AI clay animation maker for videos should support both prompt-based video generation and video-to-video restyling. PiAPI's Claymation AI Generator supports Text to Video for creating new clay-style clips and Video to Video for transforming existing footage with a clay animation preset. ### Can AI make clay animation videos? Yes. AI can make clay animation-style videos through text-to-video or video-to-video generation. Text to Video creates motion from a prompt, while Video to Video transforms existing footage into a claymation look. ### How do I turn a video into claymation with AI? Upload a short source video into Video to Video mode, keep the preset clay animation style, and generate the result. If you want to remove, add, or preserve a specific detail, use the optional prompt field for that extra instruction. ### What is the difference between Text to Video and Video to Video? Text to Video starts from a written prompt and creates a new clay-style animation. Video to Video starts from uploaded footage and restyles that footage as clay animation while keeping the original motion and composition as the base. ### Can I turn a video into claymation style? Yes. Use Video to Video mode to transform existing footage into claymation style. For best results, choose a short clip with a clear subject and simple movement. ### Can I create clay-style images too? Yes. Use Text to Image to create a new clay-style image from a prompt, or Image to Image to restyle an existing image as a clay object. ### Is this the same as a claymation generator? In many searches, clay animation maker and claymation generator refer to similar tools. The difference is usually intent: a clay animation maker sounds more workflow-focused, while a claymation generator sounds more tool-focused. PiAPI's Claymation AI Generator supports both image and video workflows. ### Do I need animation skills to use an AI clay animation maker? No. You do not need stop-motion or animation skills. You only need a prompt, image, or video input, then the AI tool generates the clay-style result. ## Conclusion An AI clay animation maker gives you a faster way to create clay-style videos and images. With PiAPI's Claymation AI Generator, you can create a clay animation from text, transform existing footage with Video to Video, generate still clay concepts, or restyle an image as a clay object. If you want one workflow for clay-style visuals across video and image formats, try the Claymation AI Generator . ## Best Free AI Dance Generator to Create Dance Videos Online Use an AI dance generator to turn a photo into a dance video online. Learn how it works, see one example, and try PiAPI's AI dance demo with 0.5 free signup credits. An AI dance generator turns a still photo or avatar image into a short dance video. Instead of filming choreography yourself, you can upload a clear image, pair it with a dance reference video, and generate a shareable AI dance video for TikTok, Reels, Shorts, memes, character videos, or creative tests. PiAPI's AI Dance Generator is built for this exact workflow: input photo plus input dance video, then AI-generated dance output. New PiAPI users receive 0.5 free credits on signup, so you can test eligible playground and Pay-as-you-go API workflows before building a larger video workflow. This guide keeps the process simple, with one example and a small demo playground showing how the inputs work together. ## What Is an AI Dance Generator? An AI dance generator is an image-to-video tool that animates a person, avatar, or character into a dance video. The tool uses the uploaded image as the subject and a dance video as the motion reference, then creates a new video where the subject follows the dance movement. Quick Answer: To make an AI dance video, upload a clear full-body photo, add a short dance reference video, and generate a new clip where the person or avatar in the photo performs the dance. This is useful when you want a quick dance video without recording yourself, editing motion manually, or building an animation from scratch. ## How to Turn a Photo Into an AI Dance Video The easiest workflow has three steps: - Upload an input photo or avatar image. - Upload or choose an input dance video. - Generate the AI dance video and preview the result. For best results, start with a single visible subject. A full-body image usually works better than a cropped portrait because the model has more information about the body, clothing, pose, and proportions. If you want to build this into an app later, the same workflow can connect to the Kling 2.6 API . ## What Makes a Good AI Dance Generator? A good AI dance generator should make the input workflow obvious, keep the subject recognizable, and produce motion that looks usable without heavy editing. For most users, the important checks are simple: - Can it use a real photo or avatar image? - Can it follow a dance reference video? - Does the face and outfit stay consistent enough? - Is the result easy to preview, download, or share? That is why this guide focuses on a photo-to-dance workflow instead of a long list of generic AI video tools. ## Try the AI Dance Generator Demo Use this mini playground to understand the workflow before creating your own result. Try the AI Dance Generator ## Example: Create a Dance Video From One Avatar Image Below is one sample workflow using a full-body avatar image, a short dance reference clip, and the generated AI dance result. For this example, the input photo is a full-body avatar image with one person standing in a clean studio space. The subject is centered, the face is visible, and the arms and legs are not heavily blocked by other objects. The input dance video should be short and simple. A 5-10 second single-person dance clip works best for the first test. Choose a reference video with a stable camera, visible full-body movement, and no extra dancers crossing the frame. The goal is not to make the most complex dance on the first try. A simple reference video makes it easier to judge whether the avatar stays recognizable, the movement looks natural, and the result is usable for social content. ## Tips for Better AI Dance Videos Use a clear full-body image when possible. The face, torso, arms, legs, and feet should be visible so the AI has enough information to animate the subject. Keep the scene simple. One subject and a clean background usually produce a more stable AI dance video than a crowded scene with several people. Choose a dance reference video with visible body movement. Avoid clips with fast camera shakes, heavy motion blur, cropped feet, or multiple dancers if you want a cleaner first result. ## FAQ ### What is the best AI dance generator? The best AI dance generator is one that lets you create a dance video from a clear input photo and a dance reference video. PiAPI's AI Dance Generator is a strong option if you want a focused playground for testing avatar-to-dance video generation. ### Can I make an AI dance video for free? Yes. New PiAPI users receive 0.5 free credits on signup, which can be used to test eligible playground and Pay-as-you-go API workflows. You may still need to sign in before generating your own video, and credit limits or model costs can change over time. ### Can I turn a photo into a dance video with AI? Yes. Upload a photo as the subject, add a dance reference video, and the AI can generate a new video where the person or avatar follows the dance movement. ### What image works best for an AI dance video? A clear full-body image with one main subject works best. Avoid images where the face, hands, legs, or feet are heavily cropped or hidden. ## Create Your AI Dance Video If you already have a photo and a dance reference video, the next step is to test them together. Open PiAPI's free AI dance video generator , upload your inputs, and generate your first AI dance clip. ## Best Claymation AI Generator for Images and Videos Looking for a claymation AI generator? Learn how to create clay-style images and clay AI videos from text or uploaded images with PiAPI's claymation playground. Quick answer: A claymation AI generator creates clay-style images or videos from text, uploaded images, or uploaded videos. PiAPI's Claymation AI Generator is best suited for users who want one playground for text-to-image, image-to-image, text-to-video, and video-to-video claymation workflows. Looking for a claymation AI generator that can create both clay-style images and clay AI videos? PiAPI's Claymation AI Generator gives you one focused playground for turning text prompts, uploaded images, or uploaded videos into handmade clay-style visuals. Instead of switching between a claymation image generator, a photo-to-clay filter, and a separate clay AI video tool, you can test four workflows in one place: text to image, image to image, text to video, and video to video. - Use text to image when you want a new clay-style still from a written idea. - Use image to image when you already have a photo, sketch, product shot, or character reference. - Use text to video when you want a new clay AI video from a short scene description. - Use video to video when you want to turn existing footage into claymation style. ## What Is a Claymation AI Generator? A claymation AI generator is a tool that creates visuals in the style of handmade clay animation. The output usually has rounded forms, soft sculpted textures, visible handmade details, miniature-set lighting, and a stop-motion feel. In practical terms, a claymation AI generator is not only a photo filter. The most useful tools support both creation and transformation: generating a new scene from text, restyling an existing image, creating a short video from a prompt, or converting uploaded footage into a clay-style clip. Traditional claymation requires sculpting figures, building sets, lighting each scene, photographing frames, and animating them one by one. AI claymation tools make the style much faster to explore. You can describe a scene in text, upload a reference image, or transform existing footage into a short clay-style video. ## Why Use a Claymation AI Generator? Claymation has a warm, tactile look that stands out from generic AI images. It feels handmade, playful, and physical, which makes it useful for characters, product concepts, logos, thumbnails, social posts, and short creative videos. The main advantage is speed. You can test the clay look without sculpting models or setting up a stop-motion shoot. A good claymation AI generator should let you start from text, an image, or a video, then create a still image or a clay-style clip depending on what you need. ## Best Claymation AI Generator for Images and Videos PiAPI's Claymation AI Generator is built for both claymation images and clay AI videos. The playground supports four generation modes in one workflow: - Text to Image: describe a clay-style scene and generate an image. - Image to Image: upload an image and convert it into claymation style. - Text to Video: describe a short clay-style video scene. - Video to Video: upload a video and turn it into claymation style. That four-mode setup is the main difference. Many claymation tools focus only on photo filters, text-to-image, or one video effect. PiAPI gives you a broader claymation workspace, so you can test the same idea across image and video formats. For users comparing claymation AI tools, the important question is not only whether the output looks like clay. It is whether the workflow fits the source material you already have. A text prompt, a still image, and a video clip each need a different generation mode. ## Claymation AI Examples The best way to judge a claymation AI generator is to look at real inputs and outputs. The examples below show the four main claymation workflows: a text prompt turned into an image, a photo turned into a clay-style image, a prompt turned into a short video, and an uploaded video transformed into claymation style. ## Example 1: Text to Claymation Image Input A tiny clay baker standing beside a miniature kitchen counter, holding a fresh loaf of bread, soft handmade clay texture, rounded character shapes, warm studio lighting, cozy stop-motion set. Output This example tests whether the generator can create a complete clay-style scene from text alone, including the character, props, lighting, and handmade material texture. ## Example 2: Image to Claymation Image Input Output This example tests image preservation. The input composition remains recognizable, while the people, street, and background are restyled as a miniature claymation scene. ## Example 3: Text to Clay AI Video Input A small clay robot walks across a handmade desk, looks up, waves gently at the camera, soft stop-motion movement, warm miniature studio lighting, playful handcrafted clay texture. Output This example tests motion from a written prompt. The goal is a short clay-style clip with simple character action, soft lighting, and a handcrafted stop-motion feeling. ## Example 4: Video to Claymation Video Input Output This example tests video transformation. The uploaded clip provides the original motion and scene structure, while the generator applies the claymation look to the footage. ## Conclusion The best claymation AI generator should do more than apply a simple photo filter. It should support both image and video creation, work from text or uploaded images or videos, and make the clay-style workflow easy to test. PiAPI's Claymation AI Generator is designed for that exact use case. You can create claymation images, convert images into clay style, generate clay AI videos from prompts, or turn an uploaded video into claymation style. Try the Claymation AI Generator ## FAQ ## What is a claymation AI generator? A claymation AI generator creates images or videos that look like handmade clay animation. It can add sculpted clay textures, rounded shapes, miniature-set lighting, and stop-motion style to text prompts, uploaded images, or uploaded videos. ## What is the best claymation AI generator? The best claymation AI generator depends on what you need to create. If you want one tool for claymation images and clay AI videos, PiAPI's Claymation AI Generator is a strong option because it supports text-to-image, image-to-image, text-to-video, and video-to-video workflows. ## Can AI create claymation videos? Yes. AI can create claymation-style videos from text prompts or uploaded videos. Text-to-video works well for new scenes, while video-to-video is better when you already have motion and want to transform the footage into claymation style. ## Can I turn a photo into claymation? Yes. Use image-to-image mode to turn a photo into a claymation-style image. For moving footage, use video-to-video mode to turn the clip into a clay-style video. ## Can I create claymation images from text? Yes. Text-to-image mode lets you describe a character, product, logo idea, or scene and generate a clay-style image from that prompt. ## Do I need animation skills to make clay AI videos? No. You do not need sculpting or animation skills. The generator handles the clay-style rendering and animation. You only need to provide a prompt or upload an image or video. ## Can I create both claymation images and videos in one tool? Yes. PiAPI's Claymation AI Generator supports both image and video workflows in one playground, so you can create still claymation images and clay AI videos from the same page. ## Which claymation mode should I use? Use text-to-image for new still images, image-to-image for restyling an existing photo, text-to-video for creating a new clay AI clip from a prompt, and video-to-video for turning uploaded footage into claymation style. ## 시댄스 2.0으로 광고 영상과 숏폼 마케팅 영상 만드는 방법 시댄스 2.0으로 제품 광고, 숏폼 마케팅 영상, 앱 소개 영상 등을 만드는 방법을 알아보세요. 이미지-to-video 워크플로우, 프롬프트 예시, 가격/크레딧 확인 방법까지 정리했습니다. English: How to Create Commercials and Short-Form Marketing Videos With Seedance 2.0 시댄스 2.0은 제품 사진, 앱 화면, 브랜드 이미지 같은 기존 마케팅 자산을 짧은 AI 영상으로 바꾸는 데 활용할 수 있는 영상 생성 모델입니다. 특히 이미지 한 장을 기반으로 움직임, 카메라 워크, 분위기, 제품 사용 장면을 더하고 싶을 때 유용합니다. 이 글에서는 시댄스 2.0을 일반적인 AI 영상 생성 도구로 소개하기보다, 광고 영상과 숏폼 마케팅 영상을 만드는 실무 흐름에 맞춰 정리합니다. 제품 홍보 영상, SNS 숏폼 광고, 앱 소개 영상처럼 실제 마케팅에 쓰기 쉬운 예시를 중심으로 살펴보겠습니다. 짧게 말하면, 시댄스 2.0은 이미지를 영상으로 바꾸는 image-to-video 워크플로우에 특히 잘 맞습니다. 텍스트만으로 영상을 만드는 것도 가능하지만, 광고나 마케팅 영상에서는 제품 사진이나 브랜드 이미지처럼 이미 검수된 시각 자료를 시작점으로 쓰는 편이 결과를 관리하기 쉽습니다. 바로 테스트해보고 싶다면 PiAPI의 시댄스 2.0 API 페이지 에서 이미지-to-video 흐름을 먼저 확인할 수 있습니다. 기본 사용법, 가격, API 옵션을 먼저 정리하고 싶다면 시댄스 2.0 사용법 가이드 를 함께 열어두면 좋습니다. 이 글에서는 어떤 입력 이미지를 준비하고, 어떤 프롬프트로 광고 영상에 가까운 결과를 만드는지 단계별로 살펴봅니다. ## 시댄스 2.0은 광고 영상 제작에 적합한가요? English: Is Seedance 2.0 suitable for creating commercial videos? 네. 시댄스 2.0은 짧은 광고 영상, 제품 소개 영상, SNS용 숏폼 콘텐츠를 빠르게 테스트하는 데 적합합니다. 특히 한 장의 이미지를 기반으로 영상 움직임을 만들 수 있기 때문에, 이미 보유한 제품 사진이나 캠페인 이미지를 활용하기 좋습니다. 광고 영상 제작에서 중요한 것은 단순히 멋진 영상을 만드는 것이 아니라, 제품이 잘 보이고 메시지가 빠르게 전달되며 브랜드 톤이 유지되는 것입니다. 시댄스 2.0을 사용할 때도 이 세 가지를 기준으로 프롬프트와 입력 이미지를 준비하는 것이 좋습니다. 시댄스 2.0이 마케팅 영상에 잘 맞는 경우는 다음과 같습니다. - 제품 사진을 짧은 홍보 영상으로 바꾸고 싶을 때 - SNS 광고용 5-10초 영상을 빠르게 테스트하고 싶을 때 - 앱 화면이나 웹사이트 화면을 더 역동적으로 보여주고 싶을 때 - 캠페인 키비주얼을 영상 티저로 확장하고 싶을 때 - 여러 광고 콘셉트를 저비용으로 비교하고 싶을 때 다만 최종 광고 소재로 쓰기 전에는 브랜드 가이드, 제품 표현, 텍스트 정확성, 인물/상표권, 플랫폼 광고 정책을 반드시 검토해야 합니다. ## 시댄스 2.0으로 만들 수 있는 마케팅 영상 유형 English: Types of marketing videos you can create with Seedance 2.0 시댄스 2.0은 긴 브랜드 필름보다는 짧고 목적이 명확한 마케팅 영상에 더 잘 어울립니다. 특히 이미지 기반 영상 생성은 기존 비주얼을 활용하므로, 결과물이 브랜드와 완전히 동떨어질 가능성을 줄일 수 있습니다. 대표적인 활용 유형은 다음과 같습니다. 영상 유형 활용 예시 추천 입력 제품 광고 영상 화장품, 전자제품, 식품, 패션 제품 홍보 제품 사진, 상세 페이지 이미지 숏폼 SNS 광고 TikTok, Reels, Shorts용 짧은 광고 세로형 제품 이미지, 라이프스타일 이미지 앱/서비스 소개 영상 앱 화면, SaaS 대시보드, 웹 서비스 소개 앱 스크린샷, 웹사이트 화면 이커머스 상품 영상 상품 상세 페이지용 짧은 움직임 흰 배경 제품 사진, 사용 장면 이미지 이 글의 핵심은 텍스트만 입력해 영상을 만드는 방식이 아니라, 이미지를 먼저 준비하고 그 이미지를 자연스럽게 움직이는 광고 영상으로 확장하는 방식입니다. 만약 광고 영상 제작용 모델을 고르는 단계라면 이 글은 실무 워크플로우를 다루고, 모델 선택은 클링 3.0 vs 시댄스 비교 와 시댄스 2.0과 Veo 3.1 비교 를 함께 보면 판단하기 쉽습니다. ## 제품 광고 영상을 만드는 기본 워크플로우 English: Basic workflow for creating product ad videos 시댄스 2.0으로 광고 영상을 만들 때는 먼저 완성된 영상이 어디에 쓰일지 정해야 합니다. 같은 제품 이미지라도 SNS 광고, 웹사이트 히어로 영상, 앱 소개 영상, 상세 페이지 영상에 따라 화면 비율과 움직임이 달라져야 합니다. 추천 워크플로우는 다음과 같습니다. - 제품 또는 브랜드 이미지를 선택합니다. - 영상의 목적을 정합니다. 예: 클릭 유도, 제품 특징 설명, 브랜드 분위기 전달. - 플랫폼에 맞는 화면 비율을 정합니다. 예: 9:16, 1:1, 16:9. - 프롬프트에 움직임, 카메라, 분위기, 배경, 속도를 구체적으로 작성합니다. - 여러 버전을 생성해 제품 가시성, 메시지 전달력, 자연스러운 움직임을 비교합니다. - 가장 좋은 결과물을 편집 툴에서 자막, 로고, CTA와 함께 마무리합니다. 예를 들어 이커머스 제품 사진을 광고 영상으로 만들고 싶다면, 프롬프트는 제품을 바꾸는 것이 아니라 제품을 더 잘 보여주는 움직임에 집중해야 합니다. 좋은 방향: ``` 제품은 화면 중앙에 선명하게 유지하고, 카메라는 천천히 앞으로 이동합니다. 배경에는 부드러운 스튜디오 조명이 들어오고, 제품 표면에 자연스러운 하이라이트가 생깁니다. 전체 분위기는 프리미엄하고 깔끔한 광고 영상처럼 보이게 합니다. ``` 피해야 할 방향: ``` 멋진 광고 영상을 만들어줘. ``` 두 번째 프롬프트는 너무 넓기 때문에 제품이 변형되거나, 움직임이 과하거나, 브랜드와 맞지 않는 결과가 나올 가능성이 높습니다. ## 시댄스 2.0 광고 영상 프롬프트 예시 English: Seedance 2.0 ad video prompt examples 아래 예시는 이미지 한 장을 입력한 뒤, 시댄스 2.0에서 어떤 움직임을 만들지 설명하는 방식으로 작성했습니다. 실제 결과는 입력 이미지의 품질, 제품 위치, 배경, 화면 비율, 모델 설정에 따라 달라질 수 있습니다. ## 제품 광고 예시 English: Product ad example 입력 이미지: 흰 배경의 스테인리스 물병 제품 사진 ``` 입력 이미지의 스테인리스 물병 형태와 금속 질감을 그대로 유지합니다. 카메라는 천천히 앞으로 줌인하고, 부드러운 스튜디오 조명이 표면을 따라 이동합니다. 화면 하단에 자연스러운 한국어 광고 자막 느낌으로 "하루를 더 오래 시원하게"가 깔끔하게 나타나는 프리미엄 제품 광고 영상. 사람, 손, 얼굴, 동물 없음. 로고 없음. 물병 형태와 라벨 없는 표면을 왜곡하지 않음. ``` 사용 목적: - 랜딩 페이지 히어로 영상 - SNS 제품 광고 - 신제품 출시 티저 ## 숏폼 광고 예시 English: Short-form ad example 입력 이미지: 과일과 함께 배치된 캔 음료 제품 사진 ``` 입력 이미지의 캔 음료와 과일 구도를 유지합니다. 카메라는 캔을 중심으로 살짝 앞으로 다가가고, 물방울과 과일 주변의 빛이 상쾌하게 반짝입니다. 밝은 여름 숏폼 광고 느낌으로 빠르고 산뜻하게 연출합니다. 화면에 한국어 광고 자막처럼 "한 모금에 여름이 톡"이 짧게 나타나는 느낌. 사람, 손, 얼굴, 동물 없음. 캔 형태를 왜곡하지 않음. ``` 사용 목적: - TikTok 광고 - Instagram Reels - YouTube Shorts ## 앱 소개 영상 예시 English: App promo video example 입력 이미지: 앱 화면 또는 SaaS 대시보드 스크린샷 ``` 스마트폰과 앱 대시보드 구도를 유지합니다. 카메라는 화면을 향해 천천히 줌인하고, 주변 UI 카드가 부드럽게 떠오르며 데이터가 정리되는 느낌을 줍니다. 깔끔한 SaaS 제품 소개 영상처럼 전문적이고 간결하게 보이게 합니다. 한국어 광고 내레이션 문구 느낌으로 "일정을 한눈에, 팀워크를 더 빠르게"라는 메시지를 떠올리게 하는 분위기. 사람, 손, 얼굴 없음. UI가 과하게 왜곡되지 않게 유지. ``` 사용 목적: - 앱 소개 페이지 - 제품 업데이트 영상 - B2B SaaS 데모 광고 ## 숏폼 광고 영상용 프롬프트 작성법 English: How to write prompts for short-form ad videos 숏폼 광고는 길이가 짧기 때문에 프롬프트도 명확해야 합니다. 시청자가 처음 1-2초 안에 제품과 분위기를 이해할 수 있어야 하므로, 너무 많은 장면 전환이나 복잡한 스토리를 넣기보다 하나의 핵심 움직임에 집중하는 것이 좋습니다. 프롬프트에는 다음 요소를 포함하는 것이 좋습니다. - 제품 또는 주요 피사체가 무엇인지 - 제품이 화면에서 유지되어야 하는 위치 - 카메라 움직임: 줌인, 패닝, 틸트, 슬로우 모션 - 분위기: 프리미엄, 밝은, 역동적인, 미니멀한 - 배경 움직임 또는 조명 변화 - 피해야 할 요소: 제품 변형, 텍스트 왜곡, 과한 움직임 예시 구조: ``` [제품/피사체]는 화면에서 [위치/크기]를 유지합니다. 카메라는 [움직임]으로 이동하고, 배경은 [분위기/조명]을 가집니다. 영상은 [광고 유형]처럼 보이게 하며, [피해야 할 요소]는 발생하지 않게 합니다. ``` 광고 영상에서는 “무엇을 만들지”보다 “무엇을 유지해야 하는지”가 중요합니다. 제품 형태, 라벨, 브랜드 색상, 핵심 UI 같은 요소를 프롬프트에 명확히 적어야 결과물을 검수하기 쉽습니다. ## 이미지 한 장으로 마케팅 영상을 만드는 방법 English: How to create marketing videos from a single image 이미지 한 장을 영상으로 바꾸는 방식은 마케팅 팀에게 특히 유용합니다. 이미 승인된 제품 이미지나 캠페인 이미지를 활용할 수 있기 때문에, 완전히 새로운 영상을 만드는 것보다 브랜드 일관성을 유지하기 쉽습니다. 좋은 입력 이미지를 고르는 기준은 다음과 같습니다. - 제품이나 주요 피사체가 선명해야 합니다. - 배경이 너무 복잡하지 않아야 합니다. - 제품 라벨이나 UI 텍스트가 작게 뭉개져 있지 않아야 합니다. - 영상으로 만들었을 때 움직일 여지가 있어야 합니다. - 플랫폼에 맞는 화면 비율을 고려해야 합니다. 이미지-to-video 방식에서는 프롬프트가 입력 이미지를 “다시 그리는” 방향이 아니라 “움직이게 하는” 방향이어야 합니다. 예를 들어 제품 사진을 넣었다면: ``` 제품 디자인과 라벨은 그대로 유지하고, 카메라만 천천히 앞으로 이동합니다. 배경에는 부드러운 빛의 움직임이 생기고, 제품 주변에는 깨끗한 스튜디오 광고 느낌의 하이라이트가 나타납니다. ``` 이렇게 작성하면 시댄스 2.0이 제품 자체를 바꾸기보다, 기존 이미지를 바탕으로 광고 영상처럼 연출하는 데 집중할 수 있습니다. ## 광고 영상 제작 시 체크해야 할 요소 English: Things to check when creating ad videos AI로 만든 광고 영상은 생성 후 검수가 중요합니다. 특히 상업적 용도로 사용할 경우에는 단순히 보기 좋은지보다 실제 광고 소재로 안전하게 사용할 수 있는지를 확인해야 합니다. 체크리스트: - 제품 형태가 원본과 다르게 변형되지 않았는가? - 로고, 라벨, UI 텍스트가 왜곡되지 않았는가? - 제품의 기능이나 효능을 과장하지 않았는가? - 인물, 배경, 브랜드 요소가 광고 정책에 맞는가? - 플랫폼별 화면 비율과 길이에 맞는가? - 자막, CTA, 로고를 추가할 공간이 남아 있는가? - 여러 버전 중 클릭을 유도할 만한 첫 장면이 있는가? 특히 제품명, 가격, 할인율, 법적 문구처럼 정확해야 하는 텍스트는 AI 영상 안에 직접 생성하기보다, 후반 편집 단계에서 별도로 넣는 것이 안전합니다. ## PiAPI 플레이그라운드에서 이미지-to-video 데모 테스트하기 English: Test an image-to-video demo in the PiAPI playground 아래 PiAPI 플레이그라운드 데모에서는 시댄스 2.0을 바로 테스트할 수 있습니다. 프롬프트 예시를 읽은 뒤 제품 이미지나 마케팅 이미지를 넣어 image-to-video 결과를 확인해볼 수 있습니다. 데모는 텍스트-to-video보다 이미지-to-video 흐름에 맞춰져 있습니다. 이 글의 핵심이 광고 영상과 숏폼 마케팅 영상이기 때문에, 사용자가 이미 가지고 있는 제품 사진, 앱 화면, 브랜드 이미지를 영상으로 바꾸는 경험을 보여주는 편이 더 자연스럽습니다. 추천 데모 구성: - 모델: Seedance 2.0 - 기본 흐름: 이미지-to-video - 기본 목적: 제품 광고 영상 생성 - 기본 길이: 짧은 숏폼 테스트용 영상 - 기본 프롬프트: 제품을 유지하고 카메라 움직임과 조명만 더하는 광고 영상 프롬프트 데모에 넣을 기본 프롬프트 예시는 다음과 같습니다. ``` 입력 이미지의 제품 디자인과 구도를 유지합니다. 카메라는 제품을 향해 천천히 줌인하고, 배경에는 부드러운 스튜디오 조명과 자연스러운 하이라이트가 생깁니다. 영상은 프리미엄 제품 광고처럼 깔끔하고 세련되게 보이게 합니다. 제품 형태와 라벨은 왜곡하지 않습니다. ``` 이 데모는 독자가 시댄스 2.0을 단순히 읽고 끝내는 것이 아니라, 실제 광고 영상 제작 흐름을 바로 체험하게 만드는 역할을 합니다. 모델별 결과 차이가 궁금하다면 시댄스 2.0과 베오 3.1 비교 와 Hunyuan AI vs Seedance 2.0 이미지-to-video 비교 도 함께 참고할 수 있습니다. ## 시댄스 2.0 가격과 크레딧은 어떻게 확인하나요? English: How to check Seedance 2.0 pricing and credits 시댄스 2.0 가격은 사용하는 모델, 해상도, 영상 길이, 생성 방식에 따라 달라질 수 있습니다. 따라서 실제 광고 제작이나 API 연동 전에 최신 가격과 크레딧 사용 방식을 확인하는 것이 좋습니다. PiAPI에서는 시댄스 2.0 페이지 에서 모델 정보와 테스트 흐름을 확인할 수 있습니다. 광고 영상처럼 여러 버전을 반복 생성해야 하는 경우에는, 한 번에 고해상도 결과물을 만들기보다 낮은 비용의 설정으로 콘셉트를 먼저 테스트한 뒤 최종 후보만 높은 품질로 생성하는 방식이 효율적입니다. API 연동이나 모델별 과금 구조까지 자세히 확인해야 한다면 시댄스 2.0 사용법, 가격, API 가이드 를 참고하세요. 이 글은 광고 소재 제작 흐름에 집중하고, 해당 가이드는 Seedance API 사용 흐름과 가격 확인에 더 초점을 둡니다. 추천 테스트 방식: - 낮은 해상도 또는 빠른 모델로 프롬프트 방향을 확인합니다. - 제품이 잘 유지되는 프롬프트를 고릅니다. - 같은 입력 이미지로 3-5개 버전을 생성합니다. - 가장 좋은 결과만 최종 광고 소재용으로 다시 생성합니다. - 편집 단계에서 자막, 로고, CTA를 추가합니다. 이렇게 하면 크레딧을 더 효율적으로 쓰면서도 광고 소재 품질을 관리할 수 있습니다. ## PiAPI에서 시댄스 2.0 테스트하기 English: Test Seedance 2.0 on PiAPI 시댄스 2.0을 광고 영상 제작에 활용하려면 먼저 작은 테스트부터 시작하는 것이 좋습니다. 하나의 제품 이미지, 하나의 광고 목적, 하나의 플랫폼을 정한 뒤 프롬프트를 조금씩 바꿔보면 어떤 움직임이 브랜드에 가장 잘 맞는지 빠르게 확인할 수 있습니다. PiAPI의 시댄스 2.0 API 를 사용하면 Seedance 2.0 기반 영상 생성 워크플로우를 테스트하고, 이후 제품 페이지나 앱, 자동화된 마케팅 제작 파이프라인에 연결할 수 있습니다. 기본적으로는 다음 순서로 시작해보세요. - 제품 이미지 또는 마케팅 이미지를 준비합니다. - 원하는 영상 목적을 정합니다. 예: 제품 광고, 숏폼 티저, 앱 소개. - 이미지-to-video 프롬프트를 작성합니다. - 여러 버전을 생성해 비교합니다. - 최종 결과에 자막, 로고, CTA를 추가합니다. 이미 시댄스 2.0의 기본 사용법을 먼저 보고 싶다면 시댄스 2.0 사용법 가이드 를 참고하고, 다른 영상 모델과 비교하고 싶다면 시댄스와 클링 비교 도 함께 확인할 수 있습니다. Dreamina와 Sora까지 포함해 더 넓게 비교하고 싶다면 Dreamina Seedance 2.0 vs Sora 2 비교 도 연결해서 읽기 좋습니다. ## FAQ ### 시댄스 2.0으로 광고 영상을 만들 수 있나요? English: Can you create commercial videos with Seedance 2.0? 네. 시댄스 2.0은 제품 이미지, 브랜드 이미지, 앱 화면 등을 기반으로 짧은 광고 영상이나 숏폼 마케팅 영상을 만드는 데 활용할 수 있습니다. 다만 최종 광고 소재로 사용하기 전에는 제품 표현, 브랜드 정책, 플랫폼 광고 정책을 검토해야 합니다. ### 시댄스 2.0은 텍스트-to-video보다 이미지-to-video에 더 적합한가요? English: Is Seedance 2.0 better suited for image-to-video than text-to-video? 광고와 마케팅 용도에서는 이미지-to-video 방식이 더 관리하기 쉽습니다. 제품 사진이나 캠페인 이미지를 시작점으로 쓰면 제품 형태, 브랜드 색상, 구도를 유지하기 좋기 때문입니다. 텍스트-to-video는 콘셉트 테스트에는 좋지만, 실제 제품 광고에서는 입력 이미지를 함께 쓰는 편이 더 안정적입니다. ### 숏폼 광고 영상에는 어떤 프롬프트가 좋나요? English: What type of prompt works best for short-form ad videos? 짧고 명확한 프롬프트가 좋습니다. 제품을 화면에서 어떻게 유지할지, 카메라가 어떻게 움직일지, 배경과 조명이 어떤 분위기인지, 피해야 할 왜곡은 무엇인지 구체적으로 적는 것이 좋습니다. ### 시댄스 2.0 가격은 어디서 확인하나요? English: Where can you check Seedance 2.0 pricing? 시댄스 2.0 가격은 모델, 해상도, 영상 길이, 생성 방식에 따라 달라질 수 있습니다. 최신 가격과 크레딧 정보는 PiAPI의 시댄스 2.0 페이지 에서 확인하는 것이 좋습니다. ### 드리미나와 시댄스는 어떤 관계인가요? English: What is the relationship between Dreamina and Seedance? 시댄스 2.0은 바이트댄스 계열의 AI 영상 모델로 알려져 있으며, 드리미나 같은 제작 도구와 함께 언급되는 경우가 많습니다. 드리미나와 Seedance 2.0의 위치를 다른 모델과 함께 보고 싶다면 Dreamina Seedance 2.0 vs Sora 2 비교 를 참고할 수 있습니다. 이 글에서는 브랜드 배경보다 실제 광고 영상 제작 워크플로우에 집중했습니다. ## How to Generate AI Images From the Terminal With an AI CLI Tool Learn how to use a command line AI tool to generate AI images from your terminal, save outputs locally, batch prompts, and automate image workflows with PiAPI CLI. A command line AI tool lets you run AI generation tasks from your terminal instead of clicking through a web interface. For image generation, that means you can write a prompt, choose a model, generate an image, save the output, and reuse the same workflow inside scripts or automation. This guide shows how to generate AI images from terminal workflows with PiAPI CLI . If you still need to install the CLI or connect your API key, start with the PiAPI CLI quick start guide first, then come back to this workflow guide. Quick answer To generate AI images from the terminal, install a command line AI tool such as PiAPI CLI, authenticate with your API key, run an image model with a text prompt, and add --download when you want to save the result locally. ## What Is a Command Line AI Tool? A command line AI tool is a CLI program that lets you call AI models with terminal commands. Instead of opening a web app, you run commands, pass inputs as flags or arguments, and receive structured outputs that can be saved, parsed, or reused. Definition A command line AI tool is software that lets developers run AI models from a terminal by passing prompts, files, and settings as command arguments. For image generation, it turns terminal commands into repeatable text-to-image workflows. For AI image generation, a command line AI tool is useful when you want to: - Generate images from repeatable prompts - Save outputs into a project folder - Test several prompt variations quickly - Batch generate images from a list of prompts - Add image generation to shell scripts, CI jobs, or AI agent workflows PiAPI CLI is built for multimodal generation, so the same terminal workflow can support image, video, audio, 3D, and chat models. ## Why Generate AI Images From the Terminal? The terminal is useful when the image generation task is part of a repeatable workflow. A web UI is convenient for visual exploration, but a CLI gives you a command that can be copied, edited, versioned, and automated. Use terminal-based image generation when you need to: - Create many variations of a prompt - Save generated assets into predictable folders - Test model behavior while building an app - Run the same generation workflow more than once - Connect image generation with scripts or AI agents In short: use a command line AI tool when the image task needs to be repeatable, scriptable, or part of a larger development workflow. Best fit: A terminal image generation workflow is best for developers who need repeatability. If the same prompt, model, or output folder will be used more than once, a CLI is usually more efficient than clicking through a web UI. ## What You Need Before You Start Before generating images from the terminal, make sure you have: - Node.js 18 or newer - A PiAPI account - A PiAPI API key - PiAPI CLI installed You can create an account and get your API key from the PiAPI workspace . If you have not installed it yet, use: Then authenticate: For a fuller setup walkthrough, use the PiAPI CLI quick start guide . ## Generate Your First AI Image From the Terminal After authentication, you can generate an image with piapi run . The exact model name and parameters may change depending on the model you choose, but the workflow is the same: choose a model, pass a prompt, and run the command. Expected result: PiAPI CLI sends the prompt to the selected image model and returns the generation result in your terminal. This is the simplest way to use PiAPI CLI as an AI image generation CLI: one command, one model, one prompt. ## Save AI Images Locally From the Command Line For real work, you usually want the image file saved locally instead of only viewing a result URL. Use the CLI download option when you want PiAPI CLI to save the generated output into your current folder. Expected result: the generated image is downloaded locally. You can also organize outputs by running commands from a project folder: If the CLI supports an output directory flag for your selected model, you can also keep outputs in a dedicated folder: This makes terminal image generation practical for design assets, ad variations, test images, and content workflows. ## Batch Generate AI Images From Multiple Prompts Batch generation is where a command line AI tool becomes more useful than a web UI. Instead of entering prompts one at a time, you can keep prompts in a text file and loop through them. Create a file called prompts.txt : Then run the loop for your operating system. Before running the batch command, confirm the file contains prompts. For Windows PowerShell: For macOS or Linux with bash/zsh: For Windows PowerShell: For macOS or Linux with bash/zsh: Expected result: PiAPI CLI generates one image for each prompt in the file and saves the outputs locally. If you are using Windows and see an error such as Missing opening '(' after keyword 'while' , it means you pasted a bash command into PowerShell. Use the PowerShell version instead. This workflow is useful when you want to test multiple creative directions, build a prompt library, or generate batches of marketing assets. ## Use a Command Line AI Tool in Scripts and Automation A CLI workflow can also be wrapped inside a script. This is useful when image generation is part of a repeatable internal process. For example, create a script called generate-assets.sh : Then run: Expected result: the script creates a folder and saves several generated images into it. On Windows, you can create a PowerShell script called generate-assets.ps1 : Then run: This is especially useful for teams that want a repeatable asset-generation workflow. Instead of remembering every prompt manually, you keep the workflow in a script and update it when needed. ## Generate Images With Flux and Other Image Models From the CLI Many developers search for a Flux CLI because they want to generate FLUX images from the terminal. PiAPI CLI can support this kind of model-specific workflow while still keeping one command-line interface across multiple model families. For model-specific details, you can also review the Flux API . For example, you can use a Flux-style workflow for fast image generation: You can also use the same CLI approach for other supported image models, depending on your account and available model list. If your workflow needs a different image model, PiAPI also provides options such as GPT Image 2 API , Nano Banana API , and Seedream API . Expected result: PiAPI CLI prints available models and their supported input fields. This is the advantage of using PiAPI CLI instead of a separate tool for every model: you can keep one terminal workflow and switch models as needed. ## Command Line AI Tool vs Web UI vs Direct API Each option is useful for a different workflow. Option Best for Tradeoff Web UI Visual exploration and one-off generation Harder to repeat or automate Command line AI tool Repeatable terminal workflows, batch prompts, local files, scripting Requires comfort with terminal commands Direct API Production apps and backend integrations Requires more engineering setup Use the web UI when you want to explore visually. Use PiAPI CLI when your workflow starts in the terminal. Use the direct API when you are building image generation into a product or backend service. The simplest rule is: web UI for exploration, CLI for repeatable local workflows, and API for production software. PiAPI CLI sits between the web UI and direct API because it gives developers a scriptable interface without requiring a full backend integration. ## Common Issues When Generating Images From Terminal ## The command is not found If piapi is not recognized, check whether the CLI is installed globally: You can also run it without a global install: ## The CLI is not authenticated If the CLI cannot access your account, log in again: For shared machines or scripts, prefer an environment variable: ## The model name or parameter is wrong Use the model list command to confirm available models and expected fields: If a command fails, check the model name, prompt parameter, file paths, and whether your account has access to the selected model. ## FAQ ## What is a command line AI tool? A command line AI tool is a CLI program that lets you run AI tasks from terminal commands. For image generation, it lets you send prompts to image models, receive results, and save outputs without opening a web interface. ## Can I generate AI images from terminal? Yes. With PiAPI CLI, you can generate AI images from terminal commands by choosing an image model, passing a prompt, and downloading the result locally. This makes the workflow repeatable because the same command can be saved, edited, and reused. ## Is PiAPI CLI only for image generation? No. PiAPI CLI supports multimodal workflows, including image, video, audio, 3D, and chat models. This article focuses on image generation because it is one of the most common terminal automation use cases. ## Can I batch generate AI images? Yes. You can place prompts in a text file and loop through them with PowerShell, bash, or zsh. This lets you generate multiple images from the command line without entering each prompt manually. ## Should I use CLI or API for AI image generation? Use CLI when you want fast terminal workflows, local testing, batch prompts, or automation scripts. Use the API when you are building image generation into a production app or backend service. ## Start Generating AI Images With PiAPI CLI PiAPI CLI gives developers a practical way to generate AI images from terminal workflows. You can run one-off prompts, save outputs locally, batch generate images, and reuse the same workflow inside scripts or AI agents. To explore the product, visit the PiAPI CLI page . If you need setup help first, read the PiAPI CLI quick start guide . ## Kling 3 vs Sora 2: Which AI Video Model Should You Use? Compare Kling 3 vs Sora 2 for AI video generation, API access, pricing, prompt following, motion quality, and production workflows. Test both models in one playground. Kling 3 and Sora 2 are two of the most important AI video models for creators, developers, and product teams comparing modern text-to-video workflows. Both can turn prompts into short videos, but they are not identical. Kling 3 gives developers more configurable video-generation options through PiAPI, while Sora 2 is designed for cinematic text-to-video generation with a simple prompt-first workflow. Quick verdict: choose Kling 3 if you want more control over API settings, duration, resolution tiers, optional audio, and advanced workflows such as multi-shot generation. Choose Sora 2 if you want a simpler text-to-video workflow for polished short-form scenes and want to test Sora video generation through a straightforward API. This guide compares Kling 3 vs Sora 2 across video quality, motion, prompt following, audio, API access, pricing, and production use cases. You can also test both models in the embedded playground using the same text prompt. Definition: Kling 3 vs Sora 2 is a comparison between two AI video generation models: Kling 3 is better for configurable API workflows, while Sora 2 is better for simple prompt-first text-to-video testing. ## Quick Verdict: Kling 3 vs Sora 2 Kling 3 is better suited for teams that want configurable AI video generation through an API. In PiAPI, Kling 3 supports text-to-video, image-to-video, single-shot generation, multi-shot generation, flexible duration from 3 to 15 seconds, 720p and 1080p pricing tiers, and optional audio. Sora 2 is better suited for simple text-to-video testing where the user wants to enter a prompt and generate a short video without managing many settings. In PiAPI, the Sora 2 text-to-video endpoint uses the sora2-video task type and is currently positioned around 720p short-form generation. If you are choosing an AI video model for a product or API workflow, the best answer is not simply "Kling is better" or "Sora is better." The better model depends on the type of video you want to create, how much control you need, and whether you care more about simple prompt-based generation or configurable production workflows. ## Try Kling 3 and Sora 2 With the Same Prompt ## Kling 3 vs Sora 2 Comparison Table Category Kling 3 Sora 2 Main use case Configurable AI video generation through PiAPI Simple text-to-video generation through PiAPI PiAPI model kling sora2 PiAPI task type video_generation sora2-video Text-to-video Yes Yes Image-to-video Yes Optional first-frame style input in the API/playground Duration 3-15 seconds 4, 8, or 12 seconds in the current playground config Resolution 720p and 1080p pricing tiers 720p currently available in the PiAPI docs Advanced control Single-shot and multi-shot support Simpler prompt-first workflow ## API Access and Docs Both models can be tested through PiAPI. The Kling API gives developers a production path for Kling video generation, while the Kling 3 API documentation covers the kling model, video_generation task type, single-shot and multi-shot workflows, duration, resolution, and audio options. The Sora 2 text-to-video documentation covers the sora2 model and sora2-video task type. ## Pricing Comparison Model or setting PiAPI pricing reference Kling 3 720p no audio $0.10/s Kling 3 720p with audio $0.15/s Kling 3 1080p no audio $0.15/s Kling 3 1080p with audio $0.20/s Sora 2 text-to-video $0.08/s Pricing can change, so confirm current pricing in the PiAPI documentation before building a production workflow. ## Example 1: Creative Commercial Scene This example uses the same prompt in Kling 3 and Sora 2 to compare character consistency, product-style lighting, reflective surfaces, steam, and camera framing. Prompt: ## Kling 3 Evaluation Kling 3 preserved the robot chef, glowing dessert, reflective counter, and controlled studio lighting clearly. The scene stays wide and centered, which makes it useful for a product-style commercial comparison. The output is less playful than the prompt in some frames, but it keeps the subject stable and the composition readable. ## Sora 2 Evaluation Sora 2 produced a strong robot-chef scene with a glowing dessert, visible steam, and a clear futuristic kitchen setup. The character reads more friendly and expressive than the Kling 3 version, and the reflective surfaces are convincing. The main limitation is that the output returned in portrait framing even though the prompt and request specified 16:9, which makes it less ready for this specific blog layout without extra handling. ## Example 2: Motion and Natural Scene This example compares how both models handle animal motion, water interaction, handheld tracking, sunset lighting, and subject consistency. Prompt: A golden retriever running through a shallow beach at sunset, water splashing around its paws, handheld tracking shot, warm cinematic lighting, natural motion, realistic fur movement, joyful summer mood, 16:9. ## Kling 3 Evaluation Kling 3 kept the golden retriever in a consistent side-running pose with warm sunset lighting and visible beach reflections. The framing matches the requested 16:9 format, which makes it easier to embed in a comparison article or product page. Water interaction is present and the motion reads naturally, with only minor softness around the legs during movement. ## Sora 2 Evaluation Sora 2 produced a polished beach scene with a clear golden retriever, warm sunset lighting, and strong facial detail. The output looks cinematic and friendly, but it reads more like a forward-facing approach shot than a handheld tracking shot. It also returned in portrait framing despite the 16:9 request, so it is visually strong but less aligned with the requested production format. ## Which Model Should You Choose? Choose Kling 3 if you want: - more API configuration - text-to-video and image-to-video options - duration flexibility - 720p and 1080p tiers - optional audio - multi-shot workflows - a model that fits production-style video API workflows Choose Sora 2 if you want: - a simpler text-to-video workflow - short-form prompt-based generation - a straightforward API request shape - a strong model for cinematic prompt exploration - a clean way to test Sora video generation through PiAPI ## Final Verdict Kling 3 and Sora 2 are both useful AI video models, but they are best for different workflows. Kling 3 is the stronger fit when you want more control, more configuration, and a developer-friendly video API workflow. Sora 2 is the stronger fit when you want a simple text-to-video path and want to test Sora-style video generation with fewer settings. For most developers comparing Kling vs Sora, the practical answer is to test both models with the same prompt. Use the embedded playground above to compare the workflow, then choose the model that best matches your product, creative style, and budget. ## FAQ ## What is the main difference between Kling 3 and Sora 2? The main difference is workflow control. Kling 3 gives developers more configuration for duration, resolution, audio, and multi-shot video generation, while Sora 2 keeps the workflow simpler for prompt-first text-to-video testing. ## Is Kling 3 better than Sora 2? Kling 3 is better if you need more configurable API controls, flexible duration, optional audio settings, and advanced workflows such as multi-shot generation. Sora 2 may be better if you want a simpler text-to-video workflow for short cinematic prompts. ## Does PiAPI support Kling 3 and Sora 2? Yes. PiAPI provides API access for Kling 3 and Sora 2, so developers can compare both models without building separate integrations for each provider. ## Can I test Kling 3 vs Sora 2 in the browser? Yes. The embedded playground in this guide lets readers switch between Kling 3 and Sora 2 and test the same text prompt. Start testing Kling 3 and Sora 2 through PiAPI today. Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## PiAPI CLI Quick Start: Generate AI Images and Videos from Your Terminal Learn how to install PiAPI CLI, authenticate with your API key, and generate AI images or videos from your terminal using PiAPI's multimodal AI models. PiAPI CLI is the official command line interface for PiAPI. It lets you call multimodal AI models for image generation, video generation, audio, 3D, and LLM workflows directly from your terminal. For the full product overview, visit the PiAPI CLI page . This quick start shows you how to install PiAPI CLI, authenticate with your API key, run your first image command, start an async video task, and discover available models. If you want an AI CLI tool for terminal-first media generation, scripts, and agent workflows, this guide gives you a short path from install to first command. ## What Is PiAPI CLI? Quick definition PiAPI CLI is a Node.js command line tool for running PiAPI models from a terminal. It helps developers generate AI images, videos, audio, 3D assets, and LLM responses without switching to a web dashboard. PiAPI CLI is a command line AI tool for accessing PiAPI models without opening a web dashboard. After installing the package, you can use the piapi command to run supported models, upload local files, download results, check quota, and inspect model schemas. The full PiAPI CLI overview also includes the latest command reference and supported model details. Unlike a generic AI coding assistant, PiAPI CLI is focused on multimodal generation. Use it when you want one terminal workflow for AI images, videos, audio, 3D assets, and LLM calls. The CLI is useful when you want repeatable AI generation workflows, terminal-first development, or automation that can be reused inside scripts and AI agents. ## Why Use an AI CLI for Image and Video Generation? An AI CLI is useful when the generation workflow starts outside a browser. Instead of clicking through a web app each time, you can save the exact command, rerun it, change parameters, and combine it with scripts. For image and video generation, this is helpful when you need to: - Generate assets from repeatable prompts - Run quick tests while building an app or prototype - Upload local files from a project folder - Save outputs to a predictable directory - Return JSON for scripts, CI jobs, or agent tools PiAPI CLI is especially useful when you want to switch between model families without learning a different command line tool for each provider. ## PiAPI CLI at a Glance Package piapi-cli Command piapi Runtime Node.js 18 or newer Main use case Terminal-based multimodal AI generation Useful for Developers, automation workflows, and AI agents ## Prerequisites Before you start, make sure you have: - Node.js 18 or newer - A PiAPI API key - A terminal app You can use PiAPI CLI globally after installation, or run it with npx if you do not want to install it first. ## Install PiAPI CLI Install the piapi-cli package with your preferred JavaScript package manager. Expected result: the piapi command becomes available globally in your terminal. You can also install it with pnpm, bun, or yarn. If you plan to use PiAPI CLI often, a global install is the easiest option. If you only want to test the CLI once, run it with npx instead: Expected result: your terminal prints the PiAPI CLI help menu. ## Authenticate With Your PiAPI API Key After installation, connect the CLI to your PiAPI account. Expected result: PiAPI CLI stores your API key locally and can use it for future commands. To check whether authentication is configured, run: On shared machines, it is safer to use an environment variable instead of passing your key directly as a command argument. PiAPI CLI reads the API key from command flags, environment variables, or the local config file. Avoid committing API keys to scripts, docs, or repositories. Use environment variables or local config for repeatable workflows. ## Generate Your First AI Image From Terminal Once authenticated, you can generate an AI image from your terminal with piapi run . Expected result: PiAPI CLI sends the prompt to the flux-dev model and returns the generation result in your terminal. You can also use gpt-image-2 for OpenAI-compatible image generation. This makes PiAPI CLI useful as an AI image generation CLI when you want to generate images from terminal commands, scripts, or agent tools. If your workflow is focused on Flux image generation, you can also explore the Flux API for model-specific details. ## Generate Your First AI Video From Terminal Video generation usually takes longer than image generation, so video models should be started as async tasks. Expected result: PiAPI CLI creates an async video generation task and returns a task ID you can use to check the result later. To check the task later, copy the returned task ID and run: Expected result: PiAPI CLI shows the current task status and result information when it is available. This workflow is useful when you want to generate AI videos from terminal scripts without blocking your whole process. For model-specific video workflows, see the Sora 2 API and Kling API pages. ## Upload Local Files and Download Results PiAPI CLI can upload local files when an input value starts with @ . For example, this command uploads a local image, removes the background, and downloads the output into ./out . Expected result: PiAPI CLI uploads photo.png , runs the background removal task, and saves the result in the out folder. The --download flag saves result URLs or supported inline payloads. The --out-dir flag controls where downloaded files are written. ## Explore Models and Check Quota Use the model commands to browse available models and inspect schemas. For a broader reference of PiAPI CLI model support, use the PiAPI CLI overview alongside the piapi model list command. ## Useful PiAPI CLI Flags Flag What it does --async Returns a task ID immediately for longer-running jobs. --download Downloads result files automatically. --out-dir <path> Sets the folder for downloaded output files. --stream Streams LLM output as it arrives. --dry-run Previews a request without executing it. --output json Prints JSON output for scripts and automation. --webhook <url> Sends callbacks to a webhook URL. --quiet Suppresses progress indicators. --non-interactive Fails instead of prompting for missing input. For automation, --output json , --quiet , and --non-interactive are especially useful because they make CLI behavior easier to parse in scripts. ## Use PiAPI CLI With AI Agents and Automation PiAPI CLI is not only for manual terminal usage. It can also be used inside AI agent and automation workflows where a tool needs to generate media, inspect model schemas, or run repeatable tasks. For Claude Code, Cursor, Codex, and other agent environments that support skills, the package README provides this command: Expected result: the PiAPI CLI skill is added to your agent environment, so the agent can use PiAPI workflows more directly. You can also use PiAPI CLI in shell scripts, CI jobs, and local automation when you need a command line interface for image, video, audio, or 3D generation. If you are building broader automation flows, the Midjourney n8n integration guide shows how PiAPI fits into no-code and workflow automation contexts. For scripts and agents, prefer JSON output when you need another tool to read the result. Expected result: PiAPI CLI prints structured JSON that a script or agent can parse. ## Troubleshooting Common PiAPI CLI Issues If your first command does not work, check these common issues before changing your prompt or model. ## piapi: command not found Your terminal cannot find the global piapi command. Try opening a new terminal window, confirm the global install completed, or use the no-install command: ## Invalid or missing API key Run the auth status command: If the CLI is not authenticated, log in again or set PIAPI_API_KEY in your environment. ## Video command returns a task ID instead of a file That is expected for async video generation. Use the task ID to check progress: Video tasks take longer than image tasks, so the final result may not be available immediately. ## When Should You Use PiAPI CLI Instead of the Web UI? Use PiAPI CLI when you want a workflow that can be repeated, scripted, or handed to an AI agent. The web UI is useful for visual exploration. The CLI is better when you want to: - Generate assets from a repeatable command - Run model calls from a script - Upload local files without manual steps - Save outputs into a known folder - Use JSON output for automation - Give AI agents access to media generation tools - Keep image, video, audio, 3D, and LLM calls in one terminal workflow If your work starts in a terminal, PiAPI CLI keeps the whole workflow there. ## FAQ ## What is PiAPI CLI? PiAPI CLI is the official Node.js command line interface for PiAPI. It lets you call supported AI models from your terminal using the piapi command, including models for images, videos, audio, 3D, and LLM workflows. ## Who should use PiAPI CLI? PiAPI CLI is best for developers, automation builders, and AI agent users who want repeatable terminal commands for multimodal AI generation instead of manual web-dashboard steps. ## How do I install PiAPI CLI? Install it globally with npm install -g piapi-cli , or run it without installing by using npx piapi-cli@latest --help . ## Can I generate AI images from terminal? Yes. For example, you can run piapi run flux-dev prompt="a corgi in space" to send an image generation request from your terminal. ## Can I generate AI videos from terminal? Yes. Use an async video command such as piapi run sora2-pro prompt="ocean waves" --async to create a video generation task. ## Does PiAPI CLI support Flux? Yes. The package README includes flux-dev examples, including piapi run flux-dev prompt="a corgi in space" . ## Does PiAPI CLI support Kling? Yes. The package README lists Kling among the supported video model families and includes kling-3 in command examples. ## Can I use PiAPI CLI without installing it? Yes. You can run npx piapi-cli@latest --help to try the CLI without a global install. ## Where does PiAPI CLI save output files? When you use --download , PiAPI CLI saves outputs to the current working directory by default. Use --out-dir <path> to choose another folder. ## Is PiAPI CLI useful for AI agents? Yes. PiAPI CLI is useful for AI agents because it exposes image, video, audio, 3D, and LLM workflows as terminal commands that agents can run or reason about. ## Start Building With PiAPI CLI PiAPI CLI gives developers and AI agents a practical way to generate images, videos, audio, 3D assets, and LLM responses from the command line. Start with installation, authenticate with your API key, then use piapi run to call the model you need. To see the main CLI overview, visit the PiAPI CLI page . To explore the full platform, check the PiAPI docs and model catalog. ## Why Your AI Kiss Generator Video Fails and How to Fix It Learn why AI kiss generator videos fail, look blurry, or appear distorted. Fix input image issues and create better Kling AI Kiss videos with PiAPI. If your AI kiss generator video failed, looked blurry, or produced distorted faces, the problem is usually the input image. Most AI kiss video generator tools depend heavily on face visibility, subject placement, lighting, image sharpness, and whether the image clearly shows two people or characters. The Kling AI Kiss generator on PiAPI uses an effect-based workflow. That means the main control is not a custom prompt. The most important thing you can control is the image you upload. This guide explains why AI kiss videos fail, how to fix common input problems, and what kind of photo works best before you try again. ## What Is an AI Kiss Generator? An AI kiss generator is a video effect tool that turns an uploaded image of two people or characters into a short kissing video. Instead of relying mainly on text prompts, tools like Kling AI Kiss depend heavily on the clarity, framing, and face visibility of the input image. The most reliable way to improve an AI kiss generator result is to fix the source image first. A clear two-person image gives the model more stable facial details, cleaner subject placement, and a better starting point for natural motion. ## Quick Answer: Why AI Kiss Generator Videos Fail AI kiss generator videos usually fail when the input image does not give the model enough clear visual information. Common causes include blurry faces, blocked mouths or eyes, extreme side profiles, low resolution, dark lighting, too many people in the frame, or two subjects positioned too far apart. To fix a failed AI kiss video, start by replacing the input image. Use a sharper two-person photo with visible faces, natural lighting, minimal occlusion, and enough room around both subjects for the kiss motion to look natural. ## Key Takeaways - AI kiss videos usually fail because the uploaded image is unclear, crowded, poorly lit, or missing two visible faces. - If an AI kiss video looks weird, check face angle, face coverage, subject distance, and image resolution first. - Kling AI Kiss is an effect-based workflow, so better input images matter more than prompt editing. - The best photo for an AI kiss generator shows two clear subjects, visible facial features, balanced lighting, and simple framing. - Always use images you have the right to use, especially for romantic or intimate AI video effects. ## Common AI Kiss Video Problems and Fixes The fastest way to improve a failed AI kiss video is to diagnose the input image before generating again. Use this table as a practical checklist. Problem Why it happens How to fix it AI kiss video looks blurry The source image is low resolution, compressed, or out of focus Use a sharper image where both faces are clear AI kiss video looks weird Face angle, pose, or spacing makes the kiss motion difficult Use a photo where both subjects face the camera or turn slightly toward each other Faces look distorted Mouth, eyes, or face shape is blocked by hair, hands, masks, sunglasses, or heavy shadows Choose an image with unobstructed facial features Wrong person moves The image includes more than two people or unclear main subjects Crop the image to only the two intended subjects Kiss motion feels unnatural Subjects are too far apart or the image has awkward body positioning Use a closer two-person composition with enough visible upper body context AI kiss generator not working The upload may not contain a usable two-subject image for the effect Try a clearer JPEG, JPG, or PNG image with two visible faces Kling kiss generator failed The effect may not detect the intended pair clearly Simplify the image and avoid group photos, heavy filters, or extreme side profiles If your goal is to use a free AI kissing video generator or test an AI kiss free workflow, these fixes still matter. Free demos and paid tools both need a clear reference image to produce stable video output. ## The Best Photo for an AI Kiss Generator The best photo for an AI kiss generator is a clean two-subject image where the model can easily understand who should be animated. The image does not need to be perfect, but it should avoid ambiguity. Input factor Better for AI kiss generation More likely to fail Subjects Exactly two clear people or characters Group photos or unclear main subjects Faces Both faces visible with readable features Hidden faces, masks, sunglasses, or heavy hair coverage Framing Medium-close composition with both subjects nearby Wide shots where subjects are far apart Image quality Sharp original image with balanced lighting Blur, compression, dark lighting, or strong filters For AI kiss video tools, the input image is the main instruction. If the image clearly shows the intended pair, the model has less ambiguity to solve during generation. ## Use Two Clear Main Subjects Choose an image with exactly two main people or characters. If there are three or more people in the frame, the model may animate the wrong subjects or create unstable motion. For Kling AI kissing video generation, a two-person crop is usually better than a wide group photo. The simpler the subject layout, the easier it is for the effect to understand the scene. ## Keep Both Faces Visible Both faces should be visible enough for the model to understand face shape, direction, and expression. Avoid photos where one subject is hidden behind hair, turned fully away, or cropped out of the frame. Slight angles can work, but extreme side profiles are more likely to produce a distorted AI kiss video. ## Avoid Blocked Facial Features Mouths, eyes, and lower face shape matter for kiss motion. Sunglasses, masks, hands, heavy hair coverage, and strong shadows can all make the result less stable. If your AI kiss video looks weird, check whether the original image hides important facial details. ## Use Balanced Lighting Dark photos, harsh shadows, and overexposed faces can reduce output quality. Natural light or soft indoor lighting usually gives the AI kiss video generator a clearer image to work with. Avoid strong filters that change skin tone, blur facial edges, or create artificial contrast around the face. ## Choose Medium-Close Framing The two subjects should be close enough for the kiss effect to work naturally, but not so close that faces are cropped. A medium-close portrait often works better than a full-body image taken from far away. If the subjects are very far apart, the generated motion may look exaggerated or unnatural. ## Use a Sharp, High-Quality Image Low-resolution images can make the AI kiss video blurry. Use the clearest version of the image available, ideally without heavy compression or screenshot artifacts. If you only have a small image, try using a sharper original file before running the generation again. ## Why Kling AI Kiss May Not Work With Some Images Kling AI Kiss is different from a general text-to-video tool. It is designed around a preset kiss effect, so the workflow is simpler: upload an image, run the effect, and preview the generated video. Because the effect is preset, custom prompt changes are not the main fix when Kling AI Kiss is not working. The better fix is usually to improve the uploaded image. For example, if the uploaded photo has one clear face and one hidden face, the model may not understand the intended interaction. If the image has a crowd, the model may not know which two subjects should move. If the photo is dark or blurry, the generated kiss video may look distorted. Before retrying, ask one simple question: would a person looking at this image immediately know which two subjects should be animated? If the answer is no, the image probably needs to be cropped, brightened, or replaced. ## Bad Input Examples: What To Avoid The examples below show common input image patterns that can make an AI kiss video fail. These synthetic images are included to explain the problem clearly without using real people's photos. Blurry two-person image Problem: The image is too soft, compressed, or out of focus. Why it fails: When the face edges are unclear, the AI kiss video generator has less detail to preserve during motion. This can make the final AI kiss video blurry, unstable, or less realistic. Fix: Use the sharpest original image available. Avoid screenshots, heavily compressed images, and photos where the faces are already blurred before generation. One clear face, one hidden face Problem: One subject is visible, but the other subject's face is covered, cropped, turned away, or hidden in shadow. Why it fails: Kling AI Kiss needs two readable subjects. If one face is unclear, the model may distort the hidden face, animate only one subject, or produce a failed kiss motion. Fix: Choose a photo where both faces are visible enough to identify. Slight angles are fine, but avoid images where one person is mostly hidden. Faces too far apart Problem: The two subjects are far apart in the frame. Why it fails: If the subjects are too far apart, the model has to create a large, unnatural movement to connect them. The generated kiss motion may look stretched, awkward, or unrealistic. Fix: Use a medium-close image where both subjects are already near each other. Leave enough room around the faces, but avoid wide shots where the people are separated by a large gap. Too many people in the image Problem: The image includes three or more people, or the intended pair is not obvious. Why it fails: The AI may not know which two people should be animated. This can cause the wrong subjects to move or create confusing motion in the final video. Fix: Crop the image to the two intended subjects before using the AI kiss generator. A simple two-person image is much more reliable than a group photo. ## Good Input Example: What To Aim For A good input image for Kling AI Kiss should show two clear subjects, visible faces, balanced lighting, and a simple composition. The subjects should be close enough for the kiss motion to feel natural, but not so close that the faces are cropped. Use this as the target pattern before generating: - Two main subjects only - Both faces visible - Sharp image quality - Balanced lighting - Minimal face occlusion - Medium-close framing - Simple background For the first version, input-image examples alone are enough to teach users what to fix before trying again. If we later generate Kling AI Kiss output clips, they can be added after each input example as short before-and-after comparisons. If you want to compare successful outputs before changing your image, see these AI kissing video examples from different photo types . ## AI Kiss Generator Checklist Before You Generate Before using an AI kiss video generator, check the image against this list: - Does the image show exactly two main subjects? - Are both faces visible? - Are the eyes, mouth, and face shape unobstructed? - Is the image sharp enough? - Is the lighting balanced? - Are the subjects close enough for a kiss motion to look natural? - Is the background simple enough? - Have you avoided heavy filters, screenshots, and strong blur? - Do you have permission or rights to use the image? If the answer is no to several items, fix the image first. A better input image is usually more effective than generating the same weak image again. ## How to Fix AI Kiss Video Results Step by Step If your AI kiss video failed, use this retry process. - Identify the visible problem in the output. - Compare the output problem with the original input image. - Crop the image to the two intended subjects. - Replace blurry or low-resolution images with sharper originals. - Avoid covered faces, sunglasses, masks, and strong shadows. - Use a medium-close image where both people are clearly visible. - Generate again with the improved image. This workflow is useful whether you are testing a free kiss AI tool, a free AI kissing video generator, or the Kling AI Kiss generator on PiAPI. ## How To Try Again With Kling AI Kiss Once your input image is cleaner, try the workflow again on the Kling AI Kiss generator . If you need the full beginner workflow, read the AI kiss generator guide . It explains how the Kling kiss generator works, what the upload flow looks like, and how the generated video is created. For developers or product teams exploring broader automation, Kling AI Kiss is part of the wider Kling effects and video generation ecosystem. You can also review the Kling Effects API and Kling API pricing and features for related API workflows. ## Safety and Consent Reminder AI kiss videos can imply romantic or intimate actions that did not happen in real life. Only upload images you have the right to use, and avoid creating romantic videos of real people without consent. For public, commercial, or social media use, be especially careful with identity, privacy, and disclosure. A technically successful AI kiss video can still be inappropriate if the people in the image did not agree to that use. ## FAQ ## Why is my AI kiss video blurry? An AI kiss video is often blurry because the input image is low resolution, compressed, or out of focus. Use a sharper photo where both faces are clear before generating again. ## Why does my AI kiss video look weird or distorted? AI kiss videos can look distorted when faces are blocked, turned too far sideways, poorly lit, or too close to the edge of the image. Use a cleaner two-person image with visible facial features and balanced lighting. ## Why is my AI kiss generator not working? An AI kiss generator may not work if the uploaded image does not contain two clear subjects or if the file is too unclear for the effect. Try a JPEG, JPG, or PNG image with two visible faces and a simple composition. ## What is the best photo for an AI kiss generator? The best photo for an AI kiss generator shows two clear subjects, visible faces, natural lighting, medium-close framing, and minimal background clutter. Avoid group photos, dark scenes, heavy filters, and covered faces. ## Can I fix a failed Kling AI Kiss video? Yes. In most cases, the best fix is to improve the input image and generate again. Crop to the intended two subjects, use a sharper image, avoid occlusion, and make sure both faces are visible. ## Does Kling AI Kiss use custom prompts? Kling AI Kiss is an effect-based workflow, so users mainly control the uploaded image rather than writing custom prompts. For this reason, input image quality is the most important factor for better results. ## Conclusion When an AI kiss generator fails, the first thing to inspect is the image. Blurry faces, hidden features, crowded scenes, extreme face angles, and poor lighting are the most common reasons an AI kiss video looks wrong. Start testing Kling Kiss API and get your API access via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## AI Kiss Generator Guide: How to Create Kling AI Kiss Videos Learn what an AI kiss generator is, how Kling AI Kiss turns photos into short kissing videos, and how to try the Kling kiss generator on PiAPI. An AI kiss generator is an image-to-video tool that turns a still photo into a short kissing video. It uses AI motion generation to animate two visible subjects, create a lean-in or kiss effect, and produce a short clip without manual video editing. The Kling AI Kiss Generator on PiAPI gives creators and developers a simple way to test this workflow with Kling Effects. Upload a reference image with two subjects, generate a short kiss video, and review the result directly in the playground. In this guide, we'll look at what an AI kiss generator is, how the Kling kiss generator works, what makes a good input image, and when this type of AI kissing video generator is useful. ## Key Takeaways - An AI kiss generator creates a short kissing video from a still image. - Kling AI Kiss on PiAPI uses a clear two-subject image as the reference input. - Better source images usually have visible faces, balanced lighting, and simple backgrounds. - Real examples in this guide show both the input image and the generated Kling kiss video. - Consent matters because AI kiss videos can imply romantic or intimate actions that did not happen. ## What is an AI Kiss Generator An AI kiss generator is software that creates a short kiss animation from a still image. The tool analyzes the people or characters in the uploaded photo, applies a kiss motion effect, and generates a video clip that can be previewed or downloaded. Most AI kissing video generator tools are built for simple creative workflows. Users do not need video editing skills, motion design software, or prompt engineering experience. The core process is usually: - Upload a photo - Choose or trigger a kiss effect - Generate the video - Preview and download the result For searchers looking for AI kiss generator tools, the main goal is usually practical. They want to know whether the tool can create a realistic AI kiss video, how fast it works, and what kind of image produces the best result. ## Kling AI Kiss Generator Guide The Kling AI Kiss Generator on PiAPI is a browser-based playground for creating short Kling kiss videos from images. It uses a fixed kiss prompt and Kling 2.6 image-to-video generation , which makes the workflow easier to test than an open-ended text-to-video prompt. Quick Answer: To create a Kling kiss video, upload a clear image with two visible subjects to PiAPI's Kling AI Kiss Generator, submit the task, and preview the generated 5-second video. The output quality depends mainly on face visibility, subject placement, lighting, and scene simplicity. Using the Kling AI Kiss playground follows a simple flow: - Open the Kling AI Kiss Generator - Upload a reference image in JPEG, JPG, or PNG format - Make sure the image includes two visible subjects - Submit the task - Preview the generated Kling kiss video Because the workflow depends on the uploaded image, output quality is closely tied to the input. A clear image with visible faces, balanced lighting, and enough space around both subjects will usually produce a more stable result. ## Key Features of Kling AI Kiss Kling AI Kiss is built for short image-to-video generation using a focused kiss effect. The main value is that it gives users a direct way to test an AI kiss video generator without building the motion from scratch. ## Photo-to-Video Kiss Generation The playground turns a still image into a short AI kiss video. This makes it useful for testing romantic, playful, or character-based video effects from a single reference image. ## Fixed Kiss Effect Workflow The Kling kiss generator uses a fixed kiss prompt and short duration. This reduces prompt setup and makes the experience easier for users who want to test the effect quickly. ## Two-Subject Image Input The reference image should include two subjects. This helps the model understand who should appear in the generated kiss animation and keeps the workflow more predictable. ## Kling Effects Integration PiAPI connects the playground to Kling Effects , giving users a focused way to test Kling AI kiss video generation from the browser before exploring broader API workflows. ## Developer-Friendly Testing For developers and product teams, the page is useful as a reference workflow. It shows how an AI video effect can be packaged into a simple upload, submit, and preview experience. ## AI Kiss Generator vs AI Kiss Video Generator The phrases AI kiss generator, AI kissing video generator, and AI kiss video generator are often used to describe the same type of tool. The difference is mostly in what the user expects to create. Phrase What it usually means AI kiss generator A broad tool for creating AI kiss content from an image AI kissing video generator A tool focused specifically on generating kissing videos AI kiss video generator A shorter way to describe the same image-to-video workflow Kiss video generator A broader phrase that may include AI and non-AI tools Kling kiss generator A Kling-based workflow for creating AI kiss videos For this guide, the important takeaway is simple: if you want a short kissing video from a photo, use an AI kiss video generator with a clear two-subject image. PiAPI's Kling AI Kiss playground is built around that exact workflow. ## Best Input Tips for AI Kiss Videos Input quality has a major impact on AI video output. Before using a kiss video generator, prepare the source image carefully. ## Use a Clear Two-Person Image The PiAPI Kling AI Kiss playground asks for a reference image with two subjects. Both subjects should be visible and easy to identify. ## Keep Faces Visible Avoid images where faces are heavily cropped, covered, blurred, or turned too far away from the camera. Clear faces help the model preserve identity and generate more natural motion. ## Choose Balanced Lighting Strong shadows, overexposure, and heavy filters can make the generated video less stable. Natural lighting usually works better. ## Avoid Cluttered Scenes Busy backgrounds, overlapping objects, and complex poses can introduce artifacts. A cleaner image gives the model a simpler scene to animate. ## Expect Short-Form Output AI kiss generators work best for short clips. The goal is a believable moment, not a long cinematic sequence. ## Can You Use an AI Kissing Generator Free? Some users search for AI kissing generator free or AI kissing video generator free because they want to test the effect before committing to a paid workflow. The safest way to evaluate these tools is to look for a demo-style playground, check whether credits or login are required, and review the current pricing before repeated generation. PiAPI's Kling AI Kiss page is designed as a try-first workflow. You can test the AI kiss effect in the playground, review the generated video quality, and then decide whether it fits your creative project or API integration needs. Free AI kiss tools can be useful for quick tests, but production use usually depends on output quality, generation limits, privacy terms, and whether the tool supports the model or API access you need. ## Example 1: Couple Photo For a simple couple-photo use case, upload a clear image with both people facing the camera or angled naturally toward each other. Example 1 input image for Kling AI Kiss. This generated input image is designed for a Kling kiss video test: two adults, clear faces, balanced lighting, simple background, and close portrait framing. ## Evaluation: Kling AI Kiss handles this example well. The subjects lean toward each other smoothly, the kiss moment is clear, and both faces remain recognizable throughout the 5-second video. Minor artifacts appear around the lips and facial edges, but the clip is usable for a short demo or social preview. ## Example 2: Character or Fan Edit For creative edits, users may upload artwork, fictional characters, or stylized images with two subjects. Example 2 input image for a stylized Kling kiss video. This generated input image uses two original fantasy-style characters with clear faces, close framing, and detailed costumes. ## Evaluation: Kling AI Kiss produces a clear, cinematic kiss motion from this stylized input. The faces stay recognizable, the costume details remain mostly stable, and the romantic framing fits the original image well. The main limitation is that fine details around hair, lips, and accessories can shift slightly during the 5-second motion, so character-style inputs may need a few retries for production use. Want to compare more photo styles before uploading your own image? See our AI kiss video examples from different photos . ## Safety and Consent AI kiss generators should be used carefully because they can create romantic or intimate scenes that did not happen in real life. Users should only upload images they have the right to use and should avoid creating kiss videos of real people without consent. This matters for privacy, trust, and responsible AI use. A kissing video can imply intimacy, so it should not be generated in a way that misrepresents someone or violates their boundaries. Before using any AI kiss video generator, review the platform's privacy terms, content policies, and refund rules. PiAPI links to its Privacy Policy , Terms and Conditions , Refund Policy , and NSFW Policy from the Kling AI Kiss page. ## When to Use PiAPI for Kling Kiss Videos PiAPI is useful when you want to test Kling kiss video generation in a developer-friendly environment. The playground helps users understand the effect visually, while the broader PiAPI platform supports teams exploring AI generation APIs . - a focused Kling kiss generator playground - a way to test image-to-video effects - a simple upload and preview workflow - access to broader AI API documentation - a developer-first gateway for image, video, audio, and other generation models For casual creators, the playground is a quick way to try the effect. For developers, it can also act as a starting point for thinking about how AI video generation might fit into a product workflow. ## FAQ ## What is an AI kiss generator? An AI kiss generator is a tool that turns a still image into a short kissing video. It uses image-to-video AI to animate subjects in the photo, create a lean-in or kiss motion, and produce a romantic or playful video clip. ## How does the Kling kiss generator work? The Kling kiss generator uses a reference image, a fixed kiss prompt, and Kling 2.6 image-to-video generation to create a short AI kiss video. On PiAPI, users upload an image with two subjects and generate the result through the Kling AI Kiss playground. ## Can I create an AI kiss video from one photo? Yes. Some AI kiss video generator workflows use one photo if that image includes two visible subjects. The PiAPI Kling AI Kiss playground asks for a reference image that includes two subjects. ## What is the best image for an AI kiss video? A clear image with two visible faces, balanced lighting, and minimal clutter usually works best. Avoid blurry photos, extreme angles, and heavily cropped subjects. ## Is the Kling AI Kiss Generator free to try? PiAPI presents the Kling AI Kiss page as a demo-style playground. Check the current page, pricing, and account requirements before using it for production or repeated generation. ## Is there a free AI kissing video generator? Some AI kissing video generator tools offer free demos, free trials, or limited credits. Always check whether the tool requires login, credits, or a paid plan before repeated use. PiAPI's Kling AI Kiss page is positioned as a demo-style playground for testing the workflow. ## What does AI kiss mean? AI kiss usually refers to an AI-generated kissing image or video. In this guide, it means a short image-to-video clip where an AI model animates two visible subjects into a kiss scene. ## Is it okay to make AI kiss videos of other people? Only use images you have permission to use. Avoid creating romantic or intimate AI videos of real people without consent, especially if the output could misrepresent them. ## Conclusion AI kiss generators make it easy to turn still photos into short romantic videos, and Kling Effects gives this workflow a focused image-to-video format. For the best results, start with a clear two-subject image, keep the scene simple, and review the output for face consistency, motion quality, and artifacts. The PiAPI Kling AI Kiss Generator is a useful way to test this effect directly in the browser. It works well as both a creative playground and a developer-friendly reference for AI video generation workflows. Start testing the Kling AI Kiss Generator on PiAPI today. Unlock the power of AI video generation with PiAPI - image, video, audio, and more. Sign up today and start building faster. ## GPT Image 2 vs Nano Banana 2.0: The Ultimate AI Image Generation Showdown Compare Nano Banana 2.0 vs GPT Image 2 in image quality, prompt accuracy, realism, pricing, and API usage. See real examples, prompt tests, and performance comparisons. AI image generation is no longer just about creating "good-looking" visuals. The real competition now comes down to which model can actually understand prompts better, generate more usable outputs, and produce images that require less manual fixing afterwards. GPT Image 2 Nano Banana 2.0 With growing interest around the GPT Image 2 API and Nano Banana 2 API, many developers, creators, and marketing teams are now trying to determine which model delivers better results for actual production workflows instead of just showcase images. In this comparison, we tested GPT Image 2 and Nano Banana 2.0 across multiple prompt scenarios including lifestyle photography, commercial advertising visuals, and text-heavy poster generation. We will compare image quality, prompt accuracy, realism, generation consistency, pricing, and API accessibility to see which model performs better in real-world usage. ## What is GPT Image 2? GPT Image 2 is OpenAI's latest AI image generation model, officially released on April 21, 2026. The model is built to generate images directly from natural language prompts and is part of OpenAI's expanding multimodal AI ecosystem. Since launch, GPT Image 2 has become one of the most talked-about image generation models among creators, developers, and marketing teams exploring scalable visual content generation. ## What is Nano Banana 2.0? Following its release, Nano Banana 2.0 has increasingly been compared alongside newer image generation models such as GPT Image 2, especially among users exploring alternative AI image generation APIs and creative workflows. ## Similarities Between GPT Image 2 and Nano Banana 2.0 Despite their different architectures, both GPT Image 2 and Nano Banana 2.0 support modern AI image generation workflows. ## Natural Language Prompting Both models can understand conversational prompts instead of relying only on short keyword-based instructions. ## Text Rendering Support Both GPT Image 2 and Nano Banana 2.0 are capable of generating readable text within posters, advertisements, menus, and branding materials. ## Multiple Aspect Ratios Both models support image formats such as 1:1, 9:16, and 16:9 for social media, banners, and cinematic content generation. ## API Accessibility The GPT Image 2 API and Nano Banana 2 API both allow developers and businesses to integrate AI image generation into creative workflows and applications. ## Key Differences Between GPT Image 2 and Nano Banana 2.0 The biggest difference between GPT Image 2 and Nano Banana 2.0 is how both models approach image generation internally. ## GPT Image 2: The "Thinking" Image Model GPT Image 2 uses a reasoning-based generation approach where the model plans scene structure, lighting, spatial relationships, and composition before generating the final image. This allows GPT Image 2 to perform particularly well in prompts involving: - Complex layouts - Object positioning - Reflections and perspective - UI mockups - Text-heavy posters - Structured compositions ## Nano Banana 2.0: The Cinematic Generation Engine Nano Banana 2.0 focuses more heavily on high-speed image generation and cinematic visual output rather than multi-step reasoning. The model prioritizes: - Cinematic lighting - Atmospheric depth - Photorealistic textures - Vibrant color grading - Visually expressive compositions ## Precision vs. Visual Atmosphere GPT Image 2 is more optimized for prompt precision and layout accuracy, while Nano Banana 2.0 focuses more heavily on visual mood, cinematic presentation, and artistic atmosphere. ## Intent Fidelity vs. Creative Interpretation GPT Image 2 generally follows prompts more strictly and accurately, especially for highly detailed instructions. Nano Banana 2.0 tends to add more artistic interpretation and cinematic flair during generation, even when those details are not explicitly mentioned in the prompt. ## Prompt Practices for GPT Image 2 and Nano Banana 2.0 Both models support natural language prompting, but they tend to respond differently depending on prompt structure and writing style. GPT Image 2 generally performs better with highly structured prompts containing detailed layout instructions, object positioning, and typography guidance, while Nano Banana 2.0 responds more naturally to prompts focused on mood, lighting, atmosphere, and cinematic styling. GPT Image Prompt Guide Nano Banana 2 Prompt Guide ## Example 1: Travel and Lifestyle Photography Prompt: A cinematic travel photograph of two friends walking through a mountain valley during sunrise, soft golden sunlight illuminating the landscape, natural candid poses, realistic skin texture, wind flowing through clothing and hair, lush greenery, atmospheric depth, ultra realistic photography, shallow depth of field, detailed environment, shot on 85mm lens, cinematic color grading, photorealistic ## Comparison Analysis For this travel photography prompt, the difference between GPT Image 2 and Nano Banana 2.0 becomes immediately noticeable once you examine how both models interpret realism and cinematic composition. GPT Image 2 approaches the scene more logically, carefully structuring spatial depth, lens compression, lighting direction, and environmental consistency before generating the final image. The result feels technically precise, especially in how the mountains, sunlight direction, and subject placement interact naturally within the scene. Nano Banana 2.0, on the other hand, prioritizes atmosphere and emotional presentation over strict scene logic. The image delivers stronger cinematic mood straight out of generation, with richer environmental textures, more dramatic color grading, and a more organic-looking landscape overall. However, once inspected closely, smaller spatial inconsistencies within the terrain and background structure become more noticeable compared to GPT Image 2's more reasoned composition. Overall, GPT Image 2 performs better in scene structure, lighting logic, and prompt fidelity, while Nano Banana 2.0 stands out more for cinematic atmosphere, environmental texture detail, and overall visual emotion. ## Example 2: Commercial Product Advertisement Prompt: A premium matcha latte in a transparent glass cup placed on a minimalist concrete table, soft morning sunlight entering from the side, visible condensation on the glass, realistic foam texture, scattered matcha powder and bamboo whisk nearby, clean Japanese cafe aesthetic, shallow depth of field, cinematic food photography, ultra realistic, shot on 50mm lens, photorealistic ## Comparison Analysis The benchmark tests highlighted a very clear difference in how both models approach image generation. GPT Image 2 behaves more like a reasoning-based system, prioritizing spatial logic, prompt accuracy, typography placement, and structured composition before generating the final image. This was especially noticeable in the Mountain Valley and Tech Poster tests, where the model handled lens compression, lighting direction, layout hierarchy, and text rendering with much higher consistency. Nano Banana 2.0, on the other hand, focuses more heavily on cinematic atmosphere and visual impact. The model produced stronger color grading, richer environmental textures, and more dramatic lighting straight out of generation, giving images a more organic and visually expressive feel. However, this came with weaker typography accuracy and less consistent spatial logic in more technically demanding prompts. Overall, GPT Image 2 performed better in structured commercial design workflows and prompt fidelity, while Nano Banana 2.0 stood out more for cinematic presentation, speed, and aesthetic-driven image generation. ## Example 3: Complex Spatial Reasoning and Reflection Scene Prompt: A modern dining table scene with exactly 7 objects arranged in specific positions: a red apple in the center, a glass cup to the left of the apple, a silver spoon placed diagonally above the cup, a folded newspaper on the right side of the apple, a black smartphone partially covering the newspaper, a lit candle behind the apple casting warm shadows forward, and a small mirror reflecting only the candle flame but not the apple. Cinematic indoor lighting, realistic reflections, accurate object spacing, realistic shadow direction, ultra realistic photography, shot on 50mm lens, photorealistic ## Comparison Analysis This final test exposed the clearest difference between GPT Image 2 and Nano Banana 2.0. GPT Image 2 handled the prompt with much stronger logical consistency, correctly following the exact object count, spatial positioning, reflection constraint, and shadow direction. The mirror reflection and lighting behavior especially demonstrated the advantage of its reasoning-based generation architecture. Nano Banana 2.0 generated a more atmospheric and visually organic scene with stronger cinematic mood and environmental texture detail. However, the model struggled more with logical precision, introducing additional objects, less accurate reflection angles, and softer lighting behavior that prioritized aesthetics over physical consistency. Overall, GPT Image 2 performed significantly better in complex reasoning and structured scene generation, while Nano Banana 2.0 remained stronger in cinematic atmosphere and visual storytelling. ## Pricing and API Differences At the time of writing, GPT Image 2 API pricing on PiAPI starts at $0.10/img per generation through the gpt-image-2-preview model. The model is currently positioned as a premium reasoning-focused image generation system, particularly for workflows involving typography, structured layouts, and complex prompt accuracy. Nano Banana 2 API pricing is currently more resolution-based. Pricing starts at $0.06 per image for 1K generation, $0.08 for 2K, and $0.12 for 4K outputs. This makes Nano Banana 2.0 a more flexible option for users prioritizing high-volume generation, faster iteration, and scalable cinematic image workflows. Both GPT Image 2 API and Nano Banana 2 API are available through PiAPI, allowing developers and businesses to integrate AI image generation directly into creative pipelines, applications, and automated production workflows. ## Final Verdict GPT Image 2 and Nano Banana 2.0 ultimately represent two very different approaches to AI image generation. GPT Image 2 behaves more like a reasoning-driven image model. Its Chain-of-Thought architecture allows it to handle complex layouts, typography, reflections, object positioning, and structured prompts with significantly stronger logical consistency. For commercial workflows involving posters, UI mockups, advertisements, and technically demanding compositions, GPT Image 2 currently feels more reliable and precise. Nano Banana 2.0, on the other hand, focuses more heavily on speed, cinematic atmosphere, and visual emotion. The model consistently produces striking color grading, organic lighting, and aesthetically rich imagery with minimal prompting, making it especially appealing for cinematic artwork, social media visuals, and creative ideation workflows. Ultimately, choosing between GPT Image 2 and Nano Banana 2.0 depends on whether you prioritize logical precision or cinematic visual storytelling. GPT Image 2 Nano Banana 2.0 PiAPI Sign up ## GPT Image 2 vs GPT Image 1.5 API: What's New in OpenAI Image Generation? Compare GPT Image 2 vs GPT Image 1.5 API. Discover new features, image quality upgrades, pricing differences, prompt tips, and why GPT Image 2 is the latest choice for AI image generation. GPT Image 2 OpenAI For creators, marketers, developers, and teams using a GPT image API, the difference goes beyond visuals alone. Better text rendering, improved consistency, stronger style control, and more dependable results can save time and improve workflow efficiency. In this guide, we compare GPT Image 2 vs GPT Image 1.5 across image quality, prompt performance, pricing, and real-world use cases. We also break down what changed, share prompt tips, and review side-by-side examples to help you decide which GPT image generator is the better choice today. ## What's New in GPT Image 2 Compared to GPT Image 1.5 Instead of simply generating better-looking images, GPT Image 2 focuses on producing results that are more usable, controllable, and reliable from the start. ## Smarter Prompt Understanding with Reasoning-Based Generation One of the biggest shifts in GPT Image 2 is the introduction of a reasoning-driven generation process. Before rendering the final image, the model can better interpret instructions, understand scene relationships, and plan visual elements more accurately. This helps when prompts include multiple constraints such as object counts, camera angles, layout requests, or detailed scene compositions. Compared with GPT Image 1.5, GPT Image 2 is better equipped to follow complex prompts with fewer mistakes. ## Sharper Image Quality with Fewer AI Artifacts GPT Image 2 delivers cleaner visuals with stronger textures, lighting balance, and more natural details. Hands, faces, shadows, and fine elements appear more refined, reducing the visual inconsistencies that often signal AI-generated content. For users creating product ads, lifestyle campaigns, or editorial visuals, this can lead to outputs that feel more polished and ready to publish. ## Major Improvements in Text Rendering Readable text inside AI-generated images has historically been a weak point across many models. GPT Image 2 makes a significant leap forward by generating clearer typography, better spacing, and more accurate lettering. This makes it much more practical for menus, posters, packaging concepts, banners, social media ads, and branded marketing assets where text quality matters. ## Better for UI and Interface Concepts GPT Image 2 is also more capable when generating app screens, landing page mockups, dashboards, and interface concepts. Cleaner alignment, sharper icons, and more structured layouts make it more useful for UI or UX ideation. For teams prototyping quickly, this can reduce the gap between concept generation and design execution. ## More Advanced Image Editing and In-Painting Compared with GPT Image 1.5, GPT Image 2 offers stronger image editing consistency. Users can modify specific parts of an image, such as changing clothing, facial expressions, objects, or backgrounds, while preserving the rest of the composition more accurately. This is valuable for iterative creative workflows where only one element needs adjustment. ## Stronger Consistency Across Multiple Images Maintaining the same character or subject across several generations can be difficult for AI image models. GPT Image 2 improves visual consistency, making it easier to create image sequences with recurring characters, similar styling, or campaign-ready series assets. This is especially useful for storytelling, product catalogs, or brand content sets. ## Better Value for Commercial Workflows While GPT Image 2 may cost more than GPT Image 1.5 depending on usage mode, the improved prompt accuracy, cleaner text rendering, and reduced need for manual editing can offer stronger overall value. For marketers, developers, and businesses using a GPT image API, fewer retries and higher-quality outputs can translate into better efficiency and stronger campaign performance. ## Best ChatGPT Image Prompts and Prompt Tips for GPT Image 2 Getting strong results from GPT Image 2 is not only about the model itself. Prompt quality still plays a major role in output accuracy, style, and consistency. A well-structured prompt can help the model better understand your intent and produce images that require fewer revisions. Whether you are using GPT Image 2 for content creation, design concepts, or marketing assets, these prompt tips can help improve results. ## Be Specific With the Subject and Scene Clear prompts usually perform better than vague ones. Instead of asking for a woman in a cafe, describe the environment, outfit, expression, and setting. A stylish woman sitting in a modern Paris cafe, morning sunlight through the window, warm tones, candid lifestyle photography. The added detail gives the GPT image generator more direction to work with. ## Include Style References If you already know the look you want, mention it directly. GPT Image 2 responds well to visual styles such as cinematic, product photography, editorial, anime, minimalist, retro, or luxury branding. Luxury skincare product on marble table, premium commercial photography style, soft shadows, elegant composition. ## Define Camera Angle and Composition Specifying perspective can improve image framing. Terms such as close-up, overhead shot, wide-angle, centered composition, portrait orientation, or macro shot help guide layout. This is especially useful when generating ads, product images, or social media visuals. ## Mention Lighting and Mood Lighting strongly affects image quality and emotion. Adding lighting instructions can make results feel more polished. - Soft natural daylight - Neon cyberpunk glow - Golden hour sunlight - Studio lighting - Moody cinematic shadows ## Use Text Prompts Carefully GPT Image 2 is stronger at text rendering than earlier models, making it useful for posters, menus, banners, and thumbnails. If text is required, keep wording clear and concise. Modern coffee poster with headline "Fresh Brew Daily", clean sans-serif typography. ## Iterate and Refine Many users get the best results through multiple rounds. Start with a base concept, then improve it by adjusting style, colors, composition, or subject details. This workflow often produces stronger results than trying to fit every detail into one long prompt. ## Use GPT Image 2 for Real-World Creative Tasks The model performs especially well for: - Product marketing creatives - Blog hero images - Social media campaigns - UI concept mockups - Character art and storytelling visuals ## Example 1: Luxury Watch Advertisement Prompt: A premium stainless steel wristwatch standing upright on a black reflective surface, water droplets around the base, dramatic side lighting, visible engraved dial details, realistic glass reflections, cinematic luxury campaign style, dark gradient background, headline text "Precision Redefined" at the top. ## Comparison Analysis For a premium advertising prompt like this, GPT Image 2 produces a noticeably more polished result than GPT Image 1.5. The headline text appears cleaner and more professionally aligned, while GPT Image 1.5 may introduce extra text or less refined typography. Material realism is also stronger in GPT Image 2. Water droplets show believable surface tension and reflections align naturally with the black glossy base, whereas GPT Image 1.5 can render these details less accurately. The watch itself benefits from sharper macro detail in GPT Image 2, with clearer dial markings, stronger metallic texture contrast, and a more premium finish. Lighting is another key difference, as GPT Image 2 handles dramatic side-lighting with better depth and rim highlights, while GPT Image 1.5 may appear flatter. Overall, GPT Image 1.5 can still generate a strong concept image, but GPT Image 2 delivers a result that feels much closer to a real luxury campaign photograph. ## Example 2: High-End Fashion Campaign Poster Prompt: A luxury fashion campaign poster for a modern streetwear brand. Confident model standing in a futuristic urban alley at night, neon reflections on wet pavement, cinematic blue and silver lighting, bold magazine-style composition, premium editorial photography, clean headline text "OWN THE NIGHT", smaller subtext "Fall Collection 2026", stylish modern typography, high-end billboard advertisement layout. ## Comparison Analysis For a design-heavy prompt like this, GPT Image 2 delivers a much stronger result than GPT Image 1.5 by combining image generation with professional layout quality. Typography appears cleaner, sharper, and more intentional, with readable fine print, stronger headline styling, and branding elements that feel like a real campaign rather than text simply placed onto an image. Composition is another major difference. GPT Image 2 creates a more immersive scene with believable depth, better perspective, and environmental elements that feel physically connected. The model appears naturally placed within the alley, while background signage, lighting, and surrounding objects work together more cohesively. GPT Image 1.5 can still generate a strong concept, but elements may feel flatter or less integrated. Material realism is also noticeably stronger in GPT Image 2. Clothing textures show natural folds, weight, and finish, while wet pavement reflections capture surrounding neon light more accurately. GPT Image 1.5 may produce simpler textures or less convincing reflections. Human realism sees clear gains as well. GPT Image 2 renders more natural facial detail, skin texture, and body posture, while hands and clothing interaction appear more anatomically correct. GPT Image 1.5 may still show the smoother AI-generated look in faces or softer hand details. Overall, GPT Image 1.5 works well for early-stage concepts, but GPT Image 2 produces a far more polished final asset that feels ready for real campaign use. ## Example 3: Realistic Coffee Shop Scene with Readable POS Interface Prompt: A realistic, eye-level candid photograph taken with a Fujifilm camera. A weary barista is handing a latte to a customer across a bustling independent coffee shop counter at 8:00 AM on a rainy Tuesday. On the counter is a functional, authentic POS screen (tablet) showing a visible, readable "Order Summary" with three line items such as "1. Oat Latte $6.50", "2. Croissant $4.00", and "3. Americano $5.00". Natural soft lighting from the window, visible condensation on the shop glass, high-end documentary photography style, shallow depth of field focusing on the exchange and the screen, authentic atmosphere. ## Comparison Analysis This example showcases GPT Image 2's stronger real-world logic and scene understanding. Instead of only generating a coffee shop image, it creates a more believable environment where details feel functional and accurate. The POS screen is a key difference. GPT Image 2 is more likely to produce readable menu items, realistic pricing, and totals that make sense, while GPT Image 1.5 may generate more generic or less consistent interface details. Human interaction is also improved. GPT Image 2 handles hand placement, object contact, and the coffee handoff more naturally, while GPT Image 1.5 may still show awkward anatomy or fused hand artifacts. Prompt adherence is stronger as well. The rainy Tuesday mood, softer grey lighting, and authentic cafe atmosphere feel more convincing in GPT Image 2, whereas GPT Image 1.5 can appear more staged. Overall, GPT Image 1.5 creates a solid concept image, but GPT Image 2 delivers a more realistic and commercially usable final result. ## GPT Image 2 vs GPT Image 1.5 Pricing Difference Pricing is one of the biggest considerations when choosing an AI image generation model, especially for teams producing content at scale. While GPT Image 1.5 offers a lower entry cost, GPT Image 2 focuses on higher output quality, stronger text rendering, better prompt accuracy, and more production-ready results. For many users, that can mean fewer retries, less manual editing, and stronger overall ROI despite the higher per-image cost. If your priority is affordable high-volume generation, GPT Image 1.5 remains a strong option. If your priority is premium outputs for ads, branding, ecommerce, or polished visual content, GPT Image 2 may offer better value per successful generation. ## Final Verdict: Is GPT Image 2 Worth It? GPT Image 1.5 remains a strong option for users who prioritize affordability and high-volume image generation. It is still capable of producing quality visuals for concept work, experimentation, and everyday creative tasks. However, GPT Image 2 is a clear upgrade in overall capability. Across prompt accuracy, text rendering, realism, scene understanding, and commercial readiness, the newer model consistently delivers more polished outputs with fewer compromises. For marketers, creators, developers, and businesses using a GPT image API, GPT Image 2 is the stronger long-term choice when image quality and reliability matter. While the cost is higher, the reduced need for retries and stronger final assets can justify the premium. If your focus is budget-friendly generation at scale, GPT Image 1.5 remains a practical choice. If you want the most advanced GPT image generator currently available, GPT Image 2 is the model worth watching. GPT Image 2 GPT Image 1.5 PiAPI Sign up ## 시댄스 2.0 vs 베오 3.1 비교: 어떤 영상 생성 AI가 더 좋을까? 시댄스 2.0과 베오 3.1을 비교하여 영상 품질, 가격, API 사용성을 한눈에 확인해보세요. 어떤 AI 영상 생성 모델이 나에게 적합한지 알아보세요. 같은 프롬프트를 입력했는데 어떤 모델은 광고처럼 세련된 영상을 만들고, 어떤 모델은 영화 같은 장면을 만들어낸다면 어떤 선택이 더 좋을까요? 시댄스 2.0과 베오 3.1은 각각 다른 강점을 가진 대표적인 AI 영상 생성 모델로, 사용자 목적에 따라 만족도가 크게 달라질 수 있습니다. 빠른 콘텐츠 제작이 중요한 경우도 있고, 높은 완성도의 시네마틱 영상이 필요한 경우도 있습니다. 이번 글에서는 시댄스 2.0과 베오 3.1을 비교하여 영상 품질, 가격, API 활용성 측면에서 어떤 모델이 더 적합한지 살펴보겠습니다. 시댄스 도입 흐름이 필요하다면 시댄스 2.0 사용법 도 함께 확인해보세요. ## 시댄스 2.0 이란? 시댄스 2.0은 바이트댄스가 개발한 AI 영상 생성 모델로, 텍스트 프롬프트를 입력하면 짧은 영상 콘텐츠를 자동으로 제작할 수 있는 도구입니다. 빠른 생성 속도와 안정적인 프롬프트 반영 능력이 강점으로 알려져 있으며, 광고 영상, SNS 숏폼 콘텐츠, 브랜드 마케팅 영상 등 실무형 콘텐츠 제작에 자주 활용됩니다. 실제 연동은 시댄스 API 에서 바로 확인할 수 있습니다. 또한 다양한 스타일 표현과 일관된 장면 연출이 가능해 효율적인 영상 제작을 원하는 사용자들에게 주목받고 있습니다. ## 베오 3.1 이란? 베오 3.1은 구글이 공개한 AI 영상 생성 모델로, 텍스트 프롬프트를 기반으로 높은 완성도의 영상 콘텐츠를 제작할 수 있도록 설계되었습니다. 특히 사실적인 움직임 표현, 자연스러운 카메라 연출, 세밀한 조명과 배경 묘사에서 강점을 보이며 시네마틱 스타일의 결과물로 주목받고 있습니다. 브랜드 광고, 스토리텔링 영상, 프리미엄 콘텐츠 제작 등 높은 영상 품질이 중요한 작업에 적합한 선택지로 평가받고 있습니다. ## 모델 공통점과 차이점 ## 영상 품질 시댄스 2.0과 베오 3.1은 모두 고품질 결과물을 제공하는 최신 영상 생성 AI 모델입니다. 다만 결과물의 방향성에는 차이가 있습니다. 시댄스 2.0은 기본 2K 해상도를 지원하며, 인물 일관성과 동작 자연스러움에 강점을 둔 모델입니다. 반면 베오 3.1은 4K 업스케일링을 지원하며, 조명, 그림자, 배경 디테일 등 영화 같은 시네마틱 표현력에서 높은 평가를 받고 있습니다. 높은 현실감과 프리미엄 영상 품질이 중요하다면 베오 3.1이 유리할 수 있습니다. ## 제어 기능 및 제작 워크플로우 두 모델 모두 프롬프트 기반으로 영상을 제작할 수 있지만, 제어 방식은 다르게 설계되었습니다. 시댄스 2.0은 이미지, 영상, 오디오 등 최대 12개의 참고 파일을 활용할 수 있어 복합적인 연출 작업에 강점을 보입니다. 특히 특정 동작이나 카메라 움직임을 참고 영상으로 복제하는 기능은 실무 제작 환경에서 유용하게 활용될 수 있습니다. 반면 베오 3.1은 스타일과 분위기 일관성을 유지하는 방식에 강점을 두며, 보다 자연스럽고 완성도 높은 장면 연출에 초점이 맞춰져 있습니다. ## 오디오 통합 기능 오디오 활용 방식에서도 차이가 있습니다. 시댄스 2.0은 립싱크 정확도와 비트 싱크 기능이 강점으로, 음악 영상이나 퍼포먼스 콘텐츠 제작에 적합합니다. 업로드한 음악의 리듬에 맞춰 화면 전개를 조정할 수 있어 뮤직비디오, 숏폼 콘텐츠, 광고 영상 제작에 유리합니다. 반면 베오 3.1은 발소리, 바람 소리, 주변 환경음 등 사실적인 공간 오디오 표현에 강점을 보여 몰입감 있는 영상 제작에 적합합니다. ## 영상 길이 및 활용 목적 시댄스 2.0은 최대 15초 길이의 영상 생성이 가능해 TikTok, Reels, 광고 숏폼 콘텐츠처럼 짧고 강한 메시지가 필요한 작업에 적합합니다. 베오 3.1은 4초, 6초, 8초 단위 생성 후 확장이 가능한 구조로, 고품질 장면 단위 제작이나 브랜드 캠페인 영상, 영화 스타일 프로젝트에 더 잘 어울립니다. ## 어떤 사용자가 선택하면 좋을까? 빠른 제작 속도, 강력한 제어 기능, 음악 중심 콘텐츠가 필요하다면 시댄스 2.0이 좋은 선택이 될 수 있습니다. 반대로 사실적인 영상 품질, 시네마틱 연출, 고급 상업 영상 제작이 중요하다면 베오 3.1이 더 적합한 영상 AI 모델로 평가됩니다. 다른 고성능 영상 모델 후보까지 함께 보고 싶다면 클링 3.0 vs 시댄스 비교 도 참고할 만합니다. ## 예시 1: 광고 영상 생성 프롬프트 예시: 비가 내린 미래형 서울 도심 거리에서 프리미엄 전기차가 물웅덩이를 가르며 고속 주행한다. 네온사인이 젖은 도로 위에 반사되고, 드론 카메라가 차량 측면에서 후면으로 자연스럽게 이동한다. 차량이 빌딩 앞에 정차하자 내레이션이 들린다. “새로운 기준은 이미 시작됐다.” 화면에는 한국어 문구 “당신의 다음 드라이브”가 등장하는 시네마틱 자동차 광고 영상. ## Veo 3.1 Output ## 베오 3.1 평가 베오 3.1은 이번 테스트에서 프리미엄 광고 영상 제작에 강한 성능을 보여주었습니다. 젖은 도로 위 네온사인 반사, 차량 주행 시 튀는 물보라, 빗속 조명 표현 등 환경 디테일이 매우 사실적으로 구현되어 높은 시네마틱 완성도를 보여주었습니다. 또한 화면에 등장하는 한국어 문구와 내레이션 요소도 비교적 안정적으로 표현되어 국내 시장을 겨냥한 광고 콘텐츠 제작에도 활용 가능성이 높아 보입니다. 카메라 움직임 역시 드론 촬영처럼 자연스럽게 이어졌으며, 추적 장면에서도 왜곡 없이 안정적인 공간감을 유지했습니다. 종합적으로 보면, 베오 3.1은 조명, 분위기, 현실감이 중요한 프리미엄 자동차 광고와 같은 프로젝트에서 매우 경쟁력 있는 영상 생성 AI 모델로 평가됩니다. ## Seedance 2.0 Output ## 시댄스 2.0 평가 시댄스 2.0은 동일한 미래형 서울 자동차 광고 프롬프트 테스트에서 완성된 편집본에 가까운 결과물을 보여주었습니다. 특히 멀티샷 구성 능력이 인상적이었으며, 하나의 영상 안에서 와이드 샷, 미디엄 샷, 클로즈업 장면이 자연스럽게 이어지며 스토리텔링 흐름을 만들어냈습니다. 카메라 움직임 역시 더욱 역동적인 인상을 주었습니다. 고속 추적 장면과 자연스러운 흔들림 연출이 더해져 강한 속도감과 에너지를 전달했으며, 자동차 광고나 SNS 숏폼 콘텐츠에 어울리는 몰입감 있는 결과물을 완성했습니다. 또한 장면 전환과 오디오 타이밍의 연결감도 우수했습니다. 컷 전환이 음악이나 사운드 흐름과 자연스럽게 맞물리며 리듬감 있는 영상 구성을 보여주어 뮤직비디오, 숏폼 광고, 퍼포먼스 중심 콘텐츠 제작에 강점을 확인할 수 있었습니다. 종합적으로 보면, 베오 3.1이 최고 수준의 시각적 현실감과 프리미엄 화질에 강점이 있다면, 시댄스 2.0은 창의적인 워크플로우, 편집 완성도, 스토리텔링 제어 측면에서 매우 경쟁력 있는 AI 영상 생성 모델로 평가됩니다. ## 예시 2: 스니커즈 숏폼 광고 영상 생성 프롬프트 예시: 한정판 스니커즈가 서울 홍대 거리의 네온사인 배경 위에 등장한다. 카메라는 신발 밑창, 측면 로고, 끈 디테일을 빠르게 클로즈업한다. 모델이 계단을 뛰어오르며 착용 장면이 전환되고, 점프 후 착지 순간 슬로우모션으로 먼지가 튄다. 비트가 강해지며 화면에는 한국어 문구 “지금 드롭된다”가 등장하는 트렌디한 SNS 광고 영상. ## Veo 3.1 Output ## 베오 3.1 평가 베오 3.1은 이번 스니커즈 광고 테스트에서 프리미엄 브랜드 캠페인에 어울리는 높은 완성도를 보여주었습니다. 특히 반사광이 도는 스니커즈 소재와 네온 조명의 상호작용이 매우 자연스럽게 표현되었으며, 금속성 질감과 컬러 변화도 사실적으로 구현되었습니다. 또한 화면에 등장한 한국어 문구 “지금 드롭된다” 역시 안정적인 타이포그래피로 표현되어 별도의 후반 작업 없이도 활용 가능한 수준을 보여주었습니다. 발걸음 소리와 물 튀는 효과음 등 공간감 있는 오디오 표현도 더해져 몰입감 있는 광고 영상 연출이 가능했습니다. ## Seedance 2.0 Output ## 시댄스 2.0 평가 시댄스 2.0은 실전 마케팅 콘텐츠 제작에 강점을 보여주었습니다. 다양한 카메라 앵글 전환 속에서도 신발 디자인과 모델 착용 장면의 일관성이 안정적으로 유지되었으며, 빠른 템포의 음악과 장면 전환이 자연스럽게 맞물려 SNS 숏폼 콘텐츠에 최적화된 결과물을 완성했습니다. 특히 별도 편집 없이도 TikTok, Reels, 숏폼 광고 채널에 바로 활용할 수 있는 스타일이라는 점이 강점입니다. 빠른 제작 속도와 높은 활용성을 고려하면, 바이럴 중심의 제품 마케팅 콘텐츠 제작에 매우 경쟁력 있는 AI 영상 생성 모델로 평가됩니다. ## 예시 3: 영화풍 스토리텔링 영상 생성 프롬프트 예시: 새벽 비가 내리는 서울 골목길에서 한 남자가 오래된 사진을 손에 쥔 채 천천히 걸어간다. 카메라는 젖은 도로 위 반사된 가로등 불빛을 비추며 인물을 따라간다. 골목 끝에서 한 여성이 우산을 들고 나타나고, 두 사람은 서로를 바라본다. 남자가 떨리는 목소리로 말한다. “이번엔... 늦지 않았어.” 여성이 잠시 미소 지으며 대답한다. “이번엔 기다렸어.” 카메라는 두 사람 사이로 천천히 줌인하며 감성적인 영화 예고편 스타일 장면으로 마무리된다. ## Veo 3.1 Output ## 베오 3.1 평가 베오 3.1은 이번 감성 영화 예고편 테스트에서 압도적인 시각적 완성도를 보여주었습니다. 특히 남성의 트렌치코트에 맺힌 빗방울과 젖은 원단 질감, 여성의 투명 우산 위를 흐르는 물줄기까지 매우 사실적으로 표현되어 높은 화질의 강점을 분명하게 보여주었습니다. 조명 연출 역시 인상적이었습니다. 서울 골목길의 네온사인 조명이 젖은 노면과 인물의 얼굴에 반사되는 방식이 정교하게 표현되며, 단순한 AI 영상 생성 결과물을 넘어 디지털 시네마토그래피에 가까운 분위기를 완성했습니다. 인물 표현력도 안정적이었습니다. 두 인물이 서로 마주 보고 대화하는 장면에서 얼굴 형태나 표정 왜곡이 거의 없이 자연스럽게 유지되었으며, 남성이 손에 들고 있는 오래된 사진 같은 소품 디테일까지 놓치지 않았습니다. 종합적으로 보면, 베오 3.1은 단일 장면의 예술적 완성도, 뛰어난 화질, 감성적인 분위기 연출이 중요한 프로젝트에서 매우 강력한 성능을 보여주는 AI 영상 생성 모델로 평가됩니다. ## Seedance 2.0 Output ## 시댄스 2.0 평가 시댄스 2.0은 이번 감성 영화 예고편 테스트에서 연출력과 편집 완성도 측면에서 강한 인상을 남겼습니다. 하나의 영상 안에서 4~5개의 서로 다른 카메라 앵글이 자연스럽게 이어지며, 발치에서 얼굴 클로즈업으로 이동한 뒤 다시 풀샷으로 전환되는 흐름은 전문 편집자가 구성한 듯한 완성도를 보여주었습니다. 컷 전환이 반복되는 장면에서도 인물의 의상, 얼굴 특징, 남성이 들고 있는 오래된 사진 같은 핵심 요소가 안정적으로 유지되었습니다. 이러한 일관성은 스토리텔링 중심 콘텐츠 제작에서 매우 중요한 요소이며, 장면이 바뀌어도 몰입감을 해치지 않는 결과물을 만들어냈습니다. 또한 인물 간 거리감, 시선 처리, 감정선 연결도 자연스럽게 표현되어 이야기 흐름에 몰입하기 쉬운 구성을 보여주었습니다. 빠른 호흡의 드라마틱 콘텐츠, 숏폼 광고, 감성적인 SNS 영상처럼 리듬감 있는 영상 제작에 특히 강점을 가진 모델로 평가됩니다. 종합적으로 보면, 시댄스 2.0은 뛰어난 컷 구성 능력, 강력한 인물 일관성, 빠른 전개형 스토리텔링에 강점을 가진 AI 영상 생성 모델입니다. ## 가격 비교 PiAPI 기준으로 시댄스 2.0과 베오 3.1은 과금 방식과 제공 옵션에서 차이를 보입니다. 두 모델 모두 생성된 영상 길이(초 단위)를 기준으로 비용이 계산되며, 해상도와 오디오 포함 여부에 따라 가격이 달라질 수 있습니다. ## 시댄스 2.0 가격 시댄스 2.0은 다양한 해상도 옵션을 제공하며, 480p 기준 초당 $0.10부터 시작합니다. 720p는 초당 $0.20, 1080p는 초당 $0.50으로 설정되어 있어 품질과 예산에 맞춰 선택할 수 있습니다. 또한 Fast 버전은 480p 초당 $0.08, 720p 초당 $0.16으로 제공되어 빠른 테스트나 대량 제작 환경에 유리합니다. ## 베오 3.1 가격 베오 3.1은 오디오 포함 여부에 따라 가격이 나뉘어 제공됩니다. Veo 3.1 Video + Audio는 초당 $0.24, 오디오 제외 버전은 초당 $0.12입니다. Fast 버전은 오디오 포함 초당 $0.09, 오디오 제외 초당 $0.06으로 보다 경제적으로 사용할 수 있습니다. ## 어떤 모델이 더 가성비가 좋을까? 짧은 숏폼 콘텐츠, SNS 광고, 반복 생성 작업이 중심이라면 빠른 생성 옵션과 다양한 해상도를 제공하는 시댄스 2.0이 실용적인 선택이 될 수 있습니다. 반면 고품질 영상과 오디오까지 함께 필요한 브랜드 콘텐츠나 프리미엄 프로젝트라면 베오 3.1이 높은 완성도를 제공하는 선택지가 될 수 있습니다. 작성 시점 기준 가격이며, 최신 요금은 PiAPI API Documentation 에서 확인하는 것이 좋습니다. ## PiAPI를 통한 API 사용 방법 PiAPI를 사용하면 시댄스 2.0 과 베오 3.1 같은 최신 AI 영상 생성 모델을 간단한 API 요청만으로 빠르게 활용할 수 있습니다. 텍스트 프롬프트 또는 이미지를 입력해 영상 생성 작업을 요청하고, 생성 완료 후 결과물을 받아 서비스나 콘텐츠 제작에 활용할 수 있습니다. Google 영상 모델 연동만 먼저 확인하려면 Veo 3.1 API 페이지를 참고하세요. ## 시작하기 위한 단계 ## API 키 발급 PiAPI Workspace 에 로그인한 뒤 X-API-Key를 발급받습니다. 신규 사용자는 테스트용 크레딧이 제공될 수 있습니다. ## 영상 생성 작업 요청 POST 요청을 통해 원하는 모델을 선택하고 프롬프트를 입력합니다. 시댄스 2.0 또는 베오 3.1 모델과 함께 해상도, 길이, 오디오 등 옵션도 설정할 수 있습니다. ## 작업 상태 확인 생성 작업이 완료될 때까지 상태를 확인합니다. 처리 시간은 모델과 설정에 따라 달라질 수 있습니다. ## 결과물 다운로드 및 활용 응답으로 제공되는 영상 URL을 받아 다운로드하거나 앱, 웹서비스, 광고 콘텐츠 등에 바로 활용할 수 있습니다. ## 최종 결론 시댄스 2.0과 베오 3.1은 모두 뛰어난 성능을 갖춘 최신 AI 영상 생성 모델이지만, 강점은 서로 다릅니다. 시댄스 2.0은 빠른 제작 속도, 멀티샷 구성, 숏폼 콘텐츠 제작, 실전 마케팅 활용성에서 강점을 보이며 SNS 광고나 반복 제작이 필요한 환경에 적합합니다. 반면 베오 3.1은 뛰어난 화질, 사실적인 조명과 질감 표현, 시네마틱 연출에서 높은 완성도를 보여주며 브랜드 필름, 프리미엄 광고, 영화풍 콘텐츠 제작에 더욱 적합한 선택지가 될 수 있습니다. 결국 어떤 모델이 더 좋은지는 목적에 따라 달라집니다. 빠른 제작과 효율성을 원한다면 시댄스 2.0, 최고 수준의 영상 품질과 몰입감을 원한다면 베오 3.1이 좋은 선택이 될 것입니다. 오늘 바로 PiAPI를 통해 Seedance 2.0 과 Veo 3.1 테스트를 시작해 보세요! PiAPI 와 함께라면 영상, 이미지, 음악, 챗봇 등 20가지 이상의 AI 모델이 선사하는 강력한 성능을 활용하실 수 있습니다. 지금 가입 하시고, 더욱 스마트하고 신속하며 대규모로 서비스를 구축해 나가세요. ## 클링 AI 3.0 vs 시댄스 비교: 어떤 AI 영상 생성기가 더 좋을까? (2026) 클링 AI 3.0와 시댄스를 비교해 화질, 가격, 생성 속도, 사용성, API 지원까지 한눈에 확인하세요. 어떤 AI 영상 생성기가 더 적합한지 알아보세요. 최근에는 짧은 영상 콘텐츠, 광고 소재, SNS 콘텐츠 제작까지 AI로 해결하려는 수요가 크게 늘어나고 있습니다. 이에 따라 몇 줄의 텍스트만으로 영상을 만들거나 이미지를 자연스럽게 움직이는 영상으로 바꿔주는 AI 영상 생성기에도 관심이 집중되고 있습니다. 그중에서도 현재 많이 비교되는 모델이 바로 클링 AI 3.0 과 시댄스 입니다. 클링 AI 3.0는 자연스러운 모션 표현과 높은 영상 완성도로 주목받고 있으며, 시댄스는 빠른 생성 속도와 실용적인 워크플로우, 그리고 ByteDance 생태계를 기반으로 한 확장성이 강점으로 꼽힙니다. 두 모델 모두 강력한 기능을 갖추고 있지만, 실제 사용 경험은 꽤 다를 수 있습니다. 이번 가이드에서는 클링 AI 3.0와 시댄스를 화질, 기능, 생성 모드, 가격, API 활용성까지 비교해 어떤 AI 영상 생성기가 더 적합한지 알아보겠습니다. ## 클링 AI 3.0이란? 클링 AI 3.0은 고품질 영상 생성에 특화된 최신 AI 비디오 모델로, 텍스트 프롬프트나 이미지를 기반으로 짧은 영상을 제작할 수 있는 도구입니다. 사실적인 움직임 표현, 자연스러운 카메라 모션, 안정적인 캐릭터 일관성으로 주목받으며 많은 크리에이터와 마케터들이 관심을 보이고 있습니다. 특히 클링 AI 3.0은 시네마틱한 장면 연출, 인물 중심 영상, 감성적인 분위기의 콘텐츠 제작에서 강점을 보이는 편입니다. 프롬프트에 따라 카메라 이동, 조명 분위기, 동작 표현까지 비교적 자연스럽게 구현할 수 있어 광고 영상, SNS 숏폼, 브랜딩 콘텐츠 제작에도 활용됩니다. 전반적으로 높은 영상 완성도와 디테일한 표현력이 강점인 AI 영상 생성 모델로 평가받고 있습니다. ## 시댄스란? 시댄스는 텍스트 또는 이미지를 기반으로 영상을 생성할 수 있는 AI 비디오 모델로, 빠른 생성 속도와 실용적인 사용성을 강점으로 내세우고 있습니다. 다양한 스타일의 영상 제작이 가능하며, 콘텐츠 제작자부터 마케터, 개발자까지 폭넓게 활용할 수 있는 것이 특징입니다. 특히 시댄스는 간단한 프롬프트만으로도 짧은 홍보 영상, SNS 콘텐츠, 제품 소개 영상 등 실무형 결과물을 빠르게 제작하는 데 강점을 보입니다. 반복 테스트가 필요한 광고 소재 제작이나 여러 버전의 영상을 빠르게 생성해야 하는 작업에서도 효율적으로 활용될 수 있습니다. 또한 ByteDance 생태계와 연관된 모델로 알려져 있어 향후 확장성과 API 활용 측면에서도 관심을 받고 있는 AI 영상 생성기입니다. 시댄스의 기능, 사용법, 가격, API 활용 방법을 더 자세히 알고 싶다면 시댄스 2.0 사용법과 가격 가이드 를 참고해보세요. ## 기능 차이점 및 생성 모드 비교 클링 AI 3.0와 시댄스는 모두 강력한 AI 영상 생성 기능을 제공하지만, 실제 사용 경험과 강점은 다소 차이가 있습니다. 사용 목적에 따라 더 적합한 선택지가 달라질 수 있습니다. ## 텍스트를 영상으로 생성 (Text to Video) 두 모델 모두 텍스트 프롬프트만으로 영상을 생성할 수 있습니다. 클링 AI 3.0은 장면 연출과 분위기 표현에 강점을 보이며, 보다 시네마틱한 결과물을 기대할 수 있습니다. 반면 시댄스는 빠른 생성 속도와 실용적인 결과물 제작에 강해 광고 소재나 SNS 콘텐츠 제작에 효율적입니다. ## 이미지를 영상으로 변환 (Image to Video) 정적인 이미지를 자연스럽게 움직이는 영상으로 바꾸는 기능 역시 두 모델 모두 지원합니다. 클링 AI 3.0은 카메라 이동과 부드러운 모션 표현이 강점이며, 시댄스는 제품 이미지나 인물 이미지를 빠르게 숏폼 영상으로 전환하는 작업에 유리합니다. ## 영상 화질 및 디테일 클링 AI 3.0은 디테일한 장면 표현과 자연스러운 조명 연출에서 강한 평가를 받는 편입니다. 시댄스는 선명하고 깔끔한 결과물을 빠르게 생성하는 방향에 강점이 있습니다. ## 생성 속도 및 작업 효율 빠른 반복 생성과 여러 버전 테스트가 중요하다면 시댄스가 효율적인 선택이 될 수 있습니다. 반대로 완성도 높은 결과물을 중심으로 작업한다면 클링 AI 3.0이 더 만족스러울 수 있습니다. ## 활용 목적 브랜딩 영상, 감성적인 콘텐츠, 시네마틱 영상 제작에는 클링 AI 3.0이 강점을 보일 수 있으며, 광고 영상 제작, 제품 홍보, SNS 숏폼 콘텐츠처럼 속도와 실용성이 중요한 작업에는 시댄스가 적합한 편입니다. ## 예시 1: Text to Video 비교 프롬프트: 늦은 밤 비가 내리는 서울의 네온 거리. 젖은 도로 위로 반사되는 간판 불빛과 지나가는 차량의 헤드라이트가 보인다. 카메라는 천천히 뒤로 이동하며 검은 우산을 쓴 젊은 여성을 따라간다. 여성은 잠시 멈춰 서서 카메라를 바라본 뒤 자연스럽게 한국어로 말한다. “괜찮아, 결국 다 지나갈 거야. 오늘도 정말 수고했어.” 말할 때 입 모양이 자연스럽게 맞아야 하며 감정이 담긴 표정 변화가 보여야 한다. 이후 그녀가 미소를 짓고 다시 걷기 시작한다. 주변 사람들은 우산을 쓰고 지나가며, 바람에 머리카락과 코트 자락이 흔들린다. 영화 같은 조명, 사실적인 빗방울 표현, 자연스러운 군중 움직임, 얕은 심도, 시네마틱한 분위기, 고품질 영상. ## Seedance Output 시댄스 2.0은 이번 고난도 프롬프트 테스트에서 전반적으로 매우 안정적인 결과를 보여주었습니다. 서울의 밤거리, 네온 조명, 카메라 회전 연출, 지정된 한국어 대사까지 프롬프트 요소를 정확하게 반영했으며, 코트와 피부 질감 표현도 자연스러웠습니다. 특히 “괜찮아”, “수고했어”와 같은 한국어 발음 구간에서 립싱크 정확도가 뛰어나 대사 전달력이 인상적이었습니다. 또한 카메라 이동과 고개 회전 장면에서도 얼굴과 의상 일관성이 잘 유지되어 완성도 높은 결과물로 평가할 수 있습니다. ## Kling 3.0 Output 클링 3.0은 시각적 완성도 측면에서 매우 강력한 결과를 보여주었습니다. 4K급 선명도와 사실적인 조명, 높은 수준의 질감 표현이 돋보였으며, 비가 내리는 환경과 젖은 가죽 재질의 반사 표현도 자연스럽게 구현되었습니다. 또한 장면 전체에서 캐릭터와 배경의 일관성이 안정적으로 유지되어 시간적 안정성 역시 우수했습니다. 다만 이번 테스트에서는 한국어 대사 표현과 이후 걸어가는 동작 지시를 충분히 반영하지 못해, 프롬프트 수행 정확도 측면에서는 아쉬움이 남는 결과였습니다. ## 예시 2: Text to Video 한국어 광고 영상 프롬프트 (한국어): 밝고 세련된 한국식 욕실 공간. 아침 햇살이 창문으로 들어오고, 깨끗한 세면대 위에 프리미엄 스킨케어 세럼 제품이 놓여 있다. 카메라는 제품을 클로즈업한 뒤 자연스럽게 젊은 한국인 여성이 등장한다. 그녀는 제품을 손에 들고 카메라를 보며 밝고 자신감 있는 표정으로 자연스럽게 한국어로 말한다. “피부가 달라지는 순간, 매일 아침 자신감이 시작돼요.” 이후 그녀가 세럼을 얼굴에 바르고 미소 짓는다. 카메라는 피부 결을 자연스럽게 보여주며 제품 패키지를 다시 비춘다. 마지막 장면에서 그녀가 카메라를 보며 말한다. “오늘의 피부, 지금 시작하세요.” 입 모양이 한국어 발음과 정확히 맞아야 하며, 광고처럼 세련된 조명, 깨끗한 화면 구성, 자연스러운 손동작, 고급스러운 분위기, 시네마틱 카메라 무빙, 고품질 영상. ## Seedance Output 시댄스 2.0은 이번 광고형 프롬프트 테스트에서 매우 뛰어난 실행력을 보여주었습니다. 넓은 장면 구성부터 인물 등장, 한국어 대사, 제품 사용 장면, 마지막 콜투액션까지 전체 흐름을 자연스럽게 반영하며 완성도 높은 광고 영상처럼 구현했습니다. 화면은 깔끔하고 세련된 톤으로 정리되어 실제 TV 광고처럼 정돈된 느낌을 주었으며, 상업용 콘텐츠에 적합한 결과물을 보여주었습니다. 특히 한국어 음성과 립싱크 정확도가 매우 높았고, 턱선과 목 움직임까지 자연스럽게 표현되어 몰입감을 높였습니다. 또한 장면 전환 과정에서도 인물 얼굴과 욕실 배경이 안정적으로 유지되어 실무형 광고 제작에 강점을 보여주었습니다. ## Kling 3.0 Output 클링 3.0은 이번 광고형 프롬프트 테스트에서 매우 완성도 높은 결과를 보여주었습니다. 제품 클로즈업 장면부터 인물 등장, 한국어 대사, 세럼 사용 장면, 마지막 미소 연출까지 주요 지시사항을 거의 정확하게 반영했습니다. 밝고 깔끔한 욕실 공간과 자연스러운 아침 햇살 표현도 뛰어나 실제 뷰티 광고에 가까운 분위기를 구현했습니다. 또한 “자신감이 시작돼요” 대사 구간의 립싱크 정확도도 높았으며, 장면 전환 과정에서도 제품 병과 인물 얼굴의 일관성이 안정적으로 유지되었습니다. ## 예시 3: Image to Video 스포츠 광고 스타일 영상 생성 이번 테스트에서는 정적인 스포츠 제품 이미지를 기반으로 두 모델이 얼마나 역동적인 광고형 영상을 생성할 수 있는지 비교했습니다. 농구공의 회전 움직임, 카메라 무빙, 조명 연출, 제품 디테일 유지력 등을 중심으로 확인했습니다. 이미지 생성 프롬프트 (한국어): 밝고 현대적인 실내 농구 코트 중앙 바닥 위에 프리미엄 농구공 하나가 놓여 있다. 농구공 표면의 가죽 질감과 디테일이 선명하게 보이며, 코트 바닥에는 자연스러운 반사가 비친다. 뒤쪽에는 밝은 경기장 조명과 흐릿한 관중석 배경이 보인다. 역동적인 스포츠 광고 사진 스타일, 선명한 색감, 초고해상도, 사실적인 질감, 강렬한 조명, 정교한 디테일. 움직임 프롬프트 (Image to Video): 카메라가 낮은 각도에서 농구공을 천천히 향해 돌진하며 시작한다. 잠시 후 농구공이 바닥에서 튀어 올라 한 젊은 선수가 등장해 공을 집어 들고 빠르게 드리블하며 코트를 질주한다. 경기장 조명이 강하게 비추고 관중석은 긴장감 있게 흐릿하게 보인다. 선수는 3점 라인 밖에서 점프 슛을 시도하고, 공은 느린 화면처럼 공중을 날아가 버저와 동시에 골대에 깨끗하게 들어간다. 관중석 조명이 터지며 환호 분위기가 연출된다. 슛이 성공한 직후 선수가 두 주먹을 쥐고 카메라를 보며 크게 외친다. “해냈다! 우리가 이겼다!” 대사는 자연스러운 한국어 발음과 정확한 립싱크으로 표현되어야 한다. 이후 카메라는 선수를 중심으로 빠르게 회전하며 승리의 순간을 강조한다. 역동적인 스포츠 광고 스타일, 강렬한 에너지, 자연스러운 인체 움직임, 사실적인 공의 궤적, 고품질 영상. ## Seedance Output 시댄스 2.0은 이번 스포츠 광고형 테스트에서 복잡한 장면 흐름을 매우 안정적으로 구현했습니다. 낮은 각도 시작 장면부터 드리블, 점프 슛, 버저비터 성공, 마지막 감정 표현까지 전체 서사를 자연스럽게 따라가며 높은 프롬프트 이해도를 보여주었습니다. 코트 조명과 경기장 분위기도 실제 스포츠 중계처럼 생동감 있게 표현되었으며, “해냈다! 우리가 이겼다!”라는 한국어 외침의 립싱크 정확도 역시 뛰어났습니다. 다만 빠른 점프 슛 동작과 승리 연출로 전환되는 일부 장면에서는 약간의 프레임 왜곡이 보여, 급격한 움직임 구간에서는 소폭의 아쉬움이 있었습니다. ## Kling 3.0 Output 클링 3.0은 이번 스포츠 광고형 테스트에서 높은 수준의 시각적 완성도와 안정적인 움직임 표현을 보여주었습니다. 경기장 조명, 코트 분위기, 유니폼 질감 표현이 매우 뛰어나 실제 스포츠 광고에 가까운 몰입감을 전달했으며, 빠른 액션 장면에서도 전체 영상의 안정성이 잘 유지되었습니다. 또한 “해냈다!”라는 승리 대사와 포즈도 자연스럽게 구현해 강한 감정 전달력을 보여주었습니다. 립싱크 정확도 역시 우수한 편이었으며, 특히 힘차게 외치는 순간의 얼굴 근육 움직임과 긴장감 있는 표정 표현이 인상적이었습니다. 다만 빠른 전개에 맞춰 전체 대사가 다소 축약되어 표현된 점은 일부 아쉬움으로 남았습니다. ## 가격 비교 클링 AI 3.0와 시댄스는 모두 사용량 기반(Pay-as-you-go) 방식으로 제공되어, 생성한 영상의 길이와 해상도에 따라 비용이 달라집니다. 개인 크리에이터부터 마케팅 팀, 개발자까지 필요한 만큼 유연하게 사용할 수 있다는 점이 장점입니다. 시댄스 2.0은 PiAPI 기준으로 480p 해상도는 초당 $0.10부터 시작하며, 720p는 초당 $0.20, 1080p는 초당 $0.50부터 이용할 수 있습니다. 빠른 생성 옵션인 Fast 모델은 480p 초당 $0.08, 720p 초당 $0.16부터 제공되어 반복 테스트나 빠른 작업에 적합합니다. 클링 AI 3.0은 PiAPI 기준으로 720p 영상 생성이 초당 $0.10부터 시작하며, 오디오 포함 시 초당 $0.15입니다. 1080p는 초당 $0.15, 오디오 포함 시 초당 $0.20부터 이용할 수 있어 고해상도 영상 제작에도 경쟁력 있는 가격대를 보여줍니다. 모델 해상도 / 옵션 PiAPI 기준 시작 가격 적합한 작업 시댄스 2.0 480p 초당 $0.10부터 기본 영상 생성 시댄스 2.0 720p 초당 $0.20부터 SNS 및 광고 소재 제작 시댄스 2.0 1080p 초당 $0.50부터 고해상도 결과물 시댄스 2.0 Fast 480p / 720p 초당 $0.08 / $0.16부터 반복 테스트나 빠른 작업 클링 AI 3.0 720p 초당 $0.10부터, 오디오 포함 시 초당 $0.15 표준 영상 생성 클링 AI 3.0 1080p 초당 $0.15부터, 오디오 포함 시 초당 $0.20부터 고해상도 영상 제작 빠르게 여러 버전을 생성하거나 비용 효율성을 중시한다면 시댄스가 유리할 수 있으며, 고화질 영상과 오디오 포함 결과물이 필요하다면 클링 AI 3.0 역시 매력적인 선택지입니다. 위 가격은 작성 시점 기준이며, 최신 요금 및 상세 옵션은 PiAPI API Documentation 에서 확인하시길 권장합니다. ## API 가이드 클링 AI 3.0와 시댄스를 단순히 웹사이트에서 사용하는 것뿐만 아니라, API를 통해 직접 서비스나 업무 프로세스에 연동해 활용할 수도 있습니다. API를 사용하면 앱, SaaS 플랫폼, 자동화 시스템, 내부 마케팅 툴 등에 AI 영상 생성 기능을 손쉽게 추가할 수 있습니다. 예를 들어 전자상거래 서비스에서는 상품 이미지를 자동으로 광고 영상으로 전환할 수 있고, 마케팅 팀은 여러 버전의 숏폼 영상을 대량으로 생성해 광고 테스트를 진행할 수 있습니다. 또한 콘텐츠 제작자는 반복적인 영상 제작 작업을 자동화해 시간을 절약할 수 있습니다. 클링 AI 3.0 API 는 높은 영상 완성도와 고해상도 결과물이 필요한 프로젝트에 적합하며, 시댄스 API 는 빠른 생성 속도와 반복 테스트가 중요한 콘텐츠 운영 환경에서 효율적으로 활용될 수 있습니다. 시댄스 2.0의 모델별 가격과 실제 API 사용 흐름은 시댄스 2.0 API 가이드 에서 더 자세히 확인할 수 있습니다. 여러 모델을 각각 따로 연동하는 대신 PiAPI 를 활용하면 하나의 통합 API로 클링 AI 3.0, 시댄스를 포함한 다양한 AI 모델에 접근할 수 있습니다. 이를 통해 개발 시간 단축, 운영 효율 향상, 테스트 속도 개선 등의 장점을 기대할 수 있습니다. 최신 API 지원 모델과 상세 연동 방법은 PiAPI 공식 문서를 참고하는 것이 좋습니다. ## 최종 결론 클링 AI 3.0와 시댄스는 모두 강력한 AI 영상 생성 모델이지만, 강점은 서로 조금 다릅니다. 보다 사실적인 영상 퀄리티, 시네마틱한 연출, 조명과 질감 표현을 중요하게 생각한다면 클링 AI 3.0이 좋은 선택이 될 수 있습니다. 브랜드 영상, 감성적인 콘텐츠, 높은 완성도의 결과물을 원하는 사용자에게 잘 어울립니다. 반면 빠른 생성 속도, 안정적인 프롬프트 수행력, 광고형 콘텐츠 제작, 여러 버전의 영상 테스트가 중요하다면 시댄스가 더욱 실용적인 선택지가 될 수 있습니다. 특히 마케팅 팀, 콘텐츠 운영자, 반복 제작이 많은 사용자에게 효율적입니다. 결국 어떤 모델이 더 좋은지는 사용 목적에 따라 달라집니다. 영상미와 디테일을 우선한다면 클링 AI 3.0, 속도와 실무 활용성을 중시한다면 시댄스를 고려해볼 만합니다. 지금 PiAPI 에서 클링 AI 3.0 , 시댄스 를 포함한 20개 이상의 AI 모델을 바로 테스트해보세요. 이미지, 영상, 챗봇, 음악 생성까지 하나의 API 로 연동할 수 있으며, 더 빠르고 효율적인 AI 서비스 구축이 가능합니다. 지금 시작해 보세요. ## GPT Image 2 API Guide: Features, Prompt Tips, Pricing, and Examples Learn how GPT Image 2 works, explore its key features, prompt tips, pricing, and real examples for production-ready AI image generation. AI image generation just took a serious leap forward. GPT Image 2 This is not just about better-looking images. It is about speed, control, and accessibility. What used to take hours of design work can now be generated in seconds with the right prompt. In this guide, we break down how the GPT Image API works, its key features, prompt tips, and real examples so you can see what this new model is actually capable of. ## What is GPT Image 2 ( GPT Image API ) GPT Image 2 is a commonly used term for the latest image generation capabilities from OpenAI. It is not an official name, but many users use it to describe the newer model with improved quality and better prompt understanding. GPT Image API Compared to earlier versions, it produces more realistic results, follows instructions more closely, and handles a wider range of styles. This makes it useful for things like marketing visuals, product mockups, and creative content. ## Key Features of GPT Image API The GPT Image API introduces several upgrades that make image generation more powerful and practical for real-world use. ## Advanced Reasoning and Multi-Image Generation The model is able to interpret prompts more intelligently, allowing it to generate multiple distinct images from a single input. This makes it useful for exploring variations and creative directions quickly. ## Greater Precision and Control It handles highly specific instructions with strong accuracy. Fine details such as textures, small objects, and complex compositions are rendered more clearly, giving users better control over the final output. ## Stronger Multilingual Understanding The model performs better across multiple languages, especially in languages like Japanese, Korean, Chinese, Hindi, and Bengali. This makes it more accessible for global users creating prompts in their native language. ## Improved Realism and Style Quality Image outputs show noticeable improvements in visual fidelity. Whether generating realistic scenes or stylized content, the results are more polished and consistent. ## Flexible Aspect Ratios The API supports a wider range of image formats, from wide layouts such as 3:1 to vertical formats like 1:3. This makes it suitable for different use cases, including social media, banners, and mobile content. ## Better Real-World Understanding With a more up-to-date knowledge base, the model has a stronger understanding of real-world concepts and context, helping it generate more relevant and accurate visuals. ## Batch Image Generation Users can generate multiple outputs in a single request, allowing for faster iteration and comparison between different variations. ## Prompt Guide for GPT Image API Getting good results with the GPT Image API comes down to writing clear and specific prompts. A simple structure works best: Subject: what is in the image Style: realistic, anime, cinematic Lighting: soft, dramatic, natural Details: background, mood, composition Example: A futuristic city skyline at night, cyberpunk style, neon lights, cinematic lighting, high detail official prompt guide from OpenAI ## Example 1: Product Mockup Prompt: Minimalist product mockup of a black wireless earbuds case on a matte surface, soft studio lighting, subtle reflections, clean background, premium branding style Output ## Evaluation: The output shows strong control over composition and lighting, with a centered layout that keeps focus on the product. Soft studio lighting creates a smooth gradient across the matte surface without harsh reflections. The material looks realistic, with a clean matte texture and subtle details like the LED indicator. Combined with minimalist branding and a dark-on-dark color palette, the overall result feels premium. ## Example 2: Poster / Ad Creative Prompt: Modern promotional poster for a sports shoe brand, dynamic composition, bold typography, high contrast lighting, motion blur effect, vibrant colors, urban streetwear aesthetic, clean layout, commercial advertising style Output ## Evaluation: The output delivers a strong commercial look with a dynamic composition that draws attention directly to the product. Motion blur and light trails add a sense of speed, matching the overall urban streetwear theme. Typography is bold and impactful, and the model handles text surprisingly well, which is often a weak point in image generation. The high-contrast color palette helps the product stand out, while the subtle background details add context without being distracting. The shoe itself is rendered with good detail, including textures and lighting, making the image feel realistic and suitable for marketing use. ## Example 3: Food Photography Prompt: A close-up of a freshly made brunch plate with avocado toast, poached eggs, and a cup of coffee, natural window lighting, shallow depth of field, soft shadows, realistic food photography style, high detail Output ## Observation: The output shows strong realism in both lighting and texture, making it look very close to professional food photography. Natural side lighting creates soft shadows and highlights details like the moisture on the eggs, giving the image a warm and inviting feel. Depth of field is handled well, with the main subject in sharp focus while background elements remain softly blurred. Texture details are especially convincing, from the bread crust to the avocado and egg, adding to the overall realism. The earthy color palette reinforces a fresh and natural look, making the image suitable for menus, social media, or lifestyle content. ## How to Use GPT Image API Getting started with the GPT Image API is simple, and platforms like PiAPI make it even easier to access the latest models, including GPT Image 2. Get Access to the API. PiAPI Send a Prompt. Write a clear text prompt describing the image you want to generate. The more specific your prompt, the better the results. Generate and Iterate. Generate your image, then refine your prompt or create variations to improve the output. here ## Pricing Pricing for GPT Image 2 on PiAPI is usage-based. The gpt-image-2-preview model is priced at $0.10/img per image generation at the time of writing. GPT Image 2 API documentation ## Verdict GPT Image 2 represents a clear step forward in AI image generation. The improvements in prompt understanding, visual quality, and consistency make it far more practical for real-world use compared to earlier models. It performs well across a wide range of use cases, from product mockups and marketing creatives to realistic lifestyle visuals. The ability to generate high-quality images quickly with simple prompts makes it especially useful for developers, marketers, and content creators looking to speed up their workflow. That said, it is still not a complete replacement for professional design tools in every scenario. Fine control and highly specific creative direction may still require manual editing or additional tools. However, for most everyday use cases, the model is more than capable. Overall, GPT Image 2 is a strong option for anyone looking to integrate image generation into their workflow, especially when paired with an accessible API platform. GPT Image 2 PiAPI Sign up ## 시댄스 2.0 완벽 가이드: 사용법, 가격, API, 영상 생성 예시까지 시댄스 2.0 사용법, 가격, API까지 한 번에 정리. AI 영상 제작을 위한 Seedance 기능, 생성 모드, 실제 예시와 평가까지 확인해보세요. 요즘은 별도의 촬영 없이도 텍스트나 이미지 입력만으로 영상을 만들 수 있는 시대가 되었습니다. 이러한 변화 속에서 AI 기반 영상 생성 도구에 대한 관심도 빠르게 높아지고 있습니다. 바이트댄스(ByteDance) 가 개발한 시댄스(Seedance)는 그 중 하나로, 간단한 프롬프트만으로 다양한 스타일의 영상을 생성할 수 있는 AI 도구입니다. 드리미나(Dreamina)와 함께 AI 콘텐츠 제작 생태계를 확장하고 있으며, 시댄스 2.0 에서는 더 다양한 생성 방식과 향상된 결과를 제공합니다. 이번 글에서는 시댄스 2.0 사용법, 가격, API 활용 방법을 중심으로 기능과 특징을 정리하고, 실제 생성 예시를 통해 결과 품질도 함께 살펴보겠습니다. 시댄스가 클링 AI 3.0과 비교했을 때 어떤 장단점이 있는지 궁금하다면 클링 3.0과 시댄스 비교 가이드 도 함께 확인해보세요. ## 시댄스란? 시댄스(Seedance)는 바이트댄스(ByteDance)가 개발한 AI 영상 생성 도구로, 텍스트나 이미지를 기반으로 영상을 자동으로 생성할 수 있습니다. 복잡한 영상 편집 과정 없이도 간단한 프롬프트 입력만으로 다양한 스타일의 영상을 만들 수 있는 것이 특징입니다. 또한 드리미나(Dreamina)와 같은 다른 AI 콘텐츠 생성 도구와 함께 활용되며, 이미지 생성, 영상 생성 등 다양한 콘텐츠 제작 흐름을 지원합니다. 이러한 점에서 시댄스는 AI 기반 콘텐츠 제작을 보다 쉽게 만들어주는 도구로 주목받고 있습니다. ## 시댄스 2.0 주요 기능 ## 세 가지 생성 모드 텍스트 기반 생성(text-to-video), 시작 및 종료 프레임 기반 생성(first & last frames), 그리고 다양한 레퍼런스를 활용하는 omni reference 모드를 지원합니다. 이미지, 영상, 오디오를 조합하여 보다 다양한 영상 생성이 가능합니다. ## 멀티모달 레퍼런스 지원 프롬프트에서 @image , @video , @audio 와 같은 형식을 사용하여 이미지, 영상, 오디오를 레퍼런스로 활용할 수 있습니다. 이를 통해 보다 정밀한 영상 제어가 가능합니다. ## 유연한 영상 길이 및 화면 비율 4초에서 15초 길이의 영상을 생성할 수 있으며, 21:9, 16:9, 4:3, 1:1, 3:4, 9:16 등 다양한 화면 비율을 지원합니다. first & last frames 모드에서는 기준 이미지의 비율을 따릅니다. ## 다양한 모델 옵션 seedance-2 , seedance-2-fast , seedance-2-preview , seedance-2-fast-preview 등 다양한 모델 옵션을 제공하며, 속도와 비용에 따라 선택할 수 있습니다. ## 영상 편집 기능(프리뷰) 프리뷰 모델에서는 영상 편집 기능도 지원하며, 기존 영상을 기반으로 AI를 활용한 변형 및 편집이 가능합니다. ## Omni Reference 모드 최대 12개의 레퍼런스(이미지, 영상, 오디오)를 결합하여 보다 복잡하고 창의적인 영상 생성을 지원합니다. 다양한 스타일 변환이나 사운드 추가, 모핑 효과 등을 구현할 수 있습니다. ## 시댄스 2.0 사용법 먼저 시댄스 2.0을 사용할 수 있는 플랫폼에 접속합니다. 시댄스는 다양한 방식으로 접근할 수 있으며, API를 통해 사용하는 경우 PiAPI 와 같은 플랫폼에서 시댄스 2 모델을 선택하여 쉽게 사용할 수 있습니다. ## 1. 생성 방식 선택 시댄스 2.0에서는 텍스트 기반 생성, 시작 및 종료 프레임 기반 생성, 그리고 omni reference 방식 중 하나를 선택할 수 있습니다. 원하는 결과에 따라 적절한 생성 방식을 선택합니다. ## 2. 레퍼런스 추가 필요한 경우 이미지, 영상, 오디오를 레퍼런스로 추가할 수 있습니다. 특히 omni reference 모드에서는 여러 레퍼런스를 함께 활용하여 보다 정교한 결과를 만들 수 있습니다. ## 3. 설정 조정 및 생성 실행 영상 길이, 화면 비율 등의 설정을 조정한 후 생성 버튼을 눌러 영상을 생성합니다. 설정에 따라 결과의 스타일과 품질이 달라질 수 있습니다. ## 4. 결과 확인 및 다운로드 생성된 영상을 확인한 후, 원하는 경우 다운로드하거나 추가로 수정할 수 있습니다. 결과가 기대에 맞지 않는 경우 프롬프트를 수정하여 다시 생성할 수 있습니다. ## 생성 모드 개요 ## 텍스트 기반 영상 생성 텍스트 프롬프트만으로 영상을 생성하는 가장 기본적인 방식입니다. 장면, 스타일, 분위기 등을 설명하면 해당 내용을 기반으로 영상이 생성됩니다. ## 시작 및 종료 프레임 기반 생성 시작 이미지와 종료 이미지를 입력하여 두 장면 사이의 자연스러운 움직임을 생성하는 방식입니다. 특정 흐름이나 전환을 표현할 때 유용합니다. ## Omni Reference 기반 생성 이미지, 영상, 오디오 등 다양한 레퍼런스를 함께 활용하여 보다 복잡하고 정교한 영상을 생성하는 방식입니다. 여러 입력을 조합해 창의적인 결과를 만들 수 있습니다. ## 시댄스 2.0 예시 및 평가 ## 예시 1: 텍스트 기반 영상 생성 프롬프트: 벚꽃 공원을 걷는 소녀, 애니메이션 풍, 부드러운 조명, 몽환적인 분위기, 살랑이는 바람 생성된 영상은 프롬프트의 주요 요소들을 전반적으로 잘 반영하고 있습니다. 벚꽃 공원 배경과 소녀의 자연스러운 움직임이 잘 표현되었으며, 애니메이션 스타일과 부드러운 조명도 일관되게 유지됩니다. 또한 머리카락과 벚꽃 잎의 움직임을 통해 은은한 바람 효과도 자연스럽게 전달됩니다. 전체적으로 분위기와 스타일이 잘 어우러진 완성도 높은 결과입니다. ## 예시 2: 텍스트 기반 영상 생성 프롬프트: 조용한 카페에서 두 사람이 마주 앉아 대화를 나누는 장면, 한 사람은 창가 쪽에 앉아 있고 다른 한 사람은 커피를 들고 있음, 자연스러운 조명, 영화 같은 연출, 카메라가 천천히 두 사람 사이를 이동, "오랜만이네"라고 말하는 분위기 전체적으로 두 인물 간의 대화 장면이 자연스럽게 표현되었으며, 감정 변화도 비교적 잘 전달됩니다. 특히 "오랜만이네"라는 대사의 분위기가 장면의 흐름과 잘 어우러지며, 재회 상황의 미묘한 긴장감을 만들어냅니다. 구도 측면에서는 창가의 직선적인 구조와 인물 배치를 통해 장면에 안정감과 대비를 동시에 주고 있으며, 카메라 이동 역시 부드럽게 이어져 몰입감을 높입니다. ## 예시 3: 시작 및 종료 프레임 기반 생성 프롬프트: 두 사람이 기차역 플랫폼에서 서로를 바라보며 짧게 대화를 나누는 장면, 감정이 담긴 분위기, 한 사람이 떠나기 전의 긴장감, 자연스러운 움직임, 영화 같은 연출, 카메라가 천천히 인물 주변을 이동하며 장면을 이어줌 시작 프레임: 마지막 프레임: 전체적으로 두 인물이 기차역 플랫폼에서 대화를 나누는 장면이 자연스럽게 표현되며, 감정적인 분위기도 잘 전달됩니다. 특히 카메라가 인물 주변을 따라 이동한 뒤 기차 내부로 이어지는 전환이 매우 부드럽게 연결되어, 하나의 장면처럼 자연스럽게 이어집니다. 또한 노을빛 조명이 플랫폼과 기차 내부에 일관되게 적용되어 전체적인 색감과 분위기가 잘 유지됩니다. ## 예시 4: Omni Reference 기반 생성 프롬프트: 서울의 밤거리 이미지를 기반으로 장면을 확장, 길거리 포장마차 주변에 사람들이 자연스럽게 있는 장면, 따뜻한 조명, 감성적인 분위기, 한국 드라마 같은 연출, 인물은 실루엣 또는 멀리서 보이는 형태, 부드러운 카메라 움직임 입력: 서울의 밤거리 이미지를 기반으로 장면이 자연스럽게 확장되며, 포장마차와 주변 환경이 실제 도심 골목처럼 잘 표현됩니다. 특히 인물들이 실루엣 형태로 자연스럽게 배치되어 전체적인 풍경과 잘 어우러지며, 거리의 분위기를 해치지 않습니다. 또한 따뜻한 조명과 비에 젖은 도로 위의 반사 효과가 어우러져 감성적인 분위기를 잘 만들어냅니다. ## 시댄스 2.0 가격 시댄스 2.0은 사용한 영상 길이와 해상도에 따라 비용이 책정되는 구조입니다. 현재 기준으로는 초당 약 $0.10부터 시작하며, 해상도가 높아질수록 비용도 함께 증가합니다. 해당 가격은 작성 시점을 기준으로 한 정보이며, 자세한 최신 가격 및 모델별 요금은 공식 API 문서를 참고하는 것이 좋습니다. ## 최종 평가 시댄스 2.0은 다양한 생성 방식과 안정적인 결과 품질을 바탕으로, AI 영상 제작에 있어 충분히 경쟁력 있는 도구입니다. 텍스트 기반 생성부터 프레임 전환, 레퍼런스 기반 생성까지 각각의 기능이 실제 사용에서도 잘 동작하며, 전반적인 완성도도 높은 편입니다. 특히 간단한 프롬프트만으로도 자연스러운 장면과 분위기를 만들어낼 수 있다는 점에서 활용성이 높으며, 생성 모드에 따라 다양한 스타일의 영상을 유연하게 구현할 수 있습니다. 전체적으로 시댄스 2.0은 실사용 기준에서도 안정성과 표현력을 모두 갖춘 모델로, AI 영상 제작을 시작하려는 사용자부터 보다 확장된 활용을 원하는 사용자까지 모두에게 적합한 선택이라고 볼 수 있습니다. 다른 AI 영상 생성 모델과 비교해 선택하고 싶다면 시댄스와 클링 AI 영상 생성 모델 비교 를 참고하면 더 쉽게 판단할 수 있습니다. ## 시댄스 API 사용 방법 앞서 살펴본 예시처럼 시댄스는 다양한 방식으로 영상을 생성할 수 있으며, 이러한 기능은 API를 통해 직접 활용할 수 있습니다. PiAPI 와 같은 플랫폼을 사용하면 시댄스 API 모델을 선택하고, 프롬프트와 레퍼런스를 입력하여 동일한 방식으로 영상을 생성할 수 있습니다. 이를 통해 영상 생성 과정을 자동화하거나, 서비스 내 기능으로 확장하는 것도 가능합니다. Get started here! 에서 바로 시작하거나, 지금 가입하고 워크스페이스에서 시댄스 2.0을 테스트해볼 수 있습니다. 지금 바로 PiAPI에서 시댄스 2.0을 테스트해보세요 . ## Seedream 5 vs Nano Banana 2 (2026): Which AI Model Is Actually Better? Tested Seedream 5 vs Nano Banana 2 across quality, speed, API, and pricing. See real examples, key differences, and which AI model you should use in 2026. If you have been testing generative models and keep running into slow renders or inconsistent outputs, you have probably come across two names: Seedream 5.0 and Nano Banana 2. Both are pushing the limits of image generation in 2026, but they take very different approaches when it comes to speed, consistency, and real-world usability. In this comparison, I tested Seedream 5 vs Nano Banana 2 across output quality, prompt adherence, speed, API integration, and pricing. If you are deciding which model to use, this breakdown will help you figure out which one actually delivers. ## What is Nano Banana 2? Nano Banana 2 (Gemini 3.1 Flash Image) is Google's fast, production-focused image model built for high-volume generation. It produces images in seconds and supports multiple resolutions from 0.5K preview up to 4K, making it suitable for rapid iteration and final outputs. Its key strength is consistency. You can lock reference images with support for up to 5 characters and 14 objects, keeping visuals stable across multiple generations. It is also search-grounded, pulling from Google Search and Images to generate more accurate, real-world visuals instead of hallucinated ones. On top of that, it supports a wide range of aspect ratios, including extreme formats like 1:8 and 8:1, and handles multilingual text and layout more reliably than most models. ## What is Seedream 5? Seedream 5.0 Lite is ByteDance's image generation model focused on reasoning and structured outputs. Its key strength is Chain-of-Thought reasoning. The model plans the scene before generating, making it better at handling complex spatial logic and relationships. It also performs well in cultural accuracy, producing visuals that feel more diverse and less western-biased compared to most models. On the cost side, Seedream 5 is more efficient. It runs about 33% cheaper on PiAPI, making it a strong option for large-scale production. ## Model Similarities and Differences Despite targeting similar workflows, Nano Banana 2 API and Seedream 5 API share a strong foundation but differ in execution. Both support high-resolution outputs, multimodal prompting, and web grounding, but they diverge in how they handle consistency, reasoning, and design. ## Which Should You Choose? Choose Nano Banana 2 if: you need high-volume, consistent outputs where characters or products must remain identical across generations. Choose Seedream 5 if: you are creating complex, design-heavy visuals where layout, hierarchy, and interpretation matter more than speed. Each example below uses the same prompt across both models to compare performance directly. ## Example 1: Product Consistency Test Prompt: A premium product shot of a white sneaker with a minimalist design, placed on a reflective surface, soft studio lighting, clean background, luxury brand aesthetic, high detail Evaluation Both models follow the prompt well, producing a clean product shot with a premium aesthetic. The difference becomes clear in realism and material quality. Nano Banana 2 delivers highly realistic textures, with visible leather grain and natural variation across the shoe. The reflection and shadows are physically accurate, giving the image a true-to-life product photography feel. It also handles branding consistently, maintaining clarity across the design. Seedream 5 produces a more polished and stylized result. The lighting is clean and visually appealing, but textures appear slightly oversmoothed, making the image feel closer to a 3D render. Reflections are brighter but less physically grounded, and detailed branding is less consistent. Overall, Nano Banana 2 stands out for stronger realism and product accuracy, while Seedream 5 leans towards a more aesthetic, ready-to-use visual style. ## Example 2: Layout and Reasoning Test Prompt: A modern restaurant menu poster with a bold headline at the top, featured dish image in the center, price list aligned on the right, and a small logo at the bottom, clean typography, balanced layout, minimal design Evaluation Both models follow the prompt well, producing a structured menu-style layout. The difference becomes clear in alignment precision and handling of text-heavy designs. Nano Banana 2 delivers a highly structured result, with precise alignment across all elements. The headline, central image, and right-aligned price list are placed exactly as instructed, creating a layout that feels production-ready. It also handles a large amount of text with strong legibility and clear hierarchy, making the design look close to a real, printable menu. Seedream 5 produces a cleaner and more minimal layout, but with less strict alignment. Elements feel slightly more floaty, and the overall structure is less rigid. While the typography is still readable, it includes fewer items and minor inconsistencies, prioritizing visual simplicity over information density. Overall, Nano Banana 2 stands out for structured, text-heavy layouts and precise alignment, while Seedream 5 leans towards a more minimal and aesthetic design style. ## Example 3: Real-World Accuracy Test Prompt: A busy street scene in Singapore with MRT signage, modern buildings, pedestrians walking, warm sunset lighting, realistic environment, high detail Evaluation Both models generate a busy urban street scene with strong visual quality. The difference becomes clear in factual accuracy and localization. Nano Banana 2 delivers a highly realistic and location-accurate result. The environment closely resembles actual Singapore streets, with correct MRT signage, recognizable building styles, and properly rendered multilingual text. Details like road signs and infrastructure are consistent with real-world references, making the output feel like an actual photograph. Seedream 5 produces a visually appealing scene, but leans more towards a generic Asian city aesthetic. While the lighting and composition are strong, certain elements lack accuracy, including signage that does not fully match real Singapore MRT design. Multilingual details are also less consistent, reducing overall authenticity. Overall, Nano Banana 2 stands out for real-world accuracy and localization, while Seedream 5 focuses more on visual style and atmosphere. ## Pricing Nano Banana 2 uses a fixed per-image pricing based on resolution: 1. 1K - $0.06 per image 2. 2K - $0.08 per image 3. 4K - $0.12 per image Seedream 5.0 Lite pricing: 1. 2K (default) - $0.052 per image 2. 3K - $0.052 per image Both models offer competitive pricing, with Seedream 5 providing consistent pricing across resolutions. Pricing is accurate at the time of writing. For the latest updates, refer to the Nano Banana 2 API docs and Seedream 5.0 API docs . ## Final Verdict Nano Banana 2 and Seedream 5 are both top-tier models in 2026, but they are built for very different purposes. Nano Banana 2 stands out for speed, precision, and real-world accuracy. It performs consistently across product shots, structured layouts, and location-based scenes, making it the better choice for high-volume production, localization, and tasks where exact control is required. Seedream 5, on the other hand, focuses on reasoning and design. It handles layout, composition, and visual style more naturally, making it a strong option for creative work where interpretation and aesthetics matter more than strict accuracy. In short, Nano Banana 2 is the more reliable, production-ready model, while Seedream 5 is better suited for design-driven and creative use cases. Start testing Nano Banana 2 and Seedream 5.0 via PiAPI today! Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Acestep Audio T2A Review (2026): Production-Ready AI Music or Not? Tested Acestep Audio for AI music generation. See real examples, audio quality, and whether it can produce production-ready tracks in 2026. Most AI music tools can generate something that sounds decent, but very few produce tracks that are actually usable beyond quick demos. Ace-step audio AI is one of the newer entrants aiming to change that. Instead of generating short clips or experimental sounds, it focuses on creating full music tracks from simple prompts with more consistent structure and usable audio quality. In this review, we test Acestep AI across multiple scenarios, evaluating its prompt adherence, sound quality, and overall reliability to determine whether it can truly generate production-ready AI music. ## What is Acestep Audio API Acestep Audio is an AI music generator that creates full tracks from simple text prompts. Instead of producing short loops or rough ideas, it focuses on generating structured music with a clear progression. Users can describe the style, mood, and overall concept, and the model translates that into a complete audio output. This makes it accessible even for those without any music production experience. As part of the broader Acestep AI ecosystem, the tool is designed for speed and ease of use, aiming to deliver results that go beyond experimentation and closer to usable music for real content. ## Key Features Sonic Versatility & Style Control Supports a wide range of genres, from lo-fi and pop to cinematic and rock. Users can easily control the mood and emotion of the track through simple prompt adjustments. Fast Generation & Strong Coherence Generates full tracks quickly while maintaining a consistent structure. Outputs feel more complete, with smoother transitions instead of loop-based stitching. Advanced Editing & Vocal Alignment Allows users to refine specific sections of a track and align lyrics with generated vocals, offering more control compared to basic AI music tools. Accessibility & Commercial Use Can be used locally for better data control, and generated tracks are typically royalty-free, making them suitable for commercial use. ## Prompt & Inputs Acestep Audio relies primarily on text prompts to generate music, making the workflow simple and accessible without requiring any audio or image inputs. A typical prompt includes the genre, mood, tempo, instruments, and optionally lyrics if vocal output is desired. The more specific the prompt, the more structured and accurate the generated track will be. For example: "Upbeat pop song, female vocals, bright and energetic mood, 120 BPM, catchy chorus, clean studio quality" Small adjustments in wording can significantly change the output, especially when defining mood and instrumentation. This makes prompt design an important factor in getting consistent and usable results. For more advanced prompting techniques, you can refer to the Ace-step prompt guides . ## Example 1 - Lo Fi Prompt: Chill lo-fi hip hop beat, soft piano, vinyl crackle, slow tempo around 70 BPM, relaxed and nostalgic mood, instrumental only Output Evaluation Acestep Audio follows the prompt very well, capturing the intended genre, mood, and instrumentation. The soft piano leads the track, while the vinyl crackle adds a subtle nostalgic touch. It also correctly keeps the track instrumental, without adding any unwanted vocals. The audio quality is strong and close to production-ready. The mix feels balanced, with the drums standing out just enough while still maintaining a relaxed lo-fi groove. The track also stays consistent from start to finish, without any noticeable drops in quality or structure. Overall, this result shows that Acestep AI understands both the style and technical elements of lo-fi music, making it a solid option for background use in videos, streams, or podcasts. ## Example 2 - Pop Vocal Track Prompt: Upbeat pop song, female vocals, bright and energetic mood, 120 BPM, catchy chorus, clean studio quality. Lyrics: I've been chasing all these lights, Dancing through the city nights, Heartbeat racing, feeling alive, This is where I come alive. Output Evaluation Acestep Audio performs strongly in this test, especially in handling vocals and lyrics. The model follows the provided lyrics closely, with clear pronunciation and no noticeable skipping or added words. The vocal delivery feels natural, with subtle inflections that match a typical pop style rather than sounding robotic. The overall arrangement aligns well with the prompt, producing a bright and energetic pop track with a clear chorus section. The vocals sit cleanly in the mix without being overpowered by the instrumental, and transitions between sections feel smooth and intentional. Most notably, the timing and alignment of lyrics to the beat are accurate, maintaining clarity even at a faster tempo. This makes the output highly usable for creators who need custom vocal tracks without additional editing. Overall, this result shows that Acestep AI is capable of generating structured, vocal-driven tracks that are close to production-ready quality. ## Example 3 - Cinematic BGM Prompt: Cinematic background score, emotional and dramatic tone, soft piano intro, gradual build with strings and ambient pads, slow tempo around 80 BPM, deep and immersive atmosphere, instrumental only Output Evaluation Acestep Audio performs very well in handling cinematic composition, particularly in terms of structure and progression. The model follows the prompt closely, starting with a soft piano intro before gradually building into a fuller arrangement with strings and ambient layers. The transitions feel smooth and intentional, rather than abrupt or loop-based. The overall atmosphere is strong, with a clear sense of depth created through spatial mixing and layering. The track avoids sounding flat, maintaining a good dynamic range where quieter sections feel intimate before building into more powerful moments. Audio quality remains consistent throughout, with the string elements sounding rich and the overall mix feeling cohesive. Importantly, the track develops over time instead of staying static, which is a common limitation in many AI music generators. Overall, this result shows that Acestep AI is capable of generating emotionally driven, cinematic background music that is suitable for content such as videos, games, or storytelling projects without requiring heavy post-processing. ## Pricing and API Acestep Audio is designed for both creators and developers, with flexible access depending on how the tool is used. For developers, the Ace-step API allows integration into applications and workflows. You can explore the Ace-step API for more details. For better results, you can also refer to the Ace-step prompt guides to improve output consistency and quality. Pricing $0.0005 per second of generated audio Pricing is accurate as of the time of writing. For the latest information, view the Ace-step API documentation . ## Final Verdict Acestep Audio T2A proves to be a strong AI music generator, especially when it comes to structured output and consistency across different styles. From lo-fi beats to vocal pop tracks and cinematic scores, the model demonstrates reliable prompt adherence and produces audio that is clean and usable with minimal post-processing. Where it stands out most is in its ability to maintain coherence across an entire track. Unlike many AI music tools that rely on loop-based generation, Acestep AI delivers more complete compositions with smoother transitions and better overall flow. The vocal generation is also a key strength, with clear pronunciation and accurate lyric alignment, making it a practical option for creators who need custom tracks with specific lyrics. That said, while the outputs are close to production-ready, they may still benefit from light refinement depending on the use case. For quick content creation, background music, or prototyping, the results are more than sufficient. Overall, Acestep Audio T2A is a reliable and efficient tool for generating AI music, particularly for creators looking for structured, prompt-driven outputs without complex workflows. Start testing Acestep Audio T2A and get your Ace-step API access via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Kling AI Avatar: Full Guide with Examples (Standard vs Pro Quality) Learn how Kling AI Avatar works with real examples and a clear Standard vs Pro quality comparison. Discover features, use cases, and how to choose the right output for production. Kling AI Kling avatar API Whether you're experimenting with a talking avatar setup or looking to create production-ready AI videos, this guide will give you a clear understanding of how to use Kling AI avatar effectively. ## What is Kling AI Avatar The Kling AI avatar is a talking avatar model developed by Kling AI that generates realistic human videos from text or audio. Instead of recording a real person, users can create a digital avatar that speaks naturally with synchronized lip movements and consistent facial identity. The model is designed specifically for talking avatar use cases, focusing on facial realism, lip sync accuracy, and stable motion. This makes it suitable for content like marketing videos, AI presenters, and social media clips where clear delivery and natural expression matter. ## Key Features of Kling AI Avatar The Kling AI avatar model focuses on delivering realistic and controllable talking avatars through a combination of multimodal inputs, high-quality video output, and strong lip sync performance. Multimodal Input Support Kling avatar supports text, image, and audio inputs, allowing flexible control over avatar appearance, voice, and behavior. This makes it easier to align identity, speech timing, and overall delivery within a single generation workflow. High-Quality Video Output The model can generate videos up to 1080p at 48 FPS, producing smooth motion and clear visuals. It also supports longer video durations, making it suitable for explainers, presentations, and continuous talking sequences. Advanced Lip Sync and Motion Control One of the standout features is its lip sync accuracy, especially across fast dialogue and multilingual speech. Facial movements are generally well-aligned with audio, with more natural expression seen in higher quality modes. Multilingual Speech Support Kling avatar supports multiple languages including English, Chinese, Japanese, and Korean. This allows creators to generate talking avatar content for different regions without changing the core workflow. Stable Identity and Long-Form Consistency The model maintains character consistency across frames, which is important for longer videos. Compared to typical avatar models, identity drift and facial distortion are better controlled. ## Kling AI Avatar Pricing Kling AI Avatar follows a pay-as-you-go pricing model, where usage is billed based on the duration of generated video. ## Standard vs Pro Pricing Comparison 1. Standard Quality (STD) $0.052 per second Suitable for basic avatar generation with consistent identity and acceptable motion quality. 2. Pro Quality (PRO) $0.104 per second Offers enhanced realism, smoother facial animation, and more accurate lip sync, making it better suited for production use. ## Key Differences in Pricing The Pro quality option is approximately 2x the cost of Standard. This price increase reflects improvements in motion smoothness, facial detail, and overall output stability. For quick testing or bulk generation, Standard is more cost-efficient. However, for content that requires higher realism and stronger viewer engagement, Pro quality justifies the higher cost. ## What You Need to Generate a Kling AI Avatar To generate a Kling AI avatar, you typically need three main inputs: an image, audio, and an optional prompt. Image (Required) The image defines the avatar's identity. This is usually a clear photo of a person, ideally front-facing with good lighting. Higher quality images generally produce more stable and realistic results. Audio (Required) The audio drives the speech and timing of the avatar. The model uses it to generate lip sync and facial movement, so clarity and pacing are important. Clean audio without background noise will result in better output. Prompt (Optional) A prompt can be used to guide the avatar's behavior, tone, or setting. For example, you can specify whether the avatar should sound casual, professional, or expressive. While optional, prompts help improve control over the final output. official Kling AI Avatar user guide ## Example 1 Input Photo Input Audio Pro Output Std Output Evaluation: For this example, the biggest difference between Standard and Pro quality is body movement and overall delivery. The Pro output feels noticeably more dynamic, with natural upper-body motion and hand gestures that match the speech. This makes the avatar look more conversational and engaging. In comparison, the Standard output is much more static, with the hands remaining fixed and the posture feeling stiff, which gives the video a more robotic and less relaxed presentation. Lip sync is reasonably solid in both versions, but the Pro output feels more cohesive because the facial animation is supported by body language. In the Standard version, the mouth movement aligns fairly well with the audio, but the lack of accompanying gestures makes the performance feel more isolated and less natural. As a result, Pro creates a stronger sense of realism even when the core speech animation is similar. One limitation shared by both outputs is text rendering. Any on-screen subtitles or text elements appear distorted and unreadable, which remains a common weakness in AI video generation. Overall, the Pro version is a clear step up for conversational, full-torso avatar videos, while Standard is more suitable for simpler talking-head use cases where motion realism is less important. ## Example 2 Input Photo Audio Input Pro Output Std Output Evaluation: For this example, the main difference lies in body language and delivery. The Pro output feels more natural and engaging, with hand gestures and upper-body movement that match the speech. In contrast, the Standard output is very stiff, with arms fixed at the sides, which creates a disconnect with the more energetic tone of the dialogue. Head movement further highlights this gap. In the Pro version, head and posture shifts align smoothly with gestures, making the delivery feel cohesive. In the Standard version, head movement exists but feels isolated due to the lack of body motion, resulting in a more robotic appearance. Both outputs maintain strong visual consistency, with stable backgrounds and no major artifacts. Overall, the key difference is animation: Pro delivers expressive, full-body movement, while Standard is largely limited to facial and head animation. ## Example 3 Input Photo Input Audio Pro Output Std Output Evaluation: For this example, the gap between Standard and Pro is clear in body movement. The Pro output feels natural and conversational, with the avatar leaning forward and using hand gestures. The Standard output remains stiff, with minimal torso movement, making it less suitable for a podcast-style setting. Facial expression also differs. While lip sync is accurate in both, the Pro version shows more natural expressions and subtle eye movement, while the Standard version appears flatter and less engaging. Both outputs are visually stable, with clean backgrounds and no noticeable artifacts. Overall, Pro is better for expressive, personality-driven content, while Standard is more suited for simple talking-head use. ## Conclusion The Kling AI avatar model is a solid choice for generating talking avatar videos, with Standard quality providing stable results, accurate lip sync, and consistent output for most use cases. However, its main limitation is the lack of expressive body movement, which can make delivery feel more rigid. While Pro quality is designed to improve realism with more dynamic motion and gestures, it may not always be reliably available. For now, Standard remains the more practical option, while Pro represents the next step toward more lifelike avatar performance. Kling AI Avatar PiAPI Sign up ## Kling O1 API Guide: How to Use Kling AI API for Cinematic Video Generation Learn how to use the Kling O1 API for AI video generation. Explore Kling AI API features, pricing, documentation, and prompt examples for cinematic workflows. Kling O1 is a multimodal video generation models by Kuaishou, positioned for high-quality short-form and single-shot video creation. For teams exploring the Kling AI API, Kling O1 is useful for cinematic short-form video generation, visual experimentation, and tighter edit-driven workflows. In this guide, we cover: 1. What is Kling O1? 2. What is Kling API? 3. How to use the Kling O1 API? and what makes the model relevant for production-oriented video generation. ## What is Kling O1? Kling O1 Kling AI API documentation ## Kling O1 API Text-to-Video and Image-to-Video Generation The Kling O1 API supports text-to-video and image-to-video generation, making it suitable for rapid visual ideation and production-ready short clips. Start and End Frame Support Kling O1 supports custom start and end frames for image-to-video generations, which helps with smoother transitions, tighter edits, and more controlled short narrative sequences. Flexible Duration Options Kling Video model allows developers to adapt output length to different workflows. Director-like Memory Kling O1 retains identity of main characters, props and settings for stability amidst dynamic camera movements. Video Editing Capabilities Kling O1 API supports AI video editing capabilities for workflows that require video editing. ## How to Use Kling O1 API Developers searching how to use Kling O1 API can think of the workflow in three steps. Step 1: Get Access to the Kling API Kling API key. Step 2: Choose the Kling O1 Workflow Select the workflow that fits your use case: 1. Text-to-video 2. Image-to-video 3. Start and end frame generation These are the capabilities are available with the Kling O1 API. Step 3: Write a Structured Prompt For best results, your prompt should define: 1. Subject 2. Environment 3. Action 4. Camera Movement 5. Visual Mood Kling O1 is positioned as a cinematic video model, so detailed prompts generally help produce better short-form outputs. ## Kling O1 API Pricing Kling O1 API pricing 1. 720p 5s: $0.39 per clip 2. 720p 10s: $0.78 per clip 3. 1080p 5s: $0.52 per clip 4. 1080p 10s: $1.04 per clip That makes pricing one of the most practical sections for developers comparing Kling video API options. ## Kling O1 Prompt Examples Below are a few examples prompts suitable for Kling O1 AI workflow. We will do T2V generations with Examples 1 and 2, while Examples 3 and 4 are I2V generations. ## Example 1: Cinematic City Shot (T2V) Kling O1 Video Output Prompt: A cinematic tracking shot of a man in a dark trench coat walking through a rainy city street at night, neon signs reflecting on the wet pavement, soft blue and magenta lighting, realistic motion, dramatic atmosphere. ## Example 2: Product (T2V) Kling O1 Video Output Prompt: A premium product video of a smartwatch placed on a reflective surface, dramatic studio lighting, slow camera push-in, subtle shadows, clean commercial framing, ultra-detailed textures. ## Example 3: I2V Scene Image Reference Kling O1 Video Output Prompt: A traveler standing on a snowy ridge at sunrise, camera slowly drifting from a side profile to a front-facing cinematic reveal, warm golden light, mountain winds, highly realistic atmosphere. Use image_1 as the start frame. ## Example 4: Start & End Frame (I2V) Start Frame Reference End Frame Reference Kling O1 Video Output Prompt: Use @image_2 as the starting frame, @image_2 as the end frame. ## Use Cases for Kling O1 Video Model Short-Form Marketing Videos The model is well suited for social clips, marketing visuals, and short campaign assets. PiAPI explicitly mentions social clips and marketing assets among Kling O1's workflow fits. Cinematic Single-Shot Scenes Because Kling O1 is positioned around high-quality short-form and single-shot generation, it works well for moody cinematic scenes and short visual storytelling. ## Final Thoughts on Kling O1 API The Kling O1 API sits in an interesting place within the broader Kling AI API lineup. It is cinematic, short-form focused, and flexible enough to support both generation and editing-oriented tasks. Its support for text-to-video, image-to-video, start and end frame workflows, and multiple editing functions makes it a practical model for teams building short-form video pipelines. Kling O1 API Key via PiAPI today! Sign up ## GPT Image 1.5 API Guide: Features, Pricing, and Prompt Examples What is GPT Image 1.5? Explore features, pricing, and how to use the gpt-image-1.5 API for high-quality AI image generation. The evolution of AI image generation has moved towards models that are not only visually impressive but also highly controllable and production-ready. One of the latest advancements in this space is GPT Image 1.5, OpenAI's advanced image generation model available through the GPT 1.5 Image API. gpt-image-1.5 API In this guide, we explore how GPT 1.5 AI works, its key features, pricing considerations, and how to use it effectively. ## What is GPT Image 1.5? The model is available through the OpenAI gpt-image-1.5 API, allowing developers to generate and edit images using structured prompts. GPT Image 1 1. Better instruction following 2. Improved image editing capabilities 3. Stronger preservation of composition and details 4. Fast generation speeds It supports both T2I and I2I workflows, making it flexible for a wide range of use cases for developers and enterprises. ## GPT Image 1.5 Release Date The gpt-image-1.5 release date was officially announced in December 2025 by OpenAI. This release marked a significant upgrade over earlier image models, introducing improved realism, editing control, and efficiency. ## Key Features of GPT Image 1.5 API Strong Prompt Adherence GPT Image 1.5 is optimized for following instructions closely, allowing developers to generate images that match specific layouts, styles, and constraints. High-Fidelity Image Generation The model produces high-quality visuals with improved lighting, texture, and composition, making it suitable for professional workflows. Advanced Image Editing GPT Image 1.5 supports editing workflows where users can modify existing images while preserving key elements such as structure, lighting, and identity. Faster Generation Speed GPT Image 1.5 delivers faster generation, enabling more efficient iteration and large-scale workflows. Text Rendering and Composition The model handles dense text rendering inside images more accurately, which is important for design, posters, and marketing creatives. Image Resolution The gpt image 1.5 resolution supports high-quality outputs suitable for production use, including detailed compositions and large-format visuals. ## GPT Image 1.5 API Pricing price at PiAPI The OpenAI gpt-image-1.5 API pricing is structured based on: 1. Quality of generation 2. Volume of generation ## How to Use GPT Image 1.5 Developers searching for how to use GPT image 1.5 can follow a simple process. Step 1: Get API Access GPT 1.5 AI API Key Step 2: Follow the API Documentations GPT 1.5 AI API documentations Step 3: Call the API Send a response and wait for the generated images. Typical API usage includes: 1. Text-to-image generation 2. Image editing 3. Batch generation workflows ## GPT Image 1.5 Prompt Examples Clear prompts significantly improve output quality. Below are structured examples. ## Example 1: Cinematic Scene GPT Image 1.5 Output Prompt: A lone astronaut stands at the edge of a collapsed highway overpass on a terraformed Mars, dusk light casting long amber shadows across red desert dunes that have swallowed the road. The visor reflects a distant colony dome glowing faintly on the horizon. Shot on anamorphic lens with subtle lens flare, shallow depth of field. Muted teal and burnt orange color grade reminiscent of Denis Villeneuve's visual style. Fine dust particles suspended in the air catch the last rays of light. ## Example 2: Product Advertisement GPT Image 1.5 Output Prompt: A matte black premium wireless earbud case sits slightly open on a slab of raw dark marble, one earbud hovering just above the case as if magnetically suspended. A single droplet of water rests on the earbud surface to emphasize water resistance. Soft directional studio lighting from the upper left with a clean gradient background shifting from charcoal to warm cream. The lighting produces a sharp specular highlight along the earbud's edge. Minimalist luxury product photography, razor-sharp focus. ## Example 3: Text Rendering Scene GPT Image 1.5 Output Prompt: A weathered wooden sign nailed to a crooked fence post in a misty countryside field. The sign reads "NOWHERE — 0 miles" in hand-painted white serif lettering with visible brushstroke texture and slight paint drips. Morning fog rolls across tall wet grass behind the sign. The wood grain is richly detailed with cracked paint and rusty nail heads. Photorealistic, natural overcast lighting, shallow depth of field with the background softly blurred. ## Use Cases for GPT Image 1.5 API Marketing and Creative Design Generate high-quality visuals for ads, banners, and product campaigns. E-commerce and Product Images Create consistent product visuals across different variations and environments. Content Creation Produce images for blogs, social media, and digital platforms. ## Final Thoughts on GPT Image 1.5 GPT Image 1.5 represents a shift toward production-ready image generation models. With strong prompt adherence, high-quality outputs, and improved editing capabilities, it is well-suited for both creative and commercial workflows. Through the GPT 1.5 image API, developers can build scalable pipelines for generating and editing images with consistency and control. For teams evaluating gpt image 1.5 API, the model offers a balance between quality, speed, and cost efficiency, making it a strong choice for modern AI-powered visual generation. GPT Image 1.5 API Key via PiAPI today! Sign up ## Hailuo vs Kling 2.6: Speed or Realism Which AI Video Model Actually Wins? Compare Hailuo vs Kling 2.6 for AI video generation. We test motion realism, prompt adherence, and output consistency to see which model actually performs better in real-world use. Hailuo AI Kling 2.6 While both models aim to generate high-quality video, differences in output behavior are not always obvious from documentation alone. A direct comparison using the same prompts provides a clearer view of how each model performs under real conditions. In this guide, both Hailuo AI and Kling 2.6 are tested across multiple scenarios using identical prompts. The evaluation focuses on prompt adherence, motion realism, temporal consistency, and visual artifacts. By the end, you'll have a clearer understanding of how both models perform and which one is better suited for your specific use case. ## What is Hailuo AI? Hailuo AI is a generative video model that supports multiple creation workflows, including text-to-video and image-to-video generation. It is designed to produce short video clips from structured inputs, allowing users to define elements such as subject, environment, motion, and camera behavior within a prompt. Beyond basic generation, Hailuo AI provides a level of control over how scenes are constructed, making it possible to produce more consistent outputs across different runs. This is especially useful when testing prompt variations or generating batches of similar content. Hailuo AI API ## What is Kling 2.6? Kling 2.6 is a generative video model that supports multiple workflows, including text-to-video and image-to-video generation. It is designed to generate short video clips from structured inputs, where users define elements such as subject, environment, motion, and camera behavior within a prompt. Similar to Hailuo AI, Kling 2.6 relies on prompt-based inputs to produce video outputs. This allows users to experiment with different prompt structures and scene setups when generating content using Kling video 2.6 AI. Kling 2.6 API ## Model Similarities and Differences Despite targeting similar workflows, Hailuo AI and Kling 2.6 share a core foundation but diverge in execution. Both generate video from structured prompts, support text/image-to-video, and offer API integration. The key differences lie in how each model handles realism, motion, and generation behavior. ## Video Quality Both generate highly detailed outputs. Kling 2.6 excels at cinematic realism, dramatic lighting, and native audio synchronization. Hailuo AI focuses on physics-first realism, delivering highly believable interactions (like water or fabric), though its overall look can be slightly less polished. ## Motion Performance Both support complex motion and camera control. Kling 2.6 prioritizes director-style camera dynamics, offering sweeping pans and tracking shots. Hailuo AI excels at subject motion, rendering fast, anatomically correct human movements and physical actions without breaking. ## Prompt Adherence Both follow structured inputs well. Hailuo AI faithfully interprets physical instructions but may animate unnecessary lip movements. Kling 2.6 offers stronger control over scene pacing, emotional tone, and features highly effective negative prompting. ## Generation Stability Both aim for consistent outputs. Hailuo AI boasts exceptional temporal consistency, rarely suffering from character morphing or limb warping during high action. Kling 2.6 maintains strong environmental coherence, though long clips can occasionally introduce minor physics errors. ## Speed and Efficiency Both support API workflows for repeated generation. Hailuo AI is generally faster, making it ideal for rapid prototyping and quick turnarounds. Kling 2.6 often requires longer rendering times due to its intricate lighting and integrated audio processing. ## Pricing The pricing for both model is as follows: Kling Pricelist Hailuo Pricelist All pricing information is accurate at the time of writing and is subject to change based on the latest API updates. Hailuo API Docs and Kling API Docs , or visit our pricing pages . ## Evaluation: How We Compare Hailuo AI vs Kling 2.6 For this comparison, we evaluate both Hailuo AI and Kling 2.6 across multiple scenarios using identical prompts. This ensures that differences in output are driven by model behavior rather than prompt variation. The evaluation framework is adapted from a Labelbox-style assessment and focuses on four key dimensions: 1. Prompt adherence 2. Motion realism 3. Temporal consistency 4. Artifacts Each example is designed to test a specific aspect of video generation, including scene composition, human motion, multi-subject interaction, and environmental detail. All outputs are generated under comparable conditions to maintain consistency across the evaluation. ## Example 1: Fluid Dynamics & Environmental Logic Prompt A macro, slow-motion shot of dark roasted espresso dripping into a ceramic cup at a modern catering event setup in Singapore. Steam gently rises from the surface of the hot coffee, curling and interacting with a warm, overhead spotlight. 16:9 aspect ratio. Silent. Hailuo Output Kling Output ## Evaluation Both Hailuo AI and Kling 2.6 interpret the scene differently. Kling 2.6 captures the lighting and attempts to reflect the catering environment, but misses the macro perspective and introduces a structural inconsistency where the espresso appears to originate from an unrealistic source. Hailuo AI accurately delivers the macro, slow-motion style with a cleaner composition, though it simplifies the background and reduces environmental detail. In terms of motion, Hailuo AI produces more physically consistent liquid behavior, with natural droplet formation and subtle steam movement. Kling 2.6 generates more dramatic steam effects, but the liquid flow appears less stable. Overall, Hailuo AI delivers a more realistic and stable result in this scenario, while Kling 2.6 follows more of the environmental cues but with noticeable inconsistencies. ## Example 2: Human Interaction & Gesture Prompt Two colleagues sitting across each other in a modern office meeting room, having a discussion. One person is speaking and gesturing naturally with their hands, while the other listens and nods. Soft indoor lighting, shallow depth of field, camera slowly pans from left to right. 16:9 aspect ratio. Hailuo Output Kling Output ## Evaluation Both Hailuo AI and Kling 2.6 miss the "sitting across" instruction, placing both subjects on the same side. Kling 2.6 follows the camera movement accurately, while Hailuo AI better captures shallow depth of field but keeps a mostly static frame. Hailuo AI shows more natural body movement, but introduces a major artifact where the hand loses structure during gestures. Kling 2.6 appears more rigid, but maintains strong consistency with only minor issues. Overall, Kling 2.6 delivers a more stable and usable result, while Hailuo AI offers more fluid motion but with noticeable artifacts. ## Example 3: Fast Motion & Camera Dynamics Prompt An aggressive FPV drone shot following a bright orange sports car drifting around a sharp curve on a winding mountain road. Dense pine forests surround the road. Golden hour lighting casts long shadows. Thick white smoke billows from the rear tires as the car slides. The camera banks and tilts dynamically to follow the car's movement. 16:9 aspect ratio. Hailuo Output Kling Output ## Evaluation Both Hailuo AI and Kling 2.6 handle the scene differently. Kling 2.6 captures the aggressive camera movement and drifting action, but misses the car color and shows instability as the car's shape and color shift during motion. Hailuo AI follows the scene setup more closely, maintaining consistent car structure and color, but simplifies the action with a more static camera and less pronounced drift. In terms of motion, Kling 2.6 delivers more dynamic camera movement and stronger visual impact, though the car's behavior becomes less physically consistent toward the end. Hailuo AI produces more grounded and stable motion, but lacks the intensity described in the scene. Overall, Kling 2.6 produces a more cinematic result, while Hailuo AI delivers a cleaner and more consistent output. ## Example 4: Fine Detail & Micro Motion Prompt A close-up shot of a hand slowly brushing through tall green grass in a windy field during sunset. Individual blades of grass move naturally in the wind, with soft golden hour lighting and shallow depth of field. The camera remains steady, focusing on fine detail and subtle motion. 16:9 aspect ratio. Hailuo Output Kling Output ## Evaluation Both Hailuo AI and Kling 2.6 handle the scene well, but with different results. Hailuo AI closely follows the scene, capturing tall green grass, strong golden hour lighting, and a clean shallow depth of field. Kling 2.6 follows the setup but generates foliage that resembles wheat rather than grass. In terms of motion, Hailuo AI shows more realistic interaction, with grass bending and parting naturally around the hand. Kling 2.6 produces a highly detailed hand, but the interaction feels less physical, with fingers appearing to pass through the plants. Both models maintain strong consistency throughout, with stable structure in both the hand and environment. Hailuo AI remains clean overall, while Kling 2.6 shows minor interaction artifacts. Overall, Hailuo AI delivers a more physically grounded and accurate result in this scenario, while Kling 2.6 emphasizes visual detail but with less realistic interaction. ## Example 5: Multi-Subject Interaction & Scene Coherence Prompt A group of friends sitting around a campfire at night, one playing guitar while others roast marshmallows. Warm firelight illuminates their faces, sparks rise into the air, and the camera slowly circles the group. Natural interaction between characters, cinematic atmosphere, shallow depth of field. 16:9 aspect ratio. Hailuo Output Kling Output ## Evaluation Both Hailuo AI and Kling 2.6 follow the scene well, generating multiple subjects, firelight, and camera movement. Hailuo AI produces a larger group, while Kling 2.6 opts for a smaller, more focused composition. In terms of motion, both models capture natural interaction and ambient movement. Hailuo AI shows slightly more active group dynamics, while Kling 2.6 delivers a more controlled, cinematic feel. Both maintain strong temporal consistency, with stable subjects and environments throughout. However, both struggle with object interaction. Hailuo AI introduces issues with marshmallow sticks bending and morphing, while Kling 2.6 produces a more severe structural artifact where a marshmallow stick appears merged with the guitar. Overall, both outputs show limitations in handling complex object interactions, but Hailuo AI remains slightly more stable, while Kling 2.6 introduces more disruptive structural inconsistencies. ## Conclusion Hailuo AI and Kling 2.6 both demonstrate strong capabilities in AI video generation, but the differences become clear when tested across real scenarios. Hailuo AI consistently produces more stable and physically coherent outputs, especially in areas like fluid motion, fine detail interaction, and overall structural consistency. It tends to handle objects and environments more reliably, making the results cleaner and more usable across different use cases. Kling 2.6, on the other hand, delivers more dynamic and visually engaging outputs, particularly in scenes involving camera movement and cinematic composition. However, this often comes with trade-offs in consistency and occasional structural artifacts in more complex scenarios. Overall, Hailuo AI is better suited for workflows that prioritize stability and consistency, while Kling 2.6 is a stronger choice for scenarios that benefit from more cinematic motion and visual impact. Hailuo AI and Kling 2.6 API keys via PiAPI today! Sign up ## Omnihuman 1.5 API Guide: How to Use ByteDance’s AI Human Video Model Learn how to use the Omnihuman 1.5 API to generate realistic AI human videos. This guide covers key features, API workflow, and how to integrate ByteDance’s Omnihuman into production. Omnihuman 1.5 Omnihuman 1.5 API Omnihuman API ## What is Omnihuman 1.5 Omnihuman 1.5 is an AI model developed by ByteDance that focuses on generating realistic human videos from inputs such as text, images, or audio. The model is built to simulate natural facial expressions, body movement, and speech, making it suitable for creating talking avatars, presenters, and human-centric video content. Compared to more general video models, Omnihuman 1.5 places a stronger emphasis on human realism, particularly in lip-sync accuracy and expression consistency. Omnihuman API ## Omnihuman 1.5 API Guide Omnihuman 1.5 API Using the Omnihuman 1.5 API follows a simple structured flow: 1. Provide a reference image for the character 2. Upload an audio file for speech or singing 3. Define a prompt describing the scene and behavior 4. Send the request to the API 5. Retrieve the generated video output Because all three inputs are required, the quality of the result depends on how well they align. A clear prompt, suitable audio, and a consistent reference image will produce more realistic outputs. ## Key Features of Omnihuman 1.5 Omnihuman 1.5 focuses on realistic human video generation by combining image, audio, and prompt inputs. The model is designed to produce natural-looking motion and consistent human behavior across generated videos. ## Multi-Input Generation (Image + Audio + Prompt) The model requires a reference image, audio, and prompt, allowing better control over character identity, voice, and scene behavior. This structured approach improves consistency compared to prompt-only generation. ## Realistic Facial Expressions Omnihuman 1.5 generates detailed facial movements that align with speech and emotion, making outputs feel more lifelike. ## Accurate Lip-Sync Alignment By using audio as a core input, the model is able to synchronize mouth movements closely with speech or singing. ## Natural Body Movement The model produces subtle gestures and body motion that match the tone and pacing of the audio input. ## Consistent Character Identity Using a reference image ensures that the generated human remains visually consistent across the video. ## Omnihuman 1.5 Pricing Omnihuman 1.5 API $0.13 per second (based on input audio length) This means the total cost scales directly with the length of the generated video. For example, longer speech or music inputs will result in higher generation costs, while shorter clips remain relatively affordable. PiAPI Omnihuman API documentation . ## Example 1: DJ Performance (Music + Rhythm) Prompt: A male DJ performing live on stage, wearing headphones and mixing music on a DJ controller, focused expression, subtle head movement following the beat, natural hand interaction with the turntables, soft club lighting with slight shadows, realistic facial expressions, cinematic style, smooth and rhythmic body motion, accurate lip-sync aligned with the music Input Photo ## Evaluation: Using the track "Lose My Mind" by Don Toliver, the output shows strong alignment between movement and rhythm, with natural body motion that follows the music well. Lip-sync accuracy is generally solid at around 85%, with most facial movements matching the audio convincingly. Hand interactions appear stable and realistic throughout the performance. Minor visual artifacts may still occur, such as elements from the source image appearing incorrectly in-frame, but overall the result remains clean and engaging for music-driven content. ## Example 2: Podcast Style (Motivational Speech) Prompt: A young man speaking in a podcast setup, sitting in front of a microphone, calm and confident tone, delivering a motivational message, natural facial expressions, slight head nods and subtle hand gestures, warm indoor lighting, relaxed studio environment, realistic and conversational style Image Input ## Evaluation: Using a motivational podcast-style audio, the output shows strong character consistency and stable facial animation throughout the sequence. Subtle jaw movement and eye expressions align well with the tone of the speech, making the delivery feel natural and engaging. Gestures are synchronized with the audio and remain controlled, adding to the overall realism. Minor issues such as slight blurring during hand movement near objects may occur, but overall the output remains visually stable and well-suited for conversational and narration-based content. ## Conclusion Omnihuman 1.5 demonstrates strong capability in generating realistic AI human videos, particularly through its structured use of image, audio, and prompt inputs. The examples show that the model performs well across both dynamic scenarios, such as music-driven content, and more controlled use cases like conversational or podcast-style videos. The requirement for combined inputs allows for better control over character consistency, speech alignment, and overall realism. However, output quality still depends on how well these inputs are prepared, with minor artifacts or inconsistencies appearing in more complex situations. Overall, the Omnihuman 1.5 API is well-suited for scalable video generation workflows, especially for applications such as content creation, marketing, and digital media. Teams that focus on clear input structure and use case alignment will be able to achieve more reliable and production-ready results. Omnihuman 1.5 PiAPI today! Sign up ## DiffRhythm AI Guide: Music Generation API, Features, and Prompt Examples Learn how DiffRhythm AI works for music generation. Explore features, prompt examples, and how to use DiffRhythm for structured audio creation. The development of generative AI has expanded beyond images and videos into the domain of music creation. One of such models in this space is DiffRhythm, an AI model designed to generate music compositions from prompts with duration flexibility. Unlike traditional music generation tools that rely on predefined loops or templates, DiffRhythm AI focuses on generating rhythm, melody, and structure through latent diffusion modeling. This enables more flexible and expressive music generation across different styles and use cases. DiffRhythm API ## What is DiffRhythm? DiffRhythm The model can generate music based on: 1. Text prompts 2. Style selections 3. Structural cues By modeling rhythm explicitly, DiffRhythm AI allows for more controlled generation of music sequences compared to earlier generative approaches. ## Key Features: DiffRhythm AI API End-to-End Full-Length Music DiffRhythm API allows developers to generate complete songs up to 4 minutes 45 seconds in a single step without stitching short clips or multi-stage workflows. Diffusion-Based Music Modeling The model uses diffusion techniques to generate audio progressively, allowing more controlled and stable music outputs. Style and Scene-Driven Creation Users can guide generation using style prompts such as genre, mood, and tempo to shape unique compositions. Pure Vocal Generation DiffRhythm AI API supports pure vocal generation with standalone vocal tracks, ideal for refining lyrics or acapella projects. Multilingual Music Users can seamlessly generate songs in English or Chinese, with natural-sounding vocal phrasing in both languages. ## How DiffRhythm Works The DiffRhythm workflow typically follows three steps: Step 1: Define the Prompt Users specify the payload, including: 1. Lyrics 2. Timeframe 3. Style 4. Reference audio Step 2: Generate the Music The model processes the prompt and generates music using diffusion-based audio synthesis, ensuring rhythm consistency. Step 3: Output and Integration The generated audio can be exported or integrated into workflows such as: 1. Video background music 2. Game audio 3. Content creation pipelines ## DiffRhythm Prompt Examples DiffRhythm API Docs ## Example 1: Lo-fi Chill Track For this example, we will do music generation for a Lofi track. DiffRhythm Output Prompt: [00:00.00]Soft piano intro with ambient pads [00:04.34]Tell me that I'm special [00:06.57]Tell me I look pretty [00:08.46]Tell me I'm a little angel [00:10.58]Sweetheart of your city [00:13.64]Say what I'm dying to hear [00:17.35]Cause I'm dying to hear you [00:20.86]Tell me I'm that new thing [00:22.93]Tell me that I'm relevant [00:24.96]Tell me that I got a big heart [00:27.04]Then back it up with evidence [00:29.94]I need it and I don't know why [00:34.28]This late at night [00:36.32]Isn't it lonely [00:39.24]I'd do anything to make you want me [00:43.40]I'd give it all up if you told me [00:47.42]That I'd be [00:49.43]The number one girl in your eyes [00:52.85]Your one and only [00:55.74]So what's it gon' take for you to want me [00:59.78]I'd give it all up if you told me [01:03.89]That I'd be [01:05.94]The number one girl in your eyes [01:11.34]Tell me I'm going real big places [01:14.32]Down to earth so friendly [01:16.30]And even through all the phases [01:18.46]Tell me you accept me [01:21.56]Well that's all I'm dying to hear [01:25.30]Yeah I'm dying to hear you [01:28.91]Tell me that you need me [01:30.85]Tell me that I'm loved [01:32.90]Tell me that I'm worth it Style: Lofi ## Example 2: Pop Ballad For this example, we will do music generation for a pop ballad. DiffRhythm Output Prompt: [00:00.00]Where have you gone? [00:05.00]Tell me that I'm enough for you [00:08.20]Even when I feel unsure [00:11.50]Hold me closer, don't let go [00:15.00]I just need to feel secure [00:20.00]Strings begin to rise gently [00:24.00]Now I'm standing in the spotlight [00:27.50]Hoping that you'll see me clear [00:31.00]Chorus builds with stronger vocals [00:35.00]Tell me I'm the one you need Style: Pop ## Example 3: Chinese EDM For this example, we will do music generation for a chinese EDM for you EDM fans out there. DiffRhythm Output Prompt: [00:00.00]电子合成器渐入，氛围铺垫 [00:04.00]节奏渐强，低频鼓点推进 [00:08.00]夜晚灯光闪烁，心跳跟着节拍 [00:11.50]城市节奏加快，感觉越来越快 [00:15.00]情绪堆叠，准备进入高潮 [00:18.50]重低音爆发，节奏全面释放 [00:22.00]跟着音乐摇摆，不再停下来 [00:25.50]双手举起，让节奏带你飞 [00:30.00]旋律持续推进，层层叠加能量 Style: EDM ## Use Cases for DiffRhythm Content Creation Generate background music for videos, social media, and digital content. Game Development Create adaptive music tracks for game environments and interactive experiences. Film and Media Produce soundtracks and mood-based compositions for visual storytelling. Rapid Prototyping Quickly generate music concepts without manual composition. ## Final Thoughts on DiffRhythm AI DiffRhythm represents a shift toward more structured and controllable AI music generation. By focusing on rhythm and timing, the model enables more consistent and musically coherent outputs. With its diffusion-based approach and flexible prompt system, DiffRhythm AI can support a wide range of creative workflows, from simple background tracks to more complex compositions. As AI-generated audio continues to evolve, models like DiffRhythm are likely to play an important role in enabling scalable and accessible music creation. Start testing DiffRhythm API Key and get your API access via PiAPI today! Sign up ## Hunyuan AI vs Seedance 2.0: Which Image-to-Video Model Is Better in 2026? Compare Hunyuan AI vs Seedance 2.0 for image-to-video generation. Explore differences in motion realism, consistency, and artifacts to find the best model for production use. Image-to-video generation is quickly becoming one of the most practical areas of generative AI. Instead of generating an entire scene from text, these models take a reference image and bring it to life with motion, making them especially useful for ads, character animation, and short-form content. Hunyuan AI Seedance 2.0 In this comparison, we evaluate Hunyuan AI vs Seedance 2.0 across key criteria including prompt adherence, motion realism, temporal consistency, and artifacts. The goal is to determine which model is better suited for reliable image-to-video generation in 2026. ## What is Hunyuan? Hunyuan AI is part of Tencent's AI ecosystem and supports image-to-video generation by turning a reference image into an animated sequence. The model focuses on producing more dynamic motion, including camera movement, subject animation, and lighting changes. One of its main strengths is visual intensity. Compared to other more controlled models, Hunyuan tends to give out a more cinematic and expressive output. Especially with well written/structured Hunyuan video prompts. Hunyuan API ## What Is Seedance 2.0? Seedance 2.0 is a video generation model from ByteDance's Dreamina ecosystem that also supports image-to-video workflows. It focuses on animating reference images with smoother motion and more controlled scene development. The main strength of Seedance 2.0 is consistency. Compared to more dynamic models, it preserves structure better across frames, resulting in cleaner and more stable outputs. This makes it well suited for production use cases where reliability matters more than aggressive motion. Seedance API ## Prompting Prompt design plays an important role in image-to-video generation, especially when controlling motion, camera behavior, and scene consistency. While both models respond to structured prompts, their behavior can differ depending on how motion and detail are described. Hunyuan generally benefits from prompts that clearly define movement, camera direction, and environmental changes to guide its more dynamic generation style. Seedance 2.0, on the other hand, performs better with more controlled and structured prompts that emphasize stability and gradual motion. For more detailed guidance, you may refer to: best prompt practices for hunyuan best prompt practices for seedance ## Pricing Both Hunyuan AI and Seedance 2.0 are accessible through API-based pricing models, where costs vary depending on the generation type, resolution, and processing steps. Seedance 2.0 offers multiple generation options, including standard text-to-video, faster variants, and image-to-video modes such as concat and replace. This gives users flexibility to balance speed, quality, and cost depending on their workflow. Seedance 2.0 price table is attached as follows Hunyuan API pricing follows a similar usage-based structure, with costs tied to video generation settings and output complexity. In practice, pricing for both models is competitive, making them viable for scalable image-to-video production depending on performance needs. Hunyuan pricing is attached as follows For more details you may refer to the following: 1. Hunyuan API Docs 2. Seedance API Docs ## Evaluation Framework Both models are evaluated using the same input and prompt to ensure a fair comparison. We focus on four key areas: prompt adherence, motion realism, temporal consistency, and artifacts. This helps highlight not just visual quality, but how reliable each model is for real image-to-video workflows. ## Example Comparisons To evaluate Hunyuan AI vs Seedance 2.0 in real image-to-video scenarios, we tested both models using the same input image and prompt for each case. These examples focus on common use cases such as portrait animation, product visualization, and cinematic scene motion. ## Example 1: Portrait Animation Prompt: A close-up portrait of a young woman, soft natural lighting, she slowly turns her head and blinks, subtle facial expression change, cinematic depth of field Hunyuan Output Seedance Output ## Evaluation Both models follow the prompt well, producing a close-up portrait with head movement and blinking. The difference shows in realism. Hunyuan delivers smooth motion and strong consistency, but the lighting feels more artificial and the skin appears overly smoothed, giving a slightly synthetic look. Seedance 2.0 produces a more natural result, with realistic lighting, subtle facial expressions, and better skin texture. Overall, Seedance 2.0 stands out for stronger photorealism. ## Example 2: Product Visualization Prompt: A premium mechanical wristwatch on a dark reflective surface, soft studio lighting, the camera slowly rotates around the watch, metallic reflections shift naturally, shallow depth of field Hunyuan Output Seedance Output ## Evaluation: Both models generate a premium watch with correct setup and camera movement, but differ in stability. Hunyuan starts strong but struggles during motion, with watch details warping and losing structure as the camera rotates. Seedance 2.0 remains stable throughout, keeping fine details intact while maintaining realistic reflections. Overall, Seedance 2.0 performs better for product-focused use cases. ## Example 3: Complex Scene Motion Prompt: A busy night street market in Tokyo, neon signs glowing, light rain falling, puddles reflecting colorful lights, people walking through the scene, camera slowly pushes forward, cinematic depth of field Hunyuan Output Seedance Output ## Evaluation Both models capture the overall night market scene with neon lighting, rain, and crowd movement, but differ significantly in realism and stability. Hunyuan captures the general atmosphere but struggles with accuracy and motion. Pedestrians appear to slide rather than walk naturally, and as the camera moves forward, faces and background elements begin to warp and lose structure. Seedance 2.0 delivers a much more realistic result. The environment feels coherent, with accurate signage, stable geometry, and natural pedestrian movement. Reflections and lighting behave consistently, even with multiple moving elements in the scene. Overall, Seedance 2.0 handles complex scenes far better, maintaining stability and realism under more challenging conditions. ## Conclusion Hunyuan AI and Seedance 2.0 both deliver capable image-to-video generation, but they perform differently depending on the use case. Hunyuan stands out for generating more dynamic and visually expressive motion, making it suitable for creative or cinematic outputs. However, this often comes with tradeoffs in consistency and structural stability, especially in more complex scenes. Seedance 2.0, on the other hand, is more reliable across all test cases. It consistently produces stable, realistic outputs with better lighting, cleaner motion, and fewer artifacts. This makes it a stronger choice for production workflows where quality and predictability matter. Overall, while Hunyuan shows strong potential, Seedance 2.0 is the better option for most real-world image-to-video applications in 2026. Seedance 2.0 and Hunyuan API keys via PiAPI today! Sign up ## Using Veo 3.1 in 2026: Complete Guide to API, Pricing, and Prompting Learn how to use Veo 3.1 API with pricing, fast mode, and prompting guide. Generate high-quality text-to-video and image-to-video with Google Veo 3.1. In 2026, video generation has become one of the most competitive areas in generative AI. Models are no longer judged purely on visual quality, but on how well they integrate into real workflows, how controllable they are, and how efficiently they can be deployed at scale. Veo 3.1 stands out as one of the most advanced models in this space. Often referred to as Google Veo 3.1, it builds on previous iterations by improving motion consistency, multi-shot control, and prompt adherence, making it highly usable for both developers and creators. This Veo 3.1 guide covers how the model works, how to use the Veo 3.1 API, pricing considerations, how to write effective prompts and examples. ## What is Google Veo 3.1? Google Veo 3.1 One of the key considerations in 2026 is speed. Veo 3.1 also features a Veo 3.1 Fast variants which is designed for rapid iteration, allowing users to generate outputs quickly for testing and prototyping. Fast mode is useful when experimenting with prompts or building workflows that require multiple iterations. However, there may be trade-offs in quality compared to standard generation modes, which prioritize visual fidelity and consistency. Choosing between Veo 3.1 fast and standard modes depends on your use case. For production-ready assets, standard generation is typically preferred, while fast mode is ideal for exploration and prompt tuning. ## How to Use the Veo 3.1 API Veo 3.1 API Veo 3.1 API key A typical workflow using the Veo 3.1 API looks like this: Veo 3.1 API documentation here ! The API supports both text-to-video and image-to-video workflows, allowing developers to either generate scenes from scratch or extend existing visuals. ## Veo 3.1 API Pricing Understanding Veo 3.1 price is critical for scaling usage. Pricing is generally based on factors such as: 1. Duration of the generated video 2. Include audio generation 3. Generation mode (fast vs standard) Costs are calculated per second of generated video. With PiAPI, the duration of the output video can be 4, 6 or 8 seconds. When planning usage, it is important to balance iteration and cost. Using Veo 3.1 fast for experimentation and reserving full-quality runs for final outputs is a common strategy. ## Veo 3.1 Prompting Guide Google [Cinematography] + [Subject] + [Action] + [Context] + [Style & Ambiance] Example: A slow cinematic tracking shot from behind, a young man walking through a neon-lit street in a crowded Tokyo nightlife district with reflections on wet pavement, moody cyberpunk lighting with soft glow and high realism. Cinematography defines how the scene is captured. This includes camera angle, movement, framing, and shot type. Examples include slow tracking shots, close-ups, aerial views, or wide-angle cinematic shots. Subject identifies the main focus of the scene. This could be a person, object, or environment that drives the visual narrative. Action describes what the subject is doing over time. Since Veo 3.1 generates motion, this is critical for temporal consistency. Context provides the surrounding environment and background details. This helps anchor the scene and improves realism. Style and Ambiance define the overall aesthetic, including lighting, mood, color grading, and visual tone. ## Veo 3.1 Prompt Examples Veo 3.1 API Docs . Examples 1 and 2, we will run T2V generations and Example 3, I2V generation task for both variants, Veo 3.1 and Veo 3.1 Fast. ## Example 1: Cinematic Scene Veo 3.1 Output Veo 3.1 Fast Output Prompt: A handheld shaky close-up shot a professional boxer throwing rapid punches and dodging attacks inside a dimly lit underground boxing gym with sweat and dust in the air gritty cinematic style with high contrast lighting and intense atmosphere. ## Example 2: Cinematic Scene Veo 3.1 Output Veo 3.1 Fast Output Prompt: A smooth aerial drone shot with slow forward motion, a tropical island coastline, waves crashing against cliffs, surrounded by lush greenery and turquoise water under a clear sky, vibrant colors with bright natural sunlight and serene ambiance. ## Example 3: Product Scene Veo 3.1 Reference Image Veo 3.1 Output Veo 3.1 Fast Output Prompt: A slow cinematic close-up shot with gentle camera orbit, a professional DSLR camera with a large lens, rotating slightly as the focus ring turns subtly and light glides across the lens glass, placed on a wooden surface in a softly lit indoor environment with natural side lighting, warm natural aesthetic with soft shadows, detailed textures, and high-end commercial realism. ## Conclusion The team concludes that Veo 3.1 continues to stand out in 2026 as a practical and controllable video generation model, capable of producing consistent, high-quality outputs across cinematic, product, and narrative use cases. With a strong API and structured prompting approach, it fits well into real production workflows. The Veo 3.1 fast variant adds important flexibility, enabling rapid iteration and prompt testing with surprisingly solid results. While standard mode remains better for final-quality outputs, fast mode is often good enough for early-stage content and significantly reduces time and cost. In practice, combining both modes allows you to move quickly without sacrificing quality, making Veo 3.1 a scalable solution for modern video generation workflows. Veo 3.1 API Key via PiAPI today! PiAPI Sign up ## Wan 2.6 vs Kling 2.6: Which AI Video Model Is Better for Production in 2026? Compare Wan 2.6 vs Kling 2.6 for AI video generation. See differences in realism, motion consistency, prompt adherence, and which model is better for production workflows in 2026. Wan 2.6 and Kling 2.6 Wan 2.6 API and Kling 2.6 API ## What is Wan 2.6 Alibaba Wan 2.6 is an AI video generation model designed for structured and consistent output. It focuses on controllability, allowing users to guide scenes more precisely through prompts. Compared to more cinematic-focused models, Wan 2.6 is built for stability and repeatability. This makes it useful for workflows where consistent results matter, especially when using the Wan 2.6 API in production environments. Wan 2.6 also offers accessible testing options, including Wan 2.6 free access, making it easier for developers and creators to evaluate before scaling. ## What is Kling 2.6 Kuaishou Kling 2.6 is an AI video generation model focused on realism and motion quality. It is designed to produce more cinematic outputs, with smoother transitions and more natural movement compared to earlier versions. Kling Video 2.6 stands out in how it handles complex scenes and dynamic motion. This makes it strong for use cases like storytelling, marketing visuals, and content creation where visual impact matters more than strict control. The Kling 2.6 API is built for scalable video generation, but factors like Kling 2.6 price and resource usage can become important when moving into production. ## Model Similarities and Differences Despite targeting slightly different use cases, Wan 2.6 and Kling 2.6 share the same core foundation. Both models generate videos from text prompts, support structured prompting, and can be integrated through the Wan 2.6 API and Kling 2.6 API for scalable workflows. The key differences come from how each model is positioned and optimized. ## Video Quality Kling 2.6 is often positioned around cinematic visuals, with a focus on realism, lighting, and overall polish. Wan 2.6 is generally associated with more consistent outputs, though visual style may vary depending on the prompt and generation. ## Motion Performance Kling 2.6 emphasizes smooth and dynamic motion, particularly in more complex scenes. Wan 2.6 tends to prioritize controlled and stable motion, which may result in more predictable outputs. ## Prompt Adherence Wan 2.6 is designed to handle structured prompts reliably, especially when multiple elements are involved. Kling 2.6 also performs well with prompts, though results can vary depending on scene complexity. ## Generation Stability Wan 2.6 is generally positioned as a more stable model with consistent outputs. Kling 2.6 can produce highly detailed results, but stability may vary in more complex generations. ## Speed and Efficiency Wan 2.6 is typically more efficient for repeated or large-scale generation. Kling 2.6 focuses more on output quality, which may come with higher resource usage. ## Pricing Comparison Pricing is an important factor when choosing between Wan 2.6 and Kling 2.6, especially for production use where costs scale quickly with usage. Pricing for Wan 2.6 is as follows: Pricing for Kling 2.6 is as follows: Kling 2.6 API Docs and Wan 2.6 API Docs . ## Evaluation Method Labelbox-style assessment . Each generated video is assessed across: 1. Prompt adherence 2. Video quality 3. Motion consistency 4. Visual artifacts All examples use the same prompts for both Wan 2.6 and Kling 2.6 to isolate model performance. ## Example 1: Simple Scene Prompt: A calm beach at sunset with gentle waves slowly rolling toward the shore, soft golden hour lighting casting warm tones across the sand and water, clear sky with light scattered clouds, subtle reflections on the wet sand, no people, wide cinematic shot, smooth and natural motion, high detail, realistic lighting Wan 2.6 Output Kling 2.6 Output ## Example 1 Evaluation Both Wan 2.6 and Kling 2.6 adhere well to the prompt, delivering a serene beach scene with accurate golden hour lighting and clean compositions. Wan 2.6 excels in visual stability and color clarity. Reflections on the wet sand are well-defined and consistent across frames. However, the wave motion feels slightly artificial, resembling a timelapse or "living photo" rather than natural, real-time movement. While the output is clean, the physical motion feels less grounded. Kling 2.6 delivers stronger motion realism. The waves break and recede with more natural weight, and foam behavior appears more physically accurate. Lighting is also more cinematic and less processed overall, although minor grain can appear in more detailed textures. In this example, Wan 2.6 leads in stability and clarity, while Kling 2.6 stands out in motion realism and overall cinematic quality. ## Example 2: Human Subject + Motion Prompt: A young woman walking through a busy city street at night, neon signs glowing in the background, light rain falling, reflections on the wet pavement, natural walking motion, slight camera tracking from the side, cinematic lighting, realistic human proportions, detailed face, smooth and natural movement Wan 2.6 Output Kling 2.6 Output ## Example 2 Evaluation Both Wan 2.6 and Kling 2.6 generate a cinematic rainy city scene, with clear reflections on wet pavement and strong overall prompt adherence. Wan 2.6 produces a visually striking output, with high contrast, vibrant neon lighting, and sharp foreground detail. The integration of rain elements, such as droplets on the subject and environment, is well executed. However, the motion feels less continuous, with quick cuts that reduce the sense of a natural walking sequence. Kling 2.6 delivers a more stable tracking shot, maintaining consistent human proportions and a natural walking gait. The movement of the subject, including subtle details like coat sway, feels more physically grounded. While the lighting is more muted and background details appear slightly softer, overall motion continuity is significantly stronger. In this example, Wan 2.6 stands out in visual atmosphere and detail, while Kling 2.6 performs better in motion consistency and tracking realism. ## Example 3: Complex Motion Scene Prompt: A fast-paced street chase scene with a man running through a crowded market, people moving in different directions, stalls with hanging fabrics and objects, dynamic camera movement following from behind, slight camera shake, cinematic lighting, motion blur, realistic human movement, high detail, smooth and continuous action Wan 2.6 Output Kling 2.6 Output ## Example 3 Evaluation Both Wan 2.6 and Kling 2.6 handle a high-intensity chase scene through a crowded market, testing complex motion and multi-subject interaction. Wan 2.6 produces a more dramatic output, using dynamic cuts and vibrant lighting to enhance intensity. However, fast motion introduces inconsistencies, including slight grounding issues and visible ghosting in background elements. Kling 2.6 maintains a stable tracking shot with more consistent character movement and better spatial interaction with the environment. While the visuals are less stylized, motion feels more physically grounded overall. In this example, Wan 2.6 emphasizes cinematic style, while Kling 2.6 performs better in motion consistency and stability. ## Example 4: Cinematic Storytelling Scene Prompt: A lone astronaut standing on a vast alien landscape under a purple sky with two moons, slow camera push-in, dramatic cinematic lighting, wind blowing dust across the ground, emotional and atmospheric mood, high detail, realistic textures, smooth and natural motion Wan 2.6 Output Kling 2.6 Output ## Example 4 Evaluation Both Wan 2.6 and Kling 2.6 successfully generate a cinematic sci-fi scene, capturing the astronaut, alien landscape, and dual moons with strong prompt adherence. Wan 2.6 delivers a clean and vibrant output, with sharp visor reflections and clear facial detail. However, the astronaut's movement feels slightly static, and the overall scene lacks deeper cinematic blending. Kling 2.6 produces a more atmospheric and cohesive result. Environmental effects like dust and fog interact more naturally with the subject, and lighting feels better integrated. The slow camera push-in adds a stronger cinematic feel, though facial detail inside the visor is less defined. In this example, Wan 2.6 stands out in subject clarity, while Kling 2.6 leads in atmospheric realism and cinematic composition. ## Example 5: Multi-Subject Interaction (Stress Test) Prompt: A group of three friends sitting around a campfire at night, talking and laughing, one person roasting marshmallows, another playing guitar, sparks flying from the fire, warm firelight illuminating faces, subtle camera movement circling the group, natural human interaction, realistic motion, high detail Wan 2.6 Output Kling 2.6 Output ## Example 5 Evaluation Both Wan 2.6 and Kling 2.6 follow the prompt well, generating a warm campfire scene with clear character interactions, including guitar playing and roasting marshmallows. Wan 2.6 delivers a vibrant and high-contrast result, with sharp facial expressions and clean close-up details. Fire lighting is punchy and well-defined. However, motion feels less consistent, particularly in the guitar interaction, and frequent cuts break the sense of a continuous scene. Kling 2.6 produces a more cohesive, single-shot output with natural night-time lighting. Firelight and sparks interact more realistically with the environment, and character movements, especially hand motions, feel smoother and more physically grounded. While the visuals are slightly more muted, the overall scene feels more unified. In this example, Wan 2.6 stands out in detail and clarity, while Kling 2.6 performs better in continuity and natural motion. ## Final Verdict: Wan 2.6 vs Kling 2.6 Wan 2.6 and Kling 2.6 both deliver strong results for AI video generation, but they are optimized for different priorities. Wan 2.6 stands out in visual clarity, structured outputs, and overall stability. It performs well in controlled scenarios, where clean frames, sharp details, and predictable results matter. This makes it a practical choice for workflows that require consistency and efficiency, especially when using the Wan 2.6 API at scale. Kling 2.6, on the other hand, consistently delivers more natural motion, better scene continuity, and stronger cinematic realism. Across multiple examples, it handles movement, camera tracking, and environmental interaction more effectively, making it better suited for content that prioritizes visual storytelling and realism. Overall, Wan 2.6 is the better option for stability and structured generation, while Kling 2.6 is the stronger choice for motion realism and cinematic output. For most production use cases where realism and motion quality are critical, Kling 2.6 has a clear edge. Wan 2.6 and Kling 2.6 API keys via PiAPI today! Sign up ## FramePack API Guide: What is FramePack AI? How to Use FramePack for AI Video Extension Learn what FramePack AI is and how to use it for AI video extension. Includes FramePack tutorial and examples. As AI video generation improves, more creators are experimenting with tools that can turn prompts into short cinematic clips. However, one limitation continues to surface across most models: generating longer, consistent videos is still difficult. This is where FramePack AI comes into play. Instead of attempting to generate an entire video in one pass, FramePack introduces a structured approach based on AI generated frames. It allows creators to extend existing clips by using the final frame as a reference point, making it possible to maintain visual consistency across time. In this guide, we will explore what FramePack is, how the FramePack AI video model works, and how to use FramePack in a practical workflow that combines video generation, frame extraction, and continuation. ## What is FramePack? FramePack is an AI video tool designed to extend and refine generated video sequences using a frame-based approach. Unlike traditional text-to-video models that produce a full clip from a single prompt, FramePack focuses on continuation. The idea behind the FramePack AI video model is simple. A video is not treated as one output, but as a sequence of connected frames. By controlling how each segment evolves from the previous one, the system can produce more stable and coherent results. This makes FramePack particularly useful for scenarios where consistency matters, such as storytelling, product demos, and long-form video generation. For developers, the FramePack API is available at PiAPI to start integrating into your workflows. ## How FramePack AI Works To understand how to use FramePack effectively, let us go through what it features. FramePack AI features next frame prediction where it utilizes next frame prediction for I2V generations to autoregressively generate video. The consistency and efficient results come from the advanced training methods for anti-drifting and anti-forgetting that the model is built on. Next, it is important to look at how the workflow is structured so that you will know how to use FramePack. The process begins with generating an initial frame using a image or video generation model. For the latter, this could be a text-to-video or image-to-video generation step. Once the clip is created, the final frame is extracted and used as the starting point for continuation. This concept is often referred to as FramePack start and end frame control. The end frame of one segment becomes the start frame of the next. Next is where the FramePack AI prompts play an important role. Instead of describing a full scene from scratch, prompts should focus on continuation, such as extending motion, changing camera perspective, or introducing new elements gradually. After the next segment is generated, the process can be repeated. The new final frame is extracted and used again, allowing you to extend the video step by step. This iterative approach is what makes FramePack powerful for long-form generation. Additionally, FramePack is often used alongside image-to-video workflows, commonly referred to as FramePack I2V. In this setup, a single image is used as the starting point for generating motion. This makes it possible to animate static images. ## FramePack Prompt Examples Effective prompts are essential when working with FramePack. Because the system builds on previous frames, prompts should guide continuation rather than restart the scene. For example, instead of describing an entirely new environment, a prompt might look like this: A close-up shot transitions into a wider angle, revealing more of the futuristic city skyline in the background. These types of FramePack prompt examples help maintain continuity while still introducing variation. ## FramePack Video Extension Examples To better understand how FramePack AI video workflows operate, it helps to look at practical scenarios. In this example, we start by generating a short cinematic clip using our Veo 3 API . The base prompt used for Veo 3 is: A lone cyberpunk samurai walking through a neon-lit street at night, rain falling, cinematic lighting, reflections on wet ground. Veo 3 Output The generated 8-second clip shows the samurai walking forward in a consistent direction, with neon lights reflecting across the environment. FramePack Reference Image Once this base clip is generated, the next step is to extract the final frame. The extracted frame is then passed into FramePack along with a continuation prompt. Unlike the original prompt, this prompt does not redefine the scene. Instead, it focuses on how the existing scene should evolve. The Framepack prompt used: The samurai continues walking forward slowly, neon signs flickering, rain intensifies slightly. FramePack Output After the new segment is generated, the process can be repeated. The final frame of the extended clip is extracted again and used for another continuation step. Over multiple iterations, the original 8-second clip can be extended into a much longer sequence without losing visual coherence. ## Using the FramePack API For developers, the FramePack API enables automation of the entire workflow. Instead of manually extracting frames and generating segments, each step can be handled programmatically. Because the FramePack AI API is modular, it can be integrated with other GenAI models. Check out the FramePack documentation for more technical details! Typical JSON-style Request Body We make it simple by handling all the backend processes throughout your video generation with our FramePack API, which allows you to choose the start and end frames as well as the duration of the output of up to 30 seconds. ## Conclusion FramePack represents a more structured approach to AI video generation. Rather than relying on a single model to produce an entire sequence, it breaks the process into smaller steps based on frames. By generating a base clip, using the final frame as an anchor, and extending the sequence iteratively, creators can produce longer and more consistent videos. This approach provides greater control over motion, composition, and continuity, making it well suited for both creative and production use cases. As AI video tools continue to evolve, workflows like FramePack are likely to become increasingly important for building reliable and scalable video generation systems. Start testing both models and get your FramePack AI Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Qwen AI vs Nano Banana 2: Which AI Model API Is Better in 2026? Compare Qwen AI vs Nano Banana 2 for image generation. Explore API features, pricing, prompt tips, and real examples to choose the best model in 2026. AI image generation models are improving fast, with better quality, control, and consistency. Two models gaining attention are Qwen Image and Nano Banana 2 . Both are accessible through API integration and are used for generating images from prompts.The Qwen API focuses on higher-quality outputs and stronger prompt understanding. Nano Banana 2 is built for speed and efficient image generation at scale.Developers often compare factors like Qwen AI API access, Qwen API key setup, and Qwen API pricing when choosing between models. At the same time, many are exploring how to use Nano Banana 2 and testing different prompt styles.In this guide, we compare Qwen AI vs Nano Banana 2 to help you choose the right image generation model for your needs in 2026. ## What is Qwen AI? Qwen AI is an image generation model developed by Alibaba, designed to create high-quality images from text prompts.It is accessed through the Qwen API, allowing developers to generate images programmatically for apps, tools, and creative use cases.The model is known for strong prompt understanding and more detailed outputs. It performs well when generating structured scenes, specific styles, or complex visual instructions.To get started, developers need a Qwen AI API key, which is used to authenticate API requests and connect to the model.You can explore the model via the Qwen api and refer to the Qwen api documentations for setup and usage. ## What is Nano Banana 2? Nano Banana 2 is an image generation model designed for fast and efficient image creation from text prompts.It is built for developers who need quick outputs and scalable performance, especially in applications that require high-volume image generation.The model is accessible through the nano banana 2 api, making it easy to integrate into apps and services with minimal setup.Compared to larger models, Nano Banana 2 focuses on speed and consistency rather than deep prompt interpretation. It works best for simpler prompts, repeated tasks, and production use cases where latency matters.You can explore the model through the nano banana 2 api and refer to the nano banana 2 api documentations for setup and usage. ## Model Similarities and Differences Despite being designed for different use cases, Qwen AI and Nano Banana 2 share several core capabilities. Both models generate images from text prompts, support structured prompt inputs, and can be integrated through API-based workflows such as the Qwen API and nano banana 2 api. However, the two models differ in performance and optimization. ## Image Quality The most noticeable difference between Qwen AI and Nano Banana 2 is image quality. Qwen AI produces more detailed visuals, with better lighting, textures, and overall refinement.Nano Banana 2 generates simpler images with less detail, but remains consistent for basic outputs. ## Prompt Adherence Qwen AI demonstrates stronger prompt understanding, especially for complex or detailed prompts. It handles multiple elements and structured scenes more accurately.Nano Banana 2 works best with simpler prompts. Developers often rely on a clear prompt guide to improve consistency. ## Generation Stability Qwen AI provides more stable outputs with fewer visual artifacts, especially in complex generations.Nano Banana 2 maintains consistency in simpler tasks but may struggle with more detailed prompts. ## Speed and Efficiency Nano Banana 2 is optimized for fast image generation and lower resource usage, making it suitable for high-volume use.Qwen AI focuses on higher-quality outputs, which may come with slightly slower generation times. ## Pricing Comparison Pricing is an important factor when choosing between Qwen AI and Nano Banana 2, especially for large-scale image generation. ## Qwen API Pricing Qwen AI uses a simple pricing model at $0.015 per image , based on PiAPI documentation. This makes it straightforward for developers to estimate costs when scaling usage through the Qwen API docs . ## Nano Banana API Pricing Nano Banana 2 pricing starts from $0.06 per image , depending on resolution.For full pricing details and supported configurations, refer to the nano banana 2 api docs . ## Prompt Tips Good prompts make a big difference in image quality and consistency. ## Qwen AI Qwen AI handles detailed prompts well. Use clear descriptions, styles, and scene details for better results.For best practices, refer to the qwen ai best prompt practices . ## Nano Banana 2 Nano Banana 2 works best with simple and direct prompts. Keep prompts short and structured for consistent outputs.You can follow a refer to the nano banana 2 prompt guide here . ## Evaluation Methodology To ensure a fair comparison, both Qwen AI and Nano Banana 2 are evaluated using a structured framework adapted from Labelbox-style image assessment . Each generated image is assessed across: 1. Prompt adherence 2. Image quality 3. Composition and realism 4. Artifacts All examples use the same prompts to isolate model performance. ## Example Comparisons Below are a few prompt examples to compare Qwen AI and Nano Banana 2 outputs. ## Example 1 Product + Lighting Control Prompt: a premium smartwatch on a black reflective surface, studio lighting, sharp reflections, minimal luxury style, high detail Qwen Image Output Nano Banana 2 Output Evaluation: Qwen AI shows stronger prompt adherence and overall image quality, producing a sharper and more polished smartwatch with clean lighting and a well-defined reflective surface. The composition feels balanced and aligned with a premium product shot, with minimal visible artifacts. Nano Banana 2 also follows the prompt well, especially in terms of reflection and product placement, but the image appears softer with less refined detail and slightly flatter lighting. While both outputs are usable, Qwen AI delivers a more realistic and high-end result, whereas Nano Banana 2 prioritizes simplicity and speed over fine detail. ## Example 2 Human + Realism Prompt: a street-style photo of a young woman walking across a pedestrian crossing, casual outfit, natural lighting, slightly windy, realistic photography Qwen Image Output Nano Banana 2 Output Evaluation: Qwen AI delivers stronger prompt adherence and realism, with clean composition, natural lighting, and a clear sense of motion from the hair and walking pose. The subject stands out well and the image feels like a polished street-style photograph with minimal artifacts. Nano Banana 2 also captures the scene correctly, including the crossing and urban setting, but the image appears slightly less focused on the subject, with more background distraction and softer overall detail. While both outputs are usable, Qwen AI produces a more refined and visually cohesive result, whereas Nano Banana 2 leans toward a more general and less controlled composition. ## Example 3 Complex Scene + Detail Prompt: a futuristic control room with multiple holographic screens, glowing blue interface, a person interacting with the system, dark environment, cinematic lighting, high detail Qwen Image Output Nano Banana 2 Output Evaluation: Qwen AI shows strong prompt adherence with clear holographic interfaces, consistent blue lighting, and a clean, structured control room setup. The image is sharp and easy to read, with minimal artifacts and a focused composition. Nano Banana 2 also captures the scene well and even adds more environmental depth and cinematic atmosphere, but the output is slightly more complex and less controlled, with some elements appearing busier and less defined. Overall, Qwen AI produces a cleaner and more precise result, while Nano Banana 2 delivers a more dramatic but less structured scene. ## Final Thoughts Qwen AI and Nano Banana 2 serve different needs in image generation.Qwen AI focuses on higher-quality outputs, better prompt adherence, and more consistent results across complex scenes. It is better suited for use cases that require detail, structure, and visual precision. Nano Banana 2 is built for speed and efficiency. It performs well for simpler prompts and high-volume generation, making it a practical choice for scalable applications. Start testing both models and get your Qwen Image and Nano Banana 2 Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Trellis vs Trellis 2: Which 3D Generation API Should You Use? Explore Trellis vs Trellis 2, including 3D generation API capabilities, mesh quality, texture consistency, and use cases like 3D product visualization. The development of AI-driven 3D generation models has introduced new possibilities for creating structured 3D assets from minimal inputs. Among these, Trellis and Trellis 2 represent two iterations of a structured 3D generation approach designed for scalable and versatile asset creation. While both models focus on generating 3D representations such as meshes and textured objects, Trellis 2 introduces improvements in structure consistency, geometry quality, and overall generation stability. The practical difference, however, depends on how these improvements translate into usable 3D outputs. In this comparison, we evaluate Trellis vs Trellis 2 across generation quality, structural consistency, and usability for workflows such as 3D product visualization, 3D asset creation, and AI-driven modeling pipelines. ## What is Trellis? Trellis is an AI-powered 3D generation framework built around structured 3D latent representations, designed to convert images (and increasingly text) into usable 3D assets. At its core, Trellis focuses on: - Structured 3D latent modeling for scalable generation - Flexibility for downstream pipelines (games, AR/VR, simulation) The Trellis API is available for developers who plan on scaling their products. Get more information on Trellis AI with our previous blog! ## What is Trellis 2? Trellis.2 is the latest open-source model by Microsoft, and it represents a major leap in image-to-3D generation quality and efficiency. Trellis 2 Microsoft is a 4B-parameter model capable of generating high-fidelity, fully textured 3D assets from a single image. Key Innovations in Trellis 2: - Voxel representation (field-free geometry) - Native 3D VAE with structured latents - Full PBR material generation (metallic, roughness, opacity) - Handles complex topology (thin structures, hollow shapes) These improvements make Trellis 2 more suitable for production workflows such as 3D product animation and 3D product visualization, where asset quality and consistency are critical. The Trellis 2 API is available for developers who plan on scaling their products. ## Trellis: 3D Generation API Model Similarities Despite differences in generation quality, both models share core capabilities. Structured 3D Generation Both Trellis and Trellis 2 use structured latent representations to generate 3D objects, enabling consistent geometry creation. Mesh-Based Output Both models produce Trellis mesh outputs that can be exported and used in 3D pipelines. Prompt-Based Generation Both support prompt-driven workflows, allowing users to generate 3D assets such as objects, characters, or product models. Scalable 3D Asset Creation Both models are suitable for generating assets for applications like 3D product visualization and concept prototyping. ## Key Differences: Trellis vs Trellis 2 Geometry Quality Trellis 2 produces more consistent and refined geometry compared to Trellis, with improved edge definition and fewer structural inconsistencies. Texture and Surface Detail Trellis 2 generates more coherent textures and smoother surface transitions, making outputs more suitable for visual presentation. Multi-View Consistency Trellis 2 demonstrates stronger consistency across different viewing angles, reducing distortions when rotating the model. Generation Stability Trellis 2 shows improved stability in generation, producing fewer artifacts and more reliable outputs across prompts. Production Readiness While Trellis is suitable for experimentation and prototyping, Trellis 2 is better suited for production workflows requiring higher quality assets. ## Evaluation: How We Compare Trellis vs Trellis 2 Both models were compared with through image to 3D generation task. The images are generated with the Nano Banana 2 API and no prompts were used for image to 3D task. To ensure a fair comparison, both models were evaluated using identical reference images and follow their corresponding documentations, Trellis API documentation and Trellis 2 API documenation . The evaluation focuses on: 1. Geometry accuracy 2. Surface detail 3. Texture consistency 4. Artifacts and distortions ## 3D Output Comparison: Trellis vs Trellis 2 ## Example 1: Game Object Model For the first example, we will generate an Energy Sword for you Halo fans out there! Reference Image Trellis AI Comparison Analysis: Trellis 2 demonstrates strong geometry accuracy and recognizable structure, successfully capturing the overall form of the Energy Sword. In contrast, Trellis fails to produce a recognizable output, indicating weak structural consistency. No major artifacts are observed in Trellis 2. ## Example 2: Character Figure We are sure you know about this one! For the second example, we did a 3D generation for Luffy from One Piece. Reference Image Trellis AI Comparison Analysis: Trellis 2 performs well in shape consistency and overall character structure, producing a recognizable result. Trellis, however, fails to generate a coherent and identifiable figure, showing limitations in handling character-based generation. Trellis 2 maintains better structural integrity with minimal artifacts. ## Example 3: Complex Object Lastly, we generated a really complex object with many intricate details, Mount Rushmore. Reference Image Trellis AI Comparison Analysis : Both Trellis and Trellis 2 struggle with this complex object, failing to accurately match the reference. The outputs lack structural accuracy and detail, highlighting limitations in handling really complex generations. No clear advantage is observed between the two models in this case. ## Final Thoughts: Trellis vs Trellis 2 Across the three evaluated examples, Trellis 2 demonstrates clear improvements in geometry accuracy and overall structural consistency compared to the original Trellis model. In both the Energy Sword and Luffy examples, Trellis 2 is able to produce recognizable outputs, while Trellis fails to generate coherent structures. However, when tested on more complex scenes such as Mount Rushmore, both models struggle to maintain accuracy and detail, highlighting current limitations in handling large-scale, multi-subject environments. Overall, Trellis 2 provides a significant step forward in 3D generation quality and reliability, making it more suitable for practical use cases. While the original Trellis model may still serve for experimentation, Trellis 2 offers a stronger foundation for workflows requiring recognizable and structurally consistent 3D outputs. Start testing both models and get your Trellis AI Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Dreamina Seedance 2.0 vs Sora 2: Full Comparison of Video Quality, Pricing, and Prompts Dreamina Seedance 2.0 vs Sora 2 comparison covering video quality, Seedance pricing, Sora AI pricing, prompts, and performance. See which model fits your needs. The release of Dreamina Seedance 2.0 introduced a practical and controllable AI video generation model built for scalable workflows. Developed under ByteDance's Dreamina ecosystem, it focuses on structured prompts and consistent output. OpenAI later introduced Sora 2 , a more advanced video generation model designed for higher realism, longer sequences, and more dynamic scene behavior. Both models support text-to-video generation through API-based workflows, but they differ significantly in how they balance realism, control, and usability. In this comparison, we evaluate Dreamina Seedance 2.0 API vs Sora 2 API across video quality, prompt adherence, motion realism, and generation stability. ## What is Dreamina Seedance 2.0? Dreamina Seedance 2.0 is an AI video generation model focused on control and consistency . It uses structured prompting to produce stable and predictable outputs, making it suitable for scalable and production-ready workflows. Its strength lies in repeatability and ease of integration, especially for applications that require reliable results across multiple generations. ## What is Sora 2? Sora 2 is OpenAI's high-end AI video model built for realism and cinematic quality . It generates more dynamic scenes with natural motion and richer visual detail. Compared to more practical models, Sora 2 prioritizes visual fidelity and complexity, making it better suited for high-quality output rather than speed or large-scale usage. ## Model Similarities and Differences Despite their different positioning, Dreamina Seedance 2.0 and Sora 2 share several core capabilities. Both models support text-to-video generation, structured prompting, and API-based integration. However, they differ in how they prioritize performance, realism, and usability. ## Video Quality The most noticeable difference between Seedance 2.0 vs Sora 2 is video realism. Seedance 2.0 produces clean and consistent outputs, while Sora 2 delivers more cinematic visuals with higher detail and improved lighting. ## Prompt Adherence Seedance 2.0 demonstrates strong prompt adherence, especially with structured prompts. Sora 2 is more flexible and interpretive, which can result in more creative outputs but slightly less predictable results. ## Generation Stability Seedance 2.0 focuses on stable and repeatable generation, reducing inconsistencies across outputs. Sora 2 has a higher performance ceiling but may show more variation depending on prompt complexity. ## Motion Realism Sora 2 shows stronger motion realism, particularly in complex scenes and dynamic movements. Seedance 2.0 maintains smoother and more controlled motion but with less physical accuracy. ## Speed and Usability Seedance 2.0 is optimized for faster generation and scalable workflows. Sora 2 is more resource-intensive and better suited for high-quality output rather than speed. ## Price Dreamina Seedance 2.0 1. Standard (higher quality): $0.15 / second 2. Fast version: $0.08 / second 3. Watermark removal: $0.008 / second Seedance 2.0 offers flexible pricing options, making it suitable for both high-quality output and scalable workflows. For more details, please refer to the official Seedance 2.0 API documentation . Sora 2 1. Sora 2: $0.08 / second 2. Sora 2 Pro: $0.24 / second Sora 2 follows a pay-as-you-go model and is positioned as a higher-end option for more advanced video generation. For more details, please refer to the official Sora 2 API documentation . ## Prompt Guide and Examples Writing strong prompts significantly improves the quality of generated videos. A well-structured prompt typically includes the following: scene → subject → action → style → motion This structure helps both Dreamina Seedance 2.0 and Sora 2 better interpret the intended output, especially for more complex or cinematic scenes. To demonstrate the capabilities of each model, both Seedance 2.0 and Sora 2 are tested using identical prompts. This allows for a direct comparison of video quality, prompt adherence, and motion consistency across both models. In general, Seedance 2.0 performs best with more structured prompts, where each element is clearly defined. Sora 2, on the other hand, responds well to more descriptive and natural prompts, often producing more cinematic and dynamic results. For more detailed guidance, you can refer to dedicated Seedance 2.0 prompt guides and Sora 2 prompt guides to better understand how to structure prompts for each model and achieve more consistent outputs. ## Evaluation: How We Evaluate Seedance 2.0 vs Sora 2 For this qualitative comparison, we focus on text-to-video generation across different scenarios, including single-shot scenes, character-focused clips, and complex multi-object environments. The evaluation framework is adapted from a Labelbox-style T2V assessment and evaluates each output across five dimensions: 1. Prompt adherence 2. Video realism 3. Video resolution 4. Artifacts 5. Motion consistency Each example uses the same prompt for Dreamina Seedance 2.0 and Sora 2 to isolate model behavior rather than prompt variation. Additionally, both models support advanced video generation capabilities, and all outputs are evaluated based on visual performance and motion quality. ## Example 1: Cinematic Scene Prompt: A cinematic wide shot of a futuristic city skyline at sunset, warm golden hour lighting reflecting off glass buildings, flying vehicles moving between skyscrapers, slow smooth camera pan from left to right, highly detailed, realistic style, soft atmospheric haze, depth of field, cinematic composition Sora 2 Output Seedance 2.0 Output ## Output Analysis Seedance 2.0 produces a stable and well-structured cinematic scene with consistent lighting and smooth camera movement. Object placement remains clear, and the motion of flying vehicles is controlled and predictable. The overall output is clean and reliable, with strong prompt adherence and minimal artifacts. Sora 2 delivers a more cinematic and immersive result, with richer lighting, stronger depth, and more dynamic reflections. Motion appears more natural, and the camera movement feels more organic, contributing to a more realistic and film-like scene. Overall, Seedance 2.0 offers greater stability and consistency, while Sora 2 achieves higher visual realism and more advanced cinematic quality. ## Example 2: Character Motion Prompt: A close-up portrait of a young woman smiling gently, natural facial expressions, subtle head movement, soft studio lighting, realistic skin texture, shallow depth of field, cinematic style Sora 2 Output Seedance 2 Output ## Output Analysis Seedance 2.0 maintains consistent facial structure with stable motion and clear expressions. The output is smooth and controlled, with minimal distortion, though facial details and lighting transitions appear slightly flatter. Sora 2 produces more natural facial movement with improved realism in skin texture and lighting. Subtle expressions and micro-movements feel more lifelike, resulting in a more expressive and dynamic output. Overall, Seedance 2.0 prioritizes stability and consistency, while Sora 2 achieves higher realism in facial detail and motion. ## Example 3: Complex Scene Prompt: A busy street market at night with multiple people walking, food vendors cooking, steam rising from stalls, neon lights reflecting on wet ground, dynamic movement, realistic environment, cinematic style, slight camera movement Sora 2 Output Seedance 2.0 Output ## Output Analysis Seedance 2.0 maintains a clear and structured scene, with stable composition across multiple subjects. Movement is controlled, and elements remain visually consistent, though interactions between people and environment feel more limited. Sora 2 produces a more dynamic and immersive environment, with stronger interaction between subjects and surroundings. Lighting, reflections, and motion appear more natural, giving the scene a more realistic and lively feel. Overall, Seedance 2.0 delivers better stability and clarity in complex scenes, while Sora 2 achieves higher realism and more natural multi-object interaction. ## Final Verdict Dreamina Seedance 2.0 provides a practical and scalable solution for AI video generation. With strong prompt adherence, consistent output, and stable motion, it performs reliably across different use cases, from simple scenes to more complex environments. Its structured approach and flexible Seedance pricing make it well suited for developers and businesses building production-ready workflows. Sora 2 focuses on higher realism, delivering more cinematic visuals, better motion physics, and richer scene interaction. It is better suited for users prioritizing visual quality and more advanced output, though with higher Sora AI pricing and less emphasis on scalability. Overall, Dreamina Seedance 2.0 stands out as a reliable and accessible choice for scalable video generation, while Sora 2 represents the higher-end option for cutting-edge realism. Start testing both models and get your Seedance 2.0 and Sora 2 API keys via PiAPI today. Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## GPT Image 1.5 vs Nano Banana 2: Which Is Better? (Full Comparison) Compare GPT Image 1.5 vs Nano Banana 2. See key differences, output quality, and which model is better for your use case. AI image generation models are becoming a core part of modern applications, enabling developers to create visuals directly from text prompts with increasing accuracy and control. Among the latest models, GPT Image 1.5 and Nano Banana 2 offer two distinct approaches to building an efficient AI image generation model, each optimized for different priorities such as visual quality or speed and cost efficiency.Both models can be integrated through API based workflows, allowing developers to generate images using structured prompts via the GPT image API and Nano Banana 2 API . With platforms like PiAPI , developers can access and switch between multiple models without managing complex infrastructure.In this GPT Image 1.5 vs Nano Banana 2 comparison, we evaluate differences in image quality, prompt adherence, and generation performance. We also explore nano banana API pricing, nano banana 2 cost, GPT-image-1-pricing, and practical examples to help developers choose the right model for their use case. ## What is GPT Image 1.5? GPT Image 1.5 is an AI image generation model that produces high fidelity visuals with strong prompt understanding and consistent outputs. It enables developers to generate images using structured text prompts. Through the GPT image API, it can be integrated into applications for use cases such as marketing visuals, content creation, and automated workflows. Refer to the GPT Image 1.5 API documentation for details on integration, request parameters, and usage. Compared to earlier models , GPT Image 1.5 offers improved lighting realism, sharper textures, and better adherence to complex prompts, making it suitable for production-level use cases. ## What is Nano Banana 2? Nano Banana 2 is a lightweight and efficient AI image generation model optimized for fast and scalable text to image workflows. It is designed for speed and cost efficiency, making it suitable for high volume image generation. The model can be integrated through API based workflows using structured prompts. For implementation details, refer to the Nano Banana 2 API documentation . Nano Banana 2 is commonly used in scenarios where speed and affordability are prioritized, including scalable content generation and rapid prototyping. ## Model Similarities and Differences: GPT Image 1.5 vs Nano Banana 2 Differences Despite being different model tiers, GPT Image 1.5 and Nano Banana 2 share several core capabilities. Both models are designed as AI image generation models that convert structured prompts into visual outputs. They can be integrated through API workflows, making them suitable for developers building applications with text to image functionality.However, there are clear differences in how each model performs across key areas: ## Image Quality The most noticeable difference in this GPT Image comparison is image fidelity. GPT Image 1.5 produces sharper textures, more realistic lighting, and stronger detail across complex scenes. Nano Banana 2 delivers solid results for general use cases, but images may appear slightly softer with less refined textures, especially in more detailed or cinematic prompts. ## Prompt Adherence GPT Image 1.5 demonstrates stronger prompt interpretation, particularly for complex prompts involving multiple elements, lighting conditions, or structured compositions. This makes it more reliable for precise prompt-based image generation. Nano Banana 2 performs well with simpler prompts but may struggle with highly detailed instructions or nuanced visual requirements. ## Generation Stability GPT Image 1.5 focuses on consistency, producing outputs with fewer artifacts and more coherent object structures across the image. Nano Banana 2 is generally stable for standard use cases, but may show minor inconsistencies in more complex scenes, especially when multiple objects or detailed environments are involved. ## Speed and Cost Efficiency Nano Banana 2 is optimized for speed and cost efficiency, making it a strong option for developers prioritizing high-volume image generation. Users exploring nano banana API pricing or nano banana 2 cost often choose it for scalable workflows. GPT Image 1.5, while slightly slower, delivers higher-quality outputs and is preferred when visual fidelity and accuracy are more important than generation speed. ## GPT Image 1.5 vs Nano Banana 2 Pricing Developers evaluating these models should consider pricing when choosing between GPT Image 1.5 and Nano Banana 2, as both models are optimized for different use cases.For GPT Image models, based on PiAPI’s pricing, image generation starts from approximately $0.011 per image for lower quality outputs at standard resolutions and can go up to around $0.25 per image for higher quality settings. This flexible pricing makes GPT Image 1.5 suitable for both cost efficient and high quality use cases depending on configuration.In comparison, Nano Banana 2 follows a more fixed pricing structure based on output resolution. Pricing starts at $0.06 per image (1K resolution) , increases to $0.08 per image (2K) , and goes up to $0.12 per image (4K) . Developers exploring nano banana API pricing or nano banana 2 cost often prefer this predictable pricing model for scaling applications.The overall cost for both models depends on: 1. Model configuration and resolution 2. Number of generated images 3. Output quality settings In this GPT Image comparison, the key difference lies in flexibility versus predictability. GPT Image 1.5 offers a wider pricing range depending on quality, while Nano Banana 2 provides more consistent per image pricing across resolutions. ## Prompt Guide and Best Practices Writing effective prompts is essential for getting high quality results from any AI image generation model. Both GPT Image 1.5 and Nano Banana 2 rely on structured prompts to generate accurate outputs.A well structured prompt typically includes: Style → Subject → Setting → Action → Composition More detailed prompts generally improve results for GPT Image 1.5, especially in complex scenes. Developers can refer to GPT Image 1.5 best prompt practices to better understand how to structure detailed and precise prompts for higher quality outputs.For Nano Banana 2, clear and concise prompts tend to work best, as overly complex instructions may not always improve output. Developers often explore nano banana prompt guide strategies or experiment with different nano banana AI prompt formats. You can also refer to Nano Banana 2 best prompt practices for more optimized prompt techniques.We evaluate all outputs using a Labelbox evaluation framework , focusing on prompt adherence, visual quality, composition, and generation consistency. ## Example 1: Cinematic Scene In this example, we test how both models perform on a complex cinematic scene involving multiple elements, lighting conditions, and depth. ## Prompt Used A cinematic, ultra realistic scene of a futuristic city at sunset, with neon lights reflecting off glass skyscrapers. Flying cars move through the air in organized traffic, while pedestrians walk along a busy street filled with digital billboards. Warm golden sunlight mixes with cool neon lighting, creating strong contrast and reflections. Wide angle composition, highly detailed, sharp focus, depth of field, 8k quality. GPT 1.5 Image Output Nano Banana 2 Output ## Analysis GPT Image 1.5 produces a more polished and photorealistic portrait, with sharper facial features, smoother skin texture, and more refined lighting transitions. The subject stands out clearly from the background, and the overall image has a cinematic feel with strong depth of field. Nano Banana 2 captures the scene accurately, including the subject, lighting, and environment, but the image appears slightly less refined. Facial details are softer, and while the lighting is natural, it lacks the same level of precision and depth. The background elements are well composed, but the overall image feels less detailed compared to GPT Image 1.5. ## Example 2: Portrait Scene In this example, we test how both models handle human subjects, focusing on realism, lighting, and facial detail. ## Prompt Used A photorealistic portrait of a young man sitting by a window in a modern apartment, soft natural morning light illuminating his face. He is wearing casual clothing and looking slightly away from the camera. The background is softly blurred with warm interior tones. Detailed skin texture, natural lighting and shadows, shallow depth of field, 50mm lens, cinematic composition, high detail. GPT Image 1.5 Output Nano Banana 2 Output ## Analysis GPT Image 1.5 produces a more polished and photorealistic portrait, with sharper facial features, smoother skin texture, and more refined lighting transitions. The subject stands out clearly from the background, and the overall image has a cinematic feel with strong depth of field.Nano Banana 2 captures the scene accurately, including the subject, lighting, and environment, but the image appears slightly less refined. Facial details are softer, and while the lighting is natural, it lacks the same level of precision and depth. The background elements are well composed, but the overall image feels less detailed compared to GPT Image 1.5. ## Example 3: Workspace Scene In this example, we test how both models handle multiple objects, fine details, and structured composition within a realistic environment. ## Prompt Used A modern creative studio desk setup with a tablet displaying a digital illustration, a wireless keyboard, a smartphone, a sketchbook, and a cup of coffee. Soft warm lighting from a desk lamp creates gentle shadows across a wooden desk. A large monitor in the background shows a design interface. Minimalist aesthetic, clean composition, realistic textures, depth of field, high detail, 4k quality. GPT Image 1.5 Output Nano Banana 2 Output ## Analysis GPT Image 1.5 produces a more refined and visually appealing workspace, with sharper object details and more consistent lighting across the scene. Elements such as the tablet display, desk surface, and surrounding objects appear well defined, with realistic textures and a polished overall composition.Nano Banana 2 captures the overall layout and objects accurately, but finer details are less pronounced. Textures appear slightly softer, and while the lighting is natural, it lacks the same level of depth and precision. The scene remains well structured, but overall feels less detailed compared to GPT Image 1.5. ## Final Thoughts: GPT Image 1.5 vs Nano Banana 2 Across the three examples, both GPT Image 1.5 and Nano Banana 2 demonstrate strong prompt alignment and the ability to generate visually appealing images across different scenarios. In simpler scenes, the two models can produce comparable results, with both delivering accurate compositions and minimal visual inconsistencies.However, GPT Image 1.5 consistently shows stronger performance in visual detail, realism, and prompt interpretation. It handles complex scenes more effectively, particularly in areas such as lighting, reflections, and fine textures. In all three examples, GPT Image 1.5 produced sharper and more cohesive outputs, while Nano Banana 2 results appeared slightly softer with less refined details.For developers evaluating GPT Image 1.5 vs Nano Banana 2, the choice ultimately depends on workflow priorities: faster generation and cost efficiency, or higher fidelity outputs with improved realism and consistency. Start testing both models and get your GPT Image 1.5 and Nano Banana 2 API keys via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Nano Banana Pro vs Nano Banana 2: Google Nano Banana API Comparison with Examples Nano Banana Pro vs Nano Banana 2: Google Nano Banana API Comparison and Image Quality Analysis The evolution of lightweight image generation models has introduced increasingly capable systems for fast and flexible visual creation. Within the Google Nano Banana API ecosystem, both Nano Banana 2 and Nano Banana Pro represent two distinct approaches to balancing efficiency and image quality. Through the Nano Banana API, developers can generate images using structured prompts and integrate AI image generation directly into applications. While the earlier released Google Nano Banana Pro introduces improved realism, stronger prompt interpretation, and enhanced visual consistency, Nano Banana 2 AI focuses on speed and scalable generation. In this comparison, we evaluate Nano Banana Pro vs Nano Banana 2 using controlled examples to understand differences in prompt alignment, photorealism, and output quality. ## What is Nano Banana 2? Nano Banana 2 (Gemini 3.1 Flash Image) is an updated version of the base model, available through the Nano Banana 2 API , designed to improve baseline image quality while maintaining efficiency. It remains optimized for scalable workflows, making it suitable for applications where Nano Banana API cost and generation speed are key considerations. ## What is Nano Banana Pro? Nano Banana Pro (Gemini 3 Pro Image) is the higher-tier model within the Nano Banana family, accessible through the Nano Banana Pro API . It is designed to produce higher fidelity images with improved prompt adherence and visual realism. Developers can access the model using a Nano Banana Pro API, enabling integration into production workflows that require higher quality outputs. In many cases, Google Nano Banana Pro images demonstrate clearer detail and stronger composition compared to lower-tier models. ## Model Similarities State-of-the-Art Performance Both models achieve strong performance across internal and benchmark evaluations , demonstrating competitive image generation quality. Real-World Knowledge Both Nano Banana 2 AI and Nano Banana Pro incorporate real-world knowledge , allowing more accurate object representation and contextual scene understanding. High-Resolution Output Both models support image generation up to 4K resolution , making them suitable for high-quality and production-ready visual outputs. Image Edit Both models support I2I tasks, where image references are accepted for subject consistency. ## Key Differences: Nano Banana Pro vs Nano Banana 2 While both models share similar capabilities, Nano Banana Pro introduces several improvements in output quality and prompt handling. Image Quality Nano Banana Pro produces higher fidelity images with sharper textures, improved lighting realism, and more refined visual details compared to Nano Banana 2. Prompt Adherence Nano Banana Pro demonstrates stronger prompt alignment, particularly for complex prompts involving text rendering, composition constraints, and multiple elements. Detail and Scene Complexity Nano Banana Pro handles more complex scenes more effectively, generating richer details and more coherent compositions, while Nano Banana 2 performs better in simpler scenarios. Text Rendering Accuracy Although both models support multilingual text, Nano Banana Pro shows higher accuracy and consistency in rendering text, especially in challenging cases such as mixed-language prompts. Generation Efficiency Nano Banana 2 is more optimized for speed and cost efficiency, making it better suited for high-volume generation workflows, while Nano Banana Pro prioritizes output quality over speed. ## Nano Banana API Pricing and Developer Considerations When integrating these models, developers should consider Google Nano Banana API pricing and usage patterns. Pricing for the Nano Banana API generally depends on: - Resolution - Model tier (Nano Banana 2 vs Nano Banana Pro) Because Nano Banana API cost is optimized for scalability, it is suitable for high-volume applications. However, Nano Banana Pro API price is higher due to improved output quality. Developers exploring Nano Banana Pro API documentation and Nano Banana 2 API documentation will typically find details on request structure, authentication, and pricing tiers. A common question is “ Does Nano Banana have an API?” - both Nano Banana 2 and Pro are fully accessible via API-based workflows. ## Evaluation: How We Compare Nano Banana Pro vs Nano Banana 2 To ensure a fair comparison, both Nano Banana 2 and Nano Banana Pro were evaluated under identical generation conditions using the same prompts using the Labelbox framework .The evaluation focuses on four dimensions: 1. Prompt alignment: how accurately the model follows instructions 2. Photorealism : how realistic the generated image appears 3. Detail: level of texture, clarity, and visual richness 4. Artifacts: presence of distortions, inconsistencies, or errors Each example isolates model behavior by keeping prompts consistent across both models. ## Image Comparison: Nano Banana Pro vs Nano Banana 2 ## Example 1: Food Photography Scene For the first example, we evaluate texture detail, lighting realism, and material rendering, particularly how well the model captures food textures and subtle lighting conditions. Nano Banana Pro Output Nano Banana 2 Output Prompt: Premium food photography style, a bowl of ramen with rich broth, soft-boiled egg, sliced pork, and green onions, placed on a wooden table in a Japanese restaurant setting, steam rising from the bowl, warm ambient lighting, close-up composition with shallow depth of field. Analysis: Both models perform strongly, demonstrating high photorealism, rich texture detail, and accurate lighting. The food elements are well rendered in both outputs, with no visible artifacts, resulting in overall comparable performance. ## Example 2: Human Portrait with Emotion Now, we assess facial realism, expression accuracy, and skin detail, as well as how naturally the model renders human subjects under controlled lighting. Nano Banana Pro Output Nano Banana 2 Output Prompt: Cinematic portrait photography style, a young woman in a white blazer standing in a modern office, natural window lighting casting soft shadows, subtle smile with confident expression, shallow depth of field, medium close-up composition. Analysis: Both models perform strongly, demonstrating high photorealism, rich texture detail, and accurate lighting. The food elements are well rendered in both outputs, with no visible artifacts, resulting in overall comparable performance. ## Example 3: Complex Multi-Object Scene with Text Nano Banana Pro Output Nano Banana 2 Output Prompt: Commercial advertising style, a modern sneaker placed on a concrete floor surrounded by scattered accessories, neon lighting in the background, bold text "LIMITED DROP" displayed clearly, dynamic angle composition with dramatic shadows. Analysis: Both models follow the prompt reasonably well, but Nano Banana Pro shows slight errors in text rendering. In terms of overall visual quality, Nano Banana 2 appears more visually appealing, with a more natural distribution of scattered objects and stronger scene composition. No significant artifacts are observed in either output. ## Final Thoughts: Nano Banana Pro vs Nano Banana 2 Across the extended set of examples, both Nano Banana 2 and Nano Banana Pro demonstrate strong baseline capabilities in prompt alignment, photorealism, and overall image quality. In simpler scenarios such as food and portrait generation, the two models often produce comparable results, with both delivering detailed outputs and minimal artifacts. However, differences become more apparent in more complex tasks. Nano Banana Pro generally performs better in prompt interpretation and structured outputs, particularly in cases involving text rendering and controlled compositions. At the same time, Nano Banana 2 can produce more visually balanced and aesthetically pleasing results in certain scenes, especially where object distribution and composition play a larger role. Nano Banana 2 remains a strong choice for efficient, high-volume workflows, while Nano Banana Pro provides improved precision and consistency for more demanding generation tasks. Ultimately, the choice between Nano Banana Pro vs Nano Banana 2 depends on workflow priorities: whether the focus is on efficiency and visual balance, or on higher accuracy and stronger prompt adherence. Start testing both models and get your Nano Banana API Key and Nano Banana Pro API Key Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Seedream 5.0 API Guide: How to Use + Quick Start Example Explore the Seedream 5.0 API by ByteDance. Learn how this AI image generation model works, how to write effective prompts, and how to integrate the Seedream API for scalable image automation. The Seedream 5.0 API lets you generate high-quality images from text prompts in seconds. Among the latest developments in this space is Seedream 5.0 Lite , an advanced Seedream 5.0 AI model designed to deliver fast, accurate, and visually consistent Seedream image generation across a wide range of applications. Through the Seedream 5.0 API , users can seamlessly integrate image generation capabilities into their workflows, enabling the creation of rich visual content at scale. As a powerful Seedream AI tool, it supports use cases such as content creation, design prototyping, and visual automation.In this Seedream developer guide, we explore the key capabilities of Seedream 5.0, its core features, and how to use the Seedream API integration to generate high-quality images efficiently. Whether you are a developer, creator, or business, this guide shows you how to use the API, with a simple example and step-by-step setup. ## What is Seedream 5.0 API? The Seedream API is a flexible interface for real-time image generation using the Seedream 5.0 image API. Powered by the Seedream 5.0 model, it transforms structured prompts into high-quality visuals with strong consistency and control.As a modern Seedream AI API, it supports a wide range of use cases, including marketing content, design workflows, and creative production, making it a scalable and reliable solution for both developers and creators. ## Key Features of Seedream 5.0 The Seedream 5.0 features are designed to deliver advanced control, accuracy, and scalability across a wide range of image generation use cases: ## Advanced Visual Reasoning Seedream 5.0 incorporates multi-step reasoning to generate outputs that align with real-world logic and physical consistency. This improves accuracy in complex scenes and structured compositions. ## Real-Time Context Awareness The model can leverage up-to-date information and trends, enabling more relevant outputs for time-sensitive or context-driven generation tasks. ## Extensive World Knowledge Built on a broad knowledge base across technical and creative domains, the Seedream 5.0 AI model supports diverse prompts spanning multiple industries and subjects. ## Information Visualization Capabilities Seedream 5.0 can generate structured visuals such as diagrams and labeled technical illustrations, making it useful for educational and professional content. ## Intelligent Image Editing Beyond generation, the Seedream AI API supports image editing based on natural language instructions, allowing users to refine and modify outputs efficiently. ## Multi-Subject Generation The model can render multiple objects within a single image while maintaining consistent attributes defined in the Seedream prompt , improving composition accuracy. ## Improved Prompt Alignment With advancements in evaluation benchmarks, Seedream 5.0 demonstrates stronger prompt following and alignment, resulting in outputs that better match user intent. ## Multimodal Capabilities Seedream 5.0 supports multimodal inputs, enabling more flexible workflows that combine different types of data and instructions. ## Precise Instruction Control The model enables fine-grained control over outputs, including: 1. Multiple subjects 2. Detailed attributes 3. Complex or unconventional logic This allows for more precise and customized image generation. ## How the Seedream 5.0 API Works ## Step 1: Create a Seedream Prompt The process begins with defining a structured Seedream prompt that describes the desired image. Effective prompts typically include: 1. Subject or main focus 2. Environment or setting 3. Style and visual tone 4. Lighting and atmosphere 5. Composition or perspective A well-structured prompt improves the accuracy, consistency, and quality of Seedream image generation. Clear and detailed inputs help the Seedream 5.0 AI model produce more reliable and visually coherent results. For best results, users can refer to Seedream prompt practices to better structure and refine their prompts. ## Step 2: Submit the Request to the Seedream API The prompt and configuration settings are sent to the Seedream AI API through a standard API request, following the structure defined in the documentation. Users can include: 1. Prompt description 2. Style or generation parameters 3. Output specifications This allows the Seedream 5.0 API to efficiently process requests while adapting to different visual requirements and use cases. ## Step 3: Generate the Image Output Once the request is received, the Seedream workflow processes the input using the Seedream 5.0 AI model and generates an image based on the provided instructions. The output can then be: 1. Displayed in applications 2. Used in creative content 3. Integrated into automated workflows This streamlined process makes Seedream 5.0 a scalable and efficient solution for Seedream AI automation and high-quality image generation. ## Example 1: Character Design ## Prompt: A cyberpunk female warrior with neon blue hair, glowing armor with intricate details, standing in a rainy futuristic city alley, neon signs reflecting on wet pavement, cinematic lighting, shallow depth of field, ultra-detailed, 4K, dramatic atmosphere, dynamic pose Seedream 5.0 Output ## Output Evaluation The output demonstrates Seedream 5.0’s ability to generate detailed, cinematic character scenes with strong visual consistency. Lighting is dynamic and realistic, with neon reflections and wet surfaces enhancing the atmosphere.The character remains sharp and cohesive, with clear facial details and consistent integration of glowing elements. The composition is well-balanced, keeping the subject prominent while maintaining a rich background.Overall, this highlights the Seedream 5.0 AI model’s strength in prompt adherence, scene composition, and high-quality visual output. ## Example 2: Product / Marketing Visual ## Prompt: A luxury perfume bottle placed on a reflective glass surface, soft studio lighting with elegant shadows, minimal background, premium product photography style, ultra-detailed, 4K, high-end aesthetic, sharp focus, subtle reflections Seedream 5.0 Output ## Output Evaluation The output demonstrates Seedream 5.0’s ability to generate clean and high-quality product visuals with strong realism. Lighting is soft and well-balanced, creating natural highlights and reflections that enhance the premium look of the bottle. The composition is minimal and focused, keeping the product as the central subject while maintaining clarity and sharp detail. Reflections on the surface are smooth and controlled, contributing to a polished and professional appearance. Overall, this highlights the Seedream 5.0 AI model’s strength in product-focused image generation, with strong prompt adherence and commercially usable visual quality. ## Example 3: Nature / Cinematic Environment ## Prompt: A serene mountain lake at sunrise, soft golden light reflecting on the water, mist floating above the surface, surrounded by pine trees, ultra-realistic, cinematic composition, high detail, calm atmosphere, 4K, wide-angle shot Seedream 5.0 Output ## Output Evaluation The output demonstrates Seedream 5.0’s ability to generate realistic natural environments with strong lighting and atmospheric depth. The sunrise lighting is soft and well-balanced, with natural reflections on the water enhancing the overall scene. Elements such as mist, distant mountains, and tree silhouettes are rendered with clarity, creating a sense of depth and immersion. The composition remains balanced and cohesive, with smooth transitions between foreground and background.Overall, this highlights the Seedream 5.0 AI model’s strength in producing visually rich and serene environments with high realism and consistency. ## Example 4: Information Visualization / Diagram ## Prompt: A clean labeled diagram of the human heart, showing internal structure and blood flow, educational style, clear annotations, minimal design, soft color palette, high clarity, vector-style illustration, white background Seedream 5.0 Output ## Output Evaluation The output demonstrates Seedream 5.0’s ability to generate clear and structured informational visuals with accurate labeling. The diagram is well-organized, with distinct sections and readable annotations that enhance understanding.The use of color and layout helps differentiate components effectively, while maintaining a clean and minimal design. Visual elements are consistent and easy to follow, supporting both clarity and educational use.Overall, this highlights the Seedream 5.0 AI model’s strength in information visualization, with strong prompt adherence and the ability to produce structured, high-clarity diagrams. ## Example 5: Multi-Subject / Complex Scene ## Prompt: A group of three astronauts exploring an alien jungle, glowing bioluminescent plants, misty atmosphere, cinematic lighting, each astronaut wearing a different colored suit (red, blue, and white), ultra-detailed, high realism, wide-angle composition Seedream 5.0 Output ## Output Evaluation The output demonstrates Seedream 5.0’s ability to generate complex multi-subject scenes with strong consistency and detail. Each astronaut is clearly defined, with distinct suit colors and accurate attributes maintained across all subjects. The environment is rich and immersive, with bioluminescent plants, mist, and lighting working together to create depth and atmosphere. The composition remains balanced, ensuring all subjects are visible while maintaining a cohesive scene. Overall, this highlights the Seedream 5.0 AI model’s strength in handling multi-subject generation, attribute consistency, and detailed scene composition. ## Seedream 5.0 API Pricing Understanding pricing is essential when evaluating any AI API. The Seedream 5.0 API is designed to offer scalable pricing based on usage, making it suitable for both small projects and large-scale applications. For more details, users can refer to the Seedream API documentation . Below is a sample pricing structure: ## Conclusion Seedream 5.0 provides a powerful and flexible solution for high-quality image generation across a wide range of use cases. With strong prompt adherence, consistent visual output, and support for complex scenes, it enables users to create detailed and production-ready visuals efficiently. From character design and product imagery to realistic environments and structured diagrams, the Seedream 5.0 API demonstrates versatility across both creative and practical applications. Its scalable performance and simple integration make it suitable for developers, creators, and businesses alike. Overall, the Seedream 5.0 AI model stands out as a reliable and capable tool for modern visual content generation. If you are deciding between the two available tiers, compare their tested outputs, pricing, and resolution options in our Seedream 5 Pro vs Seedream 5 Lite comparison . Start testing the model and get your Seedream 5.0 API Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## GPT Image 1 vs 1.5 Pricing: Cost Per Image Compared (2026) Compare GPT Image 1.5 vs GPT Image 1 with insights on image quality, prompt adherence, generation stability, and API pricing. The release of GPT Image 1 introduced a fast and efficient image generation model designed for flexible visual creation. OpenAI later introduced GPT Image 1.5 , an upgraded version of the model that focuses on improved visual fidelity, stronger prompt understanding, and more consistent generation behaviour. Both models are accessible through API implementations, allowing developers to generate images using simple prompt based workflows. In this comparison, we evaluate GPT Image 1.5 vs GPT Image 1 to understand the key differences in image quality, prompt responsiveness, and generation behaviour. We also explore GPT Image API pricing, prompt usage, and practical examples to help developers decide which model best fits their workflows. ## What is GPT Image 1 API? The GPT Image 1 API provides developers with access to OpenAI’s image generation model designed for rapid text to image creation. Using a structured prompt, developers can generate images that reflect the scene, subject, and visual style described in the input. The base GPT Image 1 API focuses on efficiency and accessibility, making it suitable for applications that require high volume image generation with relatively low latency. Because the model prioritizes speed and performance, it is well suited for scalable workflows and real time use cases. ## What is GPT Image 1.5? GPT Image 1.5 is the upgraded version of the base model and is designed to produce higher fidelity images with improved prompt interpretation. Available through the GPT Image 1.5 API , the model focuses on more refined image composition and stronger adherence to structured prompts. Compared to the standard model, GPT Image 1.5 aims to deliver sharper visual details, improved lighting realism, stronger prompt understanding, and more stable generation behaviour. ## Model Similarities and Differences Despite being different model tiers, GPT Image 1 and GPT Image 1.5 share several core capabilities. Both models are designed to generate images from structured prompts, support image editing, and can be integrated into applications through programmatic API workflows. However, GPT Image 1.5 introduces several improvements: ## Image Quality The most noticeable difference between GPT Image 1 vs GPT Image 1.5 is image fidelity. GPT Image 1.5 typically produces sharper textures, improved lighting consistency, and more refined object details compared to the base model. ## Prompt Adherence GPT Image 1.5 demonstrates stronger prompt interpretation, particularly for complex prompts involving multiple objects or detailed scene descriptions. This results in images that more closely match the intended prompt. GPT Image 1.5 also shows improved contextual understanding, making it suitable for more diverse and detailed tasks. ## Generation Stability The GPT Image 1.5 API focuses on improved generation stability, reducing visual inconsistencies and artifacts during image creation. ## Speed and Cost Efficiency The base GPT Image 1 API prioritizes fast image generation and lower cost, making it more suitable for high volume generation workflows. GPT Image 1.5, while producing higher quality images, may require slightly more generation time. ## GPT Image API Pricing Developers integrating the model into applications should consider GPT Image API pricing when choosing between GPT Image 1 and GPT Image 1.5. Pricing typically varies depending on usage and configuration. For GPT Image 1, based on PiAPI 's price chart, GPT Image 1.5 follows a similar pricing structure, with costs depending on resolution and quality settings, though higher fidelity outputs may result in slightly higher overall usage costs depending on the workflow.The GPT Image API pricing structure usually depends on: - Model tier used for generation - Number of generated images - Image resolution and quality settings For more details on implementation and usage, developers can refer to the official API documentation. ## How to Use GPT Image Models Many developers want to understand the typical workflow for generating images using GPT Image 1.5 or GPT Image 1. The process generally follows three steps: ## Step 1: Obtain an API Key Access to the model requires authentication through an GPT Image 1.5 API key, which allows developers to send requests to the GPT Image API. ## Step 2: Write a Structured Prompt A clear and structured prompt helps guide the model toward the desired output. Following best practices improves output quality and consistency. ## Step 3: Generate Images Through the API After submitting the prompt through the API, the model generates images that match the prompt description. Developers can then use these images within applications or automated workflows. ## Prompt Guide and Examples Writing strong prompts significantly improves the quality of generated images. A well structured prompt typically includes the following: background/scene → subject → key details → constraints. To demonstrate the capabilities of the model, developers often test both GPT Image 1 and GPT Image 1.5 using identical prompts. This allows for a direct comparison of output quality, prompt adherence, and generation consistency across both models. You can refer to a detailed GPT Image 1.5 prompt guide to learn how to structure prompts for more accurate and consistent image generation. We will also evaluate the results using the Labelbox framework to assess prompt adherence, visual quality, composition, and generation consistency. ## Example 1: Cinematic Scene In this example, we will start with a cinematic scene to see how well both models perform against each other. ## Prompt Used "A cinematic, ultra realistic scene of a futuristic city at sunset, with neon lights reflecting off glass skyscrapers. Flying cars move through the air in organized traffic, while pedestrians walk along a busy street filled with digital billboards. Warm golden sunlight mixes with cool neon lighting, creating strong contrast and reflections. Wide angle composition, highly detailed, sharp focus, depth of field, 8k quality." GPT Image 1 Ouput GPT Image 1.5 Output Analysis: Using a Labelbox evaluation framework, we assess prompt adherence, visual quality, composition, and generation consistency. GPT Image 1 captures the overall scene well, including the futuristic buildings, flying vehicles, and neon lighting. However, details appear softer, reflections are less realistic, and lighting lacks precision, which reduces depth and realism. GPT Image 1.5 performs better across all criteria, with sharper textures, more accurate lighting, and stronger reflections. The composition feels more balanced, and the scene is more cohesive with fewer visual inconsistencies.In this example, we test how both models handle human subjects, focusing on realism, lighting, and facial detail. ## Example 2: Portrait Scene In this example, we test how both models handle human subjects, focusing on realism, lighting, and facial detail. ## Prompt Used "A photorealistic portrait of a young woman sitting in a cozy cafe, soft natural window light illuminating her face. She is holding a cup of coffee, with shallow depth of field and blurred background. Skin texture is detailed and natural, with realistic lighting and shadows. Shot on a 50mm lens, cinematic composition, high detail." GPT Image 1 Output GPT Image 1.5 Output Analysis: Using the same framework, GPT Image 1 produces a portrait that matches the prompt, but facial details appear slightly soft with less natural skin texture. Lighting is present but lacks subtle transitions, and depth of field is less convincing.GPT Image 1.5 delivers a more realistic result, with sharper facial features, natural skin tones, and improved lighting. The subject stands out more clearly, creating a more polished and life like image. In this example, we test how both models handle multiple objects, fine details, and structured composition within a single scene. ## Example 3: Complex Object Scene In this example, we test how both models handle multiple objects, fine details, and structured composition within a single scene. ## Prompt Used "A modern workspace desk setup with a laptop, mechanical keyboard, smartphone, notebook, and a cup of coffee. The desk is made of wood, with warm ambient lighting from a desk lamp. A large monitor displays code on the screen, and there are small decorative items like a plant and headphones. Clean composition, highly detailed, realistic textures, soft shadows, 4k quality." GPT Image 1 Output GPT Image 1.5 Output Analysis: Analysis: For multi-object evaluation, GPT Image 1 correctly places key elements in the scene, showing good prompt adherence. However, smaller details lack clarity, and textures appear less defined, making the overall image feel slightly flat. GPT Image 1.5 produces a cleaner and more detailed result, with sharper objects, better material textures, and more accurate lighting. The scene appears more structured, consistent, and closer to a real workspace. ## Final Thoughts: GPT Image 1.5 vs GPT Image 1 Across the three examples, both GPT Image 1 and GPT Image 1.5 demonstrate strong prompt alignment and the ability to generate visually appealing images across different scenarios. In simpler scenes, the two models often produce comparable results, with both delivering accurate compositions and minimal visual inconsistencies.However, GPT Image 1.5 consistently shows stronger performance in visual detail, realism, and prompt interpretation. It handles complex scenes more effectively, particularly in areas such as lighting, reflections, and fine textures. In both the cinematic and workspace examples, GPT Image 1.5 produced sharper, more cohesive images, while GPT Image 1 outputs appeared slightly softer with less refined details. For developers evaluating GPT Image 1.5 vs GPT Image 1, the choice ultimately depends on workflow priorities: faster generation and lower cost, or higher fidelity outputs with improved realism and consistency. Start testing both models and get your GPT Image 1.5 and GPT Image 1 Key via PiAPI today!Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Nano Banana 2 API (2026): Docs, Pricing, Integration & Code Examples Use Nano Banana 2 API with ready-to-use code examples, request formats, and integration steps. Quick developer guide to start building with PiAPI in minutes. ## Introduction The rapid advancement of AI has introduced a new generation of models capable of generating high-quality visual content with increasing control and precision. Among the latest innovations in this space is Nano Banana 2 , the successor to Nano Banana and Nano Banana Pro , designed to deliver fast, accurate, and visually consistent image generation across a wide range of applications.Through the Nano Banana 2 API , developers and businesses can seamlessly integrate image generation capabilities into their systems, enabling the creation of rich visual content for various use cases. As a powerful AI tool, it supports applications such as content creation, design prototyping, and visual storytelling.In this guide, we explore the capabilities of Nano Banana 2, its key features, and how users can leverage the API to generate high-quality images. Whether you are a developer or a creator working with AI-generated visuals, this overview will help you understand how the model works and how to integrate it effectively. ## What is Nano Banana 2? Released by Google in February 2026, Nano Banana 2 is an advanced image generation model that creates high-quality visuals from structured prompts. It delivers improved control, consistency, and visual detail, allowing users to generate, edit, and enhance images for use cases such as marketing, design, and creative storytelling. ## Key Features of Nano Banana 2 AI Unified Image Generation Architecture Nano Banana 2 is built on a unified system that processes structured prompts, visual inputs, and contextual instructions within a single generation pipeline. This allows the model to produce high-quality images with consistent composition and visual coherence. High-Speed Generation Performance The Nano Banana 2 API is optimized for speed, enabling fast image generation with near real-time responses. This supports efficient workflows for applications that require rapid visual output. Advanced Image Generation Capabilities Nano Banana 2 enables users to generate detailed and visually rich images from structured prompts. It supports a wide range of creative scenarios, including cinematic scenes, stylized visuals, and realistic environments, with improved accuracy and consistency. Developer-Friendly Integration The Nano Banana API is designed for easy integration into applications. With simple request and response structures, developers can quickly incorporate image generation capabilities into their systems. Scalable Infrastructure Nano Banana 2 supports scalable deployment, making it suitable for both individual creators and large-scale applications. This allows users to generate images efficiently without compromising performance. Customizable Generation Parameters The Nano Banana 2 API provides configurable parameters that allow users to control outputs based on their needs. This includes refining prompts, adjusting styles, and optimizing visual results. Reliable and Consistent Output The model is designed to produce stable and consistent visual outputs across different prompts. This ensures higher reliability when used in production environments. ## How the Nano Banana 2 API Works Step 1: Create a Nano Banana Prompt The process begins with defining a structured prompt that describes the desired image. Effective prompts typically include: 1. Subject or main focus 2. Environment or setting 3. Style and visual tone 4. Lighting and atmosphere 5. Composition or perspective A well-structured prompt helps produce more accurate, consistent, and visually coherent results. For best results, refer to these prompt design best practices . Step 2: Submit the Request to the Nano Banana API The prompt and configuration settings are sent to the Nano Banana API through a standard API request, following the request structure and parameters defined in the API documentation . Developers can include: 1. Prompt description 2. Style or generation parameters 3. Output specifications 4. This allows the Nano Banana 2 API to generate images efficiently while adapting to different visual requirements. Step 3: Generate the Image Output Once the request is received, Nano Banana 2 processes the input and generates an image based on the provided instructions.The output can then be: 1. Displayed in applications 2. Used in creative content 3. Integrated into design workflows This streamlined process makes Nano Banana 2 a scalable and efficient solution for generating high-quality visuals. ## Example 1: Scenic Environment Nano Banana 2 Output Prompt: "Generate a vivid and immersive scene of a peaceful lakeside at sunrise. The setting should include soft golden light reflecting on the water, gentle mist rising from the lake, and distant mountains partially covered in fog. Describe the environment in rich detail, including sounds, atmosphere, and subtle movements such as rippling water and rustling leaves. The tone should be calm, cinematic, and emotionally soothing, allowing the viewer to feel fully present in the scene. " Output Evaluation The output demonstrates Nano Banana 2’s ability to generate calm, cinematic natural scenes with high visual clarity. Lighting is soft and realistic, with smooth reflections on the water and well-rendered atmospheric elements such as mist and depth in the background.The composition remains balanced and cohesive, with natural details like foliage and textures enhancing realism. Overall, this highlights the model’s strength in producing serene and visually immersive environments. ## Example 2: Urban Night Scene Nano Banana 2 Output Prompt: "Generate a cinematic nighttime city scene with neon lights reflecting on wet streets after rain. The environment should include tall buildings, glowing signboards, and light traffic moving through the streets. Add subtle motion such as passing cars, reflections on puddles, and flickering lights. The atmosphere should feel vibrant, modern, and slightly moody, with a strong contrast between light and shadow." Output Evaluation The output demonstrates Nano Banana 2’s ability to generate detailed urban environments with strong cinematic quality. Neon lighting and wet street reflections are rendered realistically, creating a vibrant and immersive atmosphere.The scene maintains strong structural consistency despite its complexity, with clear building details, signage, and traffic elements. Subtle motion from vehicles and light trails adds energy without reducing clarity.Overall, this example highlights the model’s strength in producing visually rich and dynamic city scenes with consistent lighting and composition. ## Example 3: Image Editing Input Prompt: "Transform the scene into a cinematic ocean environment. Replace the fishbowl setting with a vast underwater scene, including coral reefs and soft light rays passing through the water. Ensure the fish remains the main subject, preserving its position and motion while enhancing surrounding details with realistic water textures and marine elements." Nano Banana 2 Output Output Evaluation The output demonstrates Nano Banana 2’s ability to transform a simple scene into a complex underwater environment while preserving the main subject. The fish remains consistent in position and appearance, while the surrounding environment is enhanced with detailed coral, marine life, and atmospheric lighting.Light rays and depth are rendered naturally, creating a cinematic and immersive result. Overall, this highlights the model’s strength in large-scale scene transformation with strong visual coherence. ## Conclusion Nano Banana 2 demonstrates strong capabilities in both content generation and image editing, making it a versatile AI model for a wide range of creative and practical applications. From generating cinematic environments to performing controlled scene transformations, the model delivers visually consistent and high-quality outputs across different use cases.Its ability to preserve key subjects while enhancing or modifying surrounding elements highlights a high level of control and reliability. Combined with flexible API integration and scalable performance, the Nano Banana 2 API provides developers and creators with an efficient solution for building intelligent workflows. Compared to the previous models, Nano Banana 2 introduces improved consistency, control, and overall output quality.Overall, Nano Banana 2 stands out as a powerful AI automation tool that balances creativity, precision, and usability, making it well-suited for both experimentation and production-level deployment. Start testing the model and get your Nano Banana 2 API Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Nano Banana vs Nano Banana Pro: Google Nano Banana API Comparison and Pricing Guide Compare Nano Banana vs Nano Banana Pro through the Google Nano Banana API. Learn about Nano Banana API pricing, prompt examples, and how to use Nano Banana Pro. The release of Nano Banana (Gemini 2.5 Flash Image) introduced a lightweight image generation model designed for fast and flexible visual creation. Google later introduced Nano Banana Pro (Gemini 3 Pro Image), an upgraded version of the model that focuses on improved visual fidelity, studio-quality precision, and enhanced generation stability. Both models are accessible through API implementations, allowing developers to generate images using simple prompt-based workflows. In this comparison, we evaluate Nano Banana Pro vs Nano Banana to understand the key differences in image quality, prompt responsiveness, and generation behavior. We also explore Nano Banana API pricing, prompt usage, and practical examples to help developers decide which model best fits their workflows. ## What is Google Nano Banana API? The Google Nano Banana API provides developers with access to Google's image generation model designed for rapid text-to-image generation. Using a Nano Banana prompt, developers can generate images that reflect the scene, subject, and visual style described in the prompt. The base Nano Banana API focuses on efficiency and accessibility, making it suitable for applications that require high-volume image generation with relatively low latency. Because the model prioritizes speed and cost efficiency, Nano Banana API cost is generally lower than more advanced image generation models. ## What is Nano Banana Pro? Nano Banana Pro is the upgraded version of the base model and is designed to produce higher fidelity images with improved prompt interpretation. Available through the Nano Banana Pro API , the model focuses on more refined image composition and stronger adherence to structured prompts. Compared to the standard model, Google Nano Banana Pro aims to deliver sharper visual details, improved lighting realism, stronger prompt understanding and more stable generation behaviour. ## Model Similarities and Differences Despite being different model tiers, Nano Banana and Nano Banana Pro share several core capabilities. Both models are designed to generate images from structured prompts, support image editing and can be integrated into applications through programmatic API workflows. However, Nano Banana Pro introduces several improvements: Image Quality The most noticeable difference between Nano Banana vs Nano Banana Pro is image fidelity. Nano Banana Pro typically produces sharper textures, improved lighting consistency, and more refined object details compared to the base model. Prompt Adherence Nano Banana Pro demonstrates stronger prompt interpretation, particularly for complex prompts involving multiple objects or detailed scene descriptions. This results in images that more closely match the intended prompt. Also, Nano Banana Pro features real world knowledge, making it suitable for more extensive and diverse tasks. Generation Stability The Nano Banana Pro API focuses on improved generation stability, reducing visual inconsistencies and artifacts during image creation. Speed and Cost Efficiency The base Nano Banana API prioritizes fast image generation and lower Nano Banana API cost, making it more suitable for high-volume generation workflows. Nano Banana Pro, while producing higher quality images, may require slightly more generation time. ## Nano Banana API Pricing Developers integrating the model into applications should consider Google Nano Banana API pricing when choosing between the base model and the Pro version. The price offered at PiAPI starts at $0.03 per image. The Nano Banana API pricing structure typically varies depending on: 1. Model tier used for generation 2. Number of generated images 3. Image resolution ## How to Use Nano Banana Many developers search on how to use Nano Banana Pro and the older flash version want to understand the typical workflow for generating images. The process generally follows three steps: Step 1: Obtain a Nano Banana Pro API Key Access to the model requires authentication through a Nano Banana Pro API key , which allows developers to send requests to the Nano Banana Pro API. Step 2: Write a Nano Banana Pro Prompt A clear and structured Nano Banana Pro prompt helps guide the model toward the desired output with reference from the Google Nano Banana API documentation . Step 3: Generate Images Through the API After submitting the prompt through the Nano Banana Pro API, the model generates images that match the prompt description. Developers can then use these images within applications or automated workflows. ## Nano Banana Prompt Guide and Examples Writing strong Nano Banana prompts significantly improves the quality of generated images. According to Google DeepMind , A well-structured Nano Banana prompt typically includes the following: Style + Subject + Setting + Action + Composition To demonstrate the capabilities of the model, we generated several Nano Banana Pro examples using the same prompts as the base model. We will be evaluating the output using the Labelbox framework . ## Example 1: Cinematic Scene In this example, we will start with a cinematic scene to see how well both models perform against each other. Nano Banana Output Nano Banana Pro Output Prompt: Cinematic photography style, a traveler standing on an icy mountain ridge at sunset, surrounded by dramatic clouds and warm golden light, the traveler gazing toward the horizon, wide-angle composition with the subject positioned on the ridge against the glowing sky. Analysis: Both models show strong prompt alignment, though Nano Banana Pro introduces additional elements such as smoke from the mountain that were not specified in the prompt. In terms of photorealism, Nano Banana appears slightly more realistic. Both outputs demonstrate high detail, and no visible artifacts are observed. ## Example 2: Product Photography with Multilingual Text Rendering In this example, we will evaluate on a product photography with textual addition to the prompt. Nano Banana Output Nano Banana Pro Output Prompt: Premium product photography style, a modern laptop displaying the words "Buy Now! 50% OFF! 新品电脑", placed on a reflective studio surface, softly illuminated by professional studio lighting, centered product composition with dramatic reflections and clean commercial framing. Analysis: Nano Banana Pro shows stronger prompt alignment, correctly rendering the Chinese text, while Nano Banana fails to reproduce the text accurately. In terms of photorealism, Nano Banana Pro produces a more convincing product photography scene with realistic lighting. The detail is also higher in Nano Banana Pro, showing visible apps on the laptop screen, whereas Nano Banana displays a blank screen. For artifacts, Nano Banana contains errors in text rendering, while Nano Banana Pro shows no visible artifacts. ## Example 3: Futuristic City Scene For our final example, we review a scene that is sci-fi and futuristic, to see the models' intepretation of abstract ideas. Nano Banana Output Nano Banana Pro Output Prompt : Cyberpunk cinematic style, a futuristic city skyline with towering neon-lit skyscrapers, set at night with rain-soaked streets reflecting colorful lights, neon signs glowing and flickering across the buildings, wide-angle cityscape composition with reflections stretching across the wet pavement. Analysis: Both models show strong prompt alignment, though the wet street reflections appear only in the Nano Banana Pro output. Both demonstrate high photorealism, but Nano Banana Pro provides slightly richer detail. No visible artifacts are observed in both results. ## Final Thoughts: Nano Banana vs Nano Banana Pro Across the three examples, both Nano Banana and Nano Banana Pro demonstrate strong prompt alignment and high photorealism in most scenarios. In simpler scenes, the two models often produce comparable results, with both generating detailed images and minimal visual artifacts. However, Nano Banana Pro generally shows stronger performance in prompt interpretation and visual detail. It handles more complex instructions more accurately, particularly in cases involving text rendering and environmental elements such as reflections. In the product photography example, Nano Banana Pro correctly rendered the Chinese text and produced a more realistic commercial-style image, while the base Nano Banana model showed errors in text rendering. For developers evaluating Nano Banana vs Nano Banana Pro, the choice ultimately depends on workflow priorities: faster generation and lower cost, or higher fidelity image outputs and improved prompt responsiveness. Start testing both models and get your Nano Banana API Key and Nano Banana Pro API Key Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Seedance 2.0 API Guide: ByteDance AI Video Model and Prompt Examples Explore the Seedance 2.0 API by ByteDance. Learn how the Seedance AI video generator works, how to write Seedance prompts, and how to integrate the Seedance AI API. The rapid progress of generative video AI has introduced a new generation of models capable of producing cinematic video directly from prompts. Among the latest entrants in this space is Seedance 2.0 , a multimodal ByteDance AI video model designed to generate high-quality video from text, images, audio, and reference video. Through the Seedance 2.0 API , developers and creators can integrate advanced video generation into applications and media workflows. In this guide, we explore the capabilities of ByteDance Seedance 2.0, the features of the Seedance AI API, and how creators can generate cinematic outputs using structured Seedance prompts. ## What is ByteDance Seedance 2.0? Released in February 2026, Seedance 2.0 is part of ByteDance's effort to develop advanced multimodal video generation models capable of producing realistic cinematic sequences from structured inputs. It succeeds previous Seed foundational models such as the Seedream 1.0 . This design allows Seedance 2.0 AI to coordinate information from different modalities including text, images, and reference video. By combining these signals within a single generation pipeline, the model can maintain scene consistency and motion coherence across frames. Within the Seedance AI API, this multimodal capability enables several common video generation workflows: 1. Text-to-Video (T2V) generation from written prompts 2. Image-to-Video (I2V) animation using reference images 3. Reference-to-Video (R2V) generation guided by existing video inputs This architecture allows developers to build flexible AI video pipelines while enabling creators to guide generation using a variety of input sources. ## Key Features of Seedance 2.0 AI Seedance AI by ByteDance is built with multiple key features. Unified Multimodal Architecture Seedance 2.0 AI supports multimodal inputs including text, images, and video. This enables flexible generation workflows such as T2V, I2V, and R2V. Exceptional Motion Stability The Seedance AI video generation produces videos with stable motion and realistic dynamics, helping scenes maintain natural movement across frames. Director-Level Control Creators can control performance, lighting, shadows, and camera movements through structured Seedance prompts, allowing precise cinematic direction. Industry Standard Output ByteDance Seedance 2.0 AI produces cinematic video designed to align with modern production standards, supporting high-quality visual outputs. Flexible Generation Configurations The Seedance AI API provides configuration options for video duration, aspect ratio, and other generation settings. Outperforming Leading Models On the SeedVideoBench-2.0 benchmark, Seedance 2.0 demonstrates strong performance across T2V, I2V, and multimodal generation tasks. ## How the Seedance 2.0 API Works The Seedance 2.0 API allows developers to generate video programmatically using structured prompts and multimodal inputs. A typical workflow includes three main steps: Step 1: Write a Seedance Prompt The process begins with writing a Seedance prompt that describes the desired scene. Effective prompts typically specify: 1. Subject of the scene 2. Environment 3. Action or motion 4. Camera movement 5. Lighting and visual style Step 2: Submit the Request to the Seedance API The prompt and generation parameters are sent to the Seedance AI API through an API request. Developers can also include multimodal inputs such as reference images, text, or reference videos to guide generation. Step 3: Generate the Video Output The Seedance AI processes the input and generates a video sequence that follows the instructions provided in the prompt. The generated video can then be downloaded or integrated into applications and media pipelines. ## Seedance Prompt Examples Clear and structured Seedance prompts help guide the video generation process and produce more consistent outputs. Below, we present five examples: three T2V generations, one I2V generation, and one R2V example demonstrating video extension. ## Example 1: Cinematic Environment Scene Seedance 2.0 Output Prompt: A cinematic aerial shot of a coastal city at sunrise. Soft golden light reflects off the ocean while waves crash against a curved shoreline. Small fishing boats move slowly across the water as the camera glides forward above the harbor. The atmosphere feels calm and peaceful with warm morning light. Seedance 2.0 produces a stable aerial scene with smooth forward camera motion. The lighting transitions and ocean reflections remain consistent across frames, while the movement of boats and waves maintains realistic motion dynamics. ## Example 2: Product Commercial Style Seedance 2.0 Output Prompt: A cinematic product advertisement of a modern smartwatch placed on a reflective glass surface. The watch slowly rotates while dramatic studio lighting highlights its metallic edges and display. Soft shadows move across the surface as the camera slowly pushes in, creating a premium commercial look. The generated video emphasizes product detail and lighting control. The rotating watch remains stable throughout the clip, while reflections and shadows maintain realistic behavior, producing a commercial-style presentation. ## Example 3: Dynamic Action Scene Seedance 2.0 Output Prompt: A futuristic motorcycle speeding through a neon-lit city at night. Rain falls onto the street while reflections of neon signs glow on the wet pavement. The camera tracks behind the rider as the bike accelerates through narrow streets, creating a fast-paced cinematic chase scene. Seedance 2.0 demonstrates strong motion stability in a fast-moving environment. The reflections on the wet road and neon lighting remain visually consistent while the camera tracking maintains a smooth cinematic chase perspective. ## Example 4: I2V Generation Reference Image Seedance 2.0 Output Prompt: A woman sings and strums her guitar on a small stage, warm lighting, cinematic composition. Seedance 2.0 successfully animates the subject while preserving the identity and stage environment from the reference image. The guitar strumming and body movement appear natural, and the warm stage lighting remains consistent throughout the clip. ## Example 5: R2V Generation Seedance 2.0 Output The R2V generation follows the video extension workflow described in the API documentation . Seedance 2.0 extends the original video by an additional 5 seconds while maintaining scene continuity and consistent camera motion. Environmental lighting and landscape details remain stable, producing a natural continuation of the original footage. ## Final Thoughts on ByteDance Seedance 2.0 The release of Seedance 2.0 marks another major step forward in the development of generative video AI. With its unified multimodal architecture, exceptional motion stability, and director-level control, ByteDance Seedance 2.0 AI provides creators and developers with a powerful tool for producing cinematic video directly from prompts. Through the Seedance 2.0 API, developers can integrate advanced video generation capabilities into applications and automated media workflows. To decide whether that premium is worthwhile for your workflow, see our H3-versus-Seedance price and performance test . Start testing the model and get your Seedance 2.0 API Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Sora 2 API Guide: Prompt Guide, Examples and Alternatives Explore the Sora 2 API for text-to-video generation. Learn its capabilities, workflow, prompts and examples for cinematic AI video generation. Breaking it down! Released in September 2025, OpenAI Sora 2 represents the next generation of large-scale video generation models designed to create realistic videos from text prompts. Developed as part of the latest wave of multimodal generative AI systems, Sora 2 expands the capabilities of text-to-video models by enabling longer sequences, improved physical realism, and more coherent scene generation. Through the OpenAI Sora 2 API , developers can integrate AI-driven video generation directly into applications, enabling workflows such as cinematic content creation, marketing video production, and automated media generation. ## What Is The Sora 2 API? The Sora 2 API allows developers to generate videos programmatically using text and image prompts. Instead of manually producing videos through traditional workflows, Sora enables automated video generation through AI. The API converts written descriptions into temporally coherent video sequences, simulating environments, objects, and camera motion. With the OpenAI Sora API, developers can build applications such as: 1. Automated video content creation tools 2. AI filmmaking platforms 3. Marketing video generators 4. Educational animation systems 5. Storytelling and media prototyping tools The second generation of the model improves scene coherence, motion realism, and overall video fidelity compared to earlier video generation models. ## Sora AI 2 Pro Some users also search for Sora AI 2 Pro, which generally refers to premium access tiers that provide more powerful generations. We offer a Sora 2 API Pro mode that delivers higher-quality video generation with improved temporal coherence, enhanced motion realism, and more stable frame consistency. ## Sora 2: How To Use The Model Many users searching for queries around how to use Sora 2 AI want to understand the basic workflow.The general process looks like this: Define the Scene Describe the environment, subjects, and actions. Example: A cinematic shot of a futuristic city skyline at sunset with flying vehicles moving between buildings. Add Motion and Camera Direction Specify how the camera moves. Example: The camera slowly pans across the skyline while neon lights illuminate the streets below. Control Atmosphere and Style Add details about lighting, mood, and environment. Example: Warm sunset lighting, cinematic depth of field, realistic reflections on glass buildings. The more structured and descriptive the prompt, the better the generated video. ## How To Prompt Sora 2 One of the most important aspects of using the OpenAI Sora 2 API is writing effective prompts. Users frequently search for queries around how to prompt Sora 2 because prompt quality directly affects the generated video. A strong Sora prompt typically includes: 1. Subject 2. Environment 3. Action 4. Camera movement 5. Lighting and style ## Sora AI 2 Prompts: Example Prompts Below are several example Sora AI 2 prompts demonstrating how the model can be used. All examples were T2V generations, following Open AI Sora 2 API documentation . ## Example 1: Cinematic City Scene Sora 2 Output Prompt: A cinematic aerial shot of a futuristic city at sunset. Flying cars move between skyscrapers while neon signs glow across the streets. The camera slowly pans across the skyline. ## Example 2: Cinematic City Scene Sora 2 Output Prompt: A slow-motion shot of waves crashing against rocky cliffs during golden hour. Sea mist rises into the air while seagulls fly overhead. ## Example 3: Cinematic City Scene Sora 2 Output Prompt: A medieval knight riding a horse through a snowy forest. Snow particles drift through the air as the camera follows from behind. ## Sora 2 AI Alternatives Although the Sora 2 API represents a major advancement in generative video models, several Sora 2 AI alternatives exist in the rapidly evolving AI video landscape. Kling AI Kling AI is designed for cinematic video generation and supports workflows such as text-to-video (T2V) and image-to-video (I2V) creation. It focuses on producing visually rich scenes with strong motion realism and structured camera control. Wan AI Video Wan AI Video emphasizes high-resolution video synthesis and stable motion generation. It is commonly used for longer sequences and production-oriented video workflows. Luma AI Luma AI specializes in realistic scene rendering and immersive visual generation. Its models are often used for creating cinematic environments and high-quality visual storytelling. ## Final Thoughts On The Sora 2 The OpenAI Sora 2 API represents a significant step forward in AI video generation. As interest in generative video continues to grow, tools like Sora will play an increasingly important role in AI-driven media creation. For developers and creators exploring how to use Sora 2 AI, understanding prompt design and workflow integration will be key to achieving high-quality results. Start testing the model and get your Sora 2 API Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## OmniHuman 1.5 vs Kling AI Avatar: Which AI Avatar Model Performs Better in 2026? Compare OmniHuman 1.5 and Kling AI Avatar across lip-sync accuracy, facial realism, motion stability, and avatar quality through controlled testing. Examining the Differences! Released around the same period as part of the latest wave of digital human AI, OmniHuman 1.5 and Kling AI Avatar both aim to generate realistic talking avatars from text, image and audio inputs. While both models focus on AI avatar generation, they differ in architectural design, motion modeling approach, and emphasis on realism versus stylization. OmniHuman 1.5 OmniHuman 1.5 is designed with a strong emphasis on multimodal coordination and motion coherence across extended sequences. The model jointly processes text, audio, and visual inputs through shared attention mechanisms, ensuring that each modality contributes to avatar generation in a coordinated manner. Notable architectural capabilities include: 1. Speech-aware gesture generation based on timing and prosody 2. Emotion-aligned animation synchronized with semantic audio cues 3. Explicit control over camera motion, character actions, and scene timing via text instructions 4. Multi-character animation within a single scene, each driven by independent audio tracks 5. Pseudo last-frame identity preservation to prevent appearance drift 6. Motion coherence and temporal stability in sequences exceeding one minute Kling AI Avatar Kling AI Avatar is built on a multimodal large language model (MLLM) framework that integrates image, audio, and text prompts into a unified generation pipeline. This enables precise alignment between visual identity, speech timing, and instruction-based control over avatar behavior. Key architectural characteristics include: 1. Unified global planning through MLLM-based processing 2. Keyframe-controlled architecture for motion structure 3. Cross-attention mechanisms for modality coordination 4. Enhanced lip-sync strategies optimized for multilingual and fast speech 5. Optimized data processing for long-duration generation In this comparison, we evaluate both AI avatar generation models with our OmniHuman 1.5 API and Kling AI Avatar API under controlled generation conditions to determine which model delivers stronger performance in practical avatar workflows. ## Model Similarity Both OmniHuman-1.5 and Kling Avatar share similar capabilities as they are designed to generate realistic talking avatars from static visual inputs, audio signals and event textual prompts. At a functional level, their core workflows overlap significantly: Image to Avatar Generation Both models convert a single image into a talking video driven by audio input with textual controls. The generated output animates the subject while preserving core identity features such as facial structure, hairstyle, and clothing, enabling scalable digital human creation without requiring recorded footage. Audio-Driven Lip Sync Both OmniHuman 1.5 AI and Kling AI Avatar feature AI avatar lip sync capabilities, automatically aligning phonemes for natural speech animation. Lip articulation accuracy, timing alignment, and mouth shape consistency are central evaluation criteria. Facial Expression Animation Beyond lip movement, both models generate dynamic facial expressions that reflect speech rhythm and tone. Subtle eyebrow, cheek, and eye movements contribute to overall realism and prevent a mechanical appearance. Head Motion Synthesis Both OmniHuman 1.5 and Kling AI Avatar produce natural head movements during speech, including nods and slight turns. Temporal smoothness and physical plausibility are key indicators of quality. Upper-Body Micro-Movements Both models incorporate subtle shoulder and posture adjustments to enhance realism. Stability and the absence of jitter are important evaluation factors. High-Resolution Video Output Both OmniHuman API and Kling AI Avatar API support high-resolution rendering suitable for production use, with emphasis on visual clarity, texture preservation, and identity stability across frames.While the high-level feature sets overlap, the two models are built on different architectural approaches, which may influence output behavior under real-world conditions. ## OmniHuman 1.5 API vs Kling AI Avatar API: Core Differences Although both models support similar AI avatar generation workflows, their architectural priorities differ in emphasis and implementation. At a high level: OmniHuman 1.5 prioritizes multimodal motion intelligence, gesture realism, and long-sequence temporal coherence. Kling AI Avatar prioritizes high-fidelity facial rendering, multilingual lip-sync precision, and globally structured multimodal planning. While these architectural distinctions provide theoretical positioning, the practical impact can only be determined through controlled evaluation. ## Evaluation: How We Compare OmniHuman 1.5 API vs Kling AI Avatar API? Since both models share overlapping workflows, this comparison focuses on output behavior rather than feature availability. All tests were conducted under controlled conditions using identical portrait images, audio inputs and textual instructions from our OmniHuman 1.5 AI API Docs and Kling Avatar API Docs . Each model was evaluated across five dimensions: 1. Lip-sync accuracy 2. Facial realism 3. Expression alignment with speech tone 4. Motion coherence and temporal stability 5. Identity preservation across continuous sequences ## Avatar Comparison: OmniHuman 1.5 vs Kling AI Avatar To ensure a fair comparison, the same portrait image is used across all three examples. The image was generated using our Nano Banana Pro API . Avatar Input ## Example 1: Neutral Speech Test We begin with a controlled neutral speech test to evaluate baseline lip-sync accuracy and facial stability. Audio Input OmniHuman 1.5 Output Kling AI Avatar Output Prompt: Generate a realistic talking avatar delivering a calm presentation while maintaining natural eye contact with the camera. Analysis: Both OmniHuman 1.5 and Kling AI Avatar produced stable talking avatars under the neutral speech test. However, Kling AI Avatar demonstrated stronger lip-sync accuracy and clearer mouth articulation throughout the clip. Phoneme alignment appeared more precise, particularly during consonant transitions and faster syllables. Facial textures and eye movement remained stable in both outputs, but Kling AI Avatar maintained slightly more natural facial dynamics. Overall, Kling AI Avatar delivered the more convincing result in this baseline lip-sync evaluation. ## Example 2: Emotional Variation Test Next, we evaluate how both models handle expressive speech and emotional transitions. Audio Input OmniHuman 1.5 Output Kling AI Avatar Output Prompt: Generate a talking avatar reacting naturally to emotional changes in the speech, including subtle smiles, emphasis, and expressive facial movement. Analysis: Both models successfully generated expressive facial movements in response to the emotional speech. Kling AI Avatar produced noticeably stronger expressions, with more pronounced eyebrow movement and facial dynamics. While this resulted in a highly expressive output, some moments appeared slightly exaggerated, approaching the boundary of natural facial behavior. OmniHuman 1.5, by contrast, produced more restrained expressions with smoother transitions between emotional states. Although the expressiveness was less pronounced, the overall motion appeared more stable and consistent. As a result, Kling AI Avatar demonstrated stronger emotional expressiveness, while OmniHuman 1.5 showed greater motion stability. ## Example 3: Stability Test To assess temporal coherence and motion stability during continuous speech, we evaluate how both models maintain consistent facial motion and identity throughout the clip. Audio Input OmniHuman 1.5 Output Kling AI Avatar Output Prompt: Generate a natural talking avatar delivering the speech while maintaining consistent facial identity and stable motion throughout the clip. Analysis: In this test, Kling AI Avatar produced a more natural overall result. The avatar generated coordinated hand movements that aligned well with the speech rhythm, creating a more convincing speaking behavior. OmniHuman 1.5 also maintained stable facial motion and identity, but the avatar occasionally introduced a subtle head tilt that appeared slightly unnatural relative to the speech delivery. Despite this minor issue, both models performed well overall, producing realistic avatars with stable rendering and no noticeable artifacts. ## Final Thoughts on OmniHuman 1.5 vs Kling AI Avatar Across the three controlled tests, both OmniHuman 1.5 and Kling AI Avatar demonstrated strong capabilities in AI avatar generation. Both models produced stable talking avatars with consistent facial rendering, accurate lip synchronization, and high visual quality. Across the three controlled tests, both OmniHuman 1.5 and Kling AI Avatar demonstrated strong capabilities in AI avatar generation, producing stable talking avatars with consistent facial rendering, accurate lip synchronization, and high visual quality. In the baseline lip-sync and motion evaluations, Kling AI Avatar showed stronger alignment between speech and avatar movement. Facial expressions and hand gestures appeared more coordinated with the audio, resulting in a more natural speaking performance. OmniHuman 1.5, by contrast, maintained stable facial rendering and smooth motion transitions, though occasional head movements appeared slightly less natural. Overall, both models deliver production-ready AI avatar generation, proving to be top AI tools for generating UGC video content or presentation content. Kling AI Avatar offers stronger motion expressiveness, while OmniHuman 1.5 provides stable multimodal animation and consistent identity preservation. Start testing both models and get your Kling Avatar API key and OmniHuman 1.5 API Key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Kling 3.0 vs Kling 3.0 Omni: Which Model Is Better for AI Video Generation? Compare Kling 3.0 and Kling 3.0 Omni across video quality, motion realism, and production use cases to decide which AI video model performs better. Understanding the Distinctions! Released in February 2026, the Kling 3.0 model series introduced a new phase in the Kling video generation ecosystem. In the same model series, Kuaishou released Kling Video 3.0 and Kling Video 3.0 Omni . Official announcement of the release of the Kling 3.0 series on their website ## Kling 3.0 Series Release and Model Overview Kling 3.0 evolves from the Kling 2.6 model and focuses on improved temporal stability, multi-shot control, and structured scene generation. Kling 3.0 Omni , by contrast, is built on the Kling O1 architecture and introduces support for video reference input, enabling users to guide generation using an existing video. While these models originate from different architectural branches, their supported API workflows currently overlap. At the time of writing, video reference input is not supported via API. This means that for standard generation workflows such as: 1. Text-to-Video (Single Shot) 2. Text-to-Video (Multi Shot) 3. Text-to-Video Single Shot both Kling 3 and Kling 3 Omni operate under comparable input constraints. In theory, this suggests that output behavior for standard video generation should be similar. In this comparison, we evaluate Kling 3.0 and Kling 3.0 Omni under identical generation conditions to determine whether measurable differences in video quality emerge when video reference is not used. ## Final Verdict! Kling 3.0 and Kling 3.0 Omni perform similarly in standard text-to-video generation, producing comparable visual quality and motion across most prompts. The key distinction emerges when reference images are involved: Kling 3.0 Omni handles reference-guided generation more reliably, maintaining stronger consistency with the provided input. For general T2V workflows either model performs well, but for projects that rely on image references, Kling 3.0 Omni is the more dependable choice. ## Kling Video API: What Is Actually Different? Both models support: 1. Text-to-Video (T2V) 2. Image-to-Video (I2V) 3. Multi-shot generation 4. Multi-character coreference 5. Native audio generation 6. Flexible duration settings The major distinction lies in Kling 3.0 Omni supporting video reference input capability. For all other workflows, the models are expected to behave similarly. Hence, this comparison focus entirely on output behaviour, and not the feature set. ## Evaluation: How We Evaluate Kling 3.0 API vs Kling 3.0 Omni API For the qualitative comparison, we focus on the single shot (T2V), multi shot (T2V) and I2V capability of both models. For image-to-video generation, images were generated using our Nano Banana Pro API playground for quality results. The evaluation framework is adapted from a Labelbox-style T2V assessment and evaluates each output across four dimensions: 1. Prompt adherence 2. Video realism 3. Video resolution 4. Artifacts Each example uses the same prompt for Kling Video 3.0 API and Kling Video 3.0 Omni API to isolate model behavior rather than prompt variation. Additionally both Kling 3.0 and Kling 3.0 Omni support native sound generation, all videos evaluated in this comparison were generated with audio. ## Video Comparison: Kling 3.0 vs Kling 3.0 Omni ## Example 1: Single shot (T2V) We begin with a controlled single-shot generation. Kling 3.0 Output Kling 3.0 Omni Output Prompt: A cinematic shot inside a quiet subway train at night. A young man in a navy jacket sits by the window as city lights streak past outside. The camera starts in a medium-wide shot from across the aisle, then slowly pushes in toward him. As the train moves, subtle reflections of passing lights appear on the window glass and faintly across his face. He turns his head slightly toward the window and exhales softly. The lighting should feel natural and consistent with a moving train environment. No cuts. Analysis: Both Kling 3.0 and Kling 3.0 Omni followed most scene instructions, including environment, camera push-in, and lighting consistency. However, neither model correctly executed the specified head turn toward the window or the subtle exhale. Both demonstrated high realism, with stable lighting and convincing reflections. Resolution was strong in both outputs, and no visible artifacts were observed. Overall, performance was comparable, with minor action-level deviations in prompt adherence. ## Example 2: Multi shot (T2V) Next we test a structured multi-shot sequence, the feature that sets the Kling 3 model series apart from other Kling models. Kling 3.0 Output Kling 3.0 Omni Output Prompt 1: A detective in a grey suit examines a small metal key under warm desk lighting in a dim office. Prompt 2: The camera cuts to a medium shot of him standing near a window as rain hits the glass behind him. Prompt 3: Close-up of his face as he narrows his eyes thoughtfully. Analysis: Both models followed the multi-shot structure and maintained character consistency across shots. However, although the prompt specified that the window should be behind the detective, both outputs positioned him facing the window, indicating a spatial misinterpretation. Realism and resolution were strong in both cases, with stable lighting and facial detail. Kling 3.0 showed no visible artifacts, while Kling 3.0 Omni exhibited a brief, slight distortion of the key in the first shot. Overall, both performed similarly, with minor spatial deviation and a small object-level artifact observed in the Omni output. ## Example 3: I2V To assess identity consistency, we tested the I2V generation capability. Reference images We followed the prompt structure from our Kling AI API documentation of referencing using @image_1, the city at night and @image_2, the woman in white blazer. Kling 3.0 Output Kling 3.0 Omni Output Prompt: Using the provided reference images, generate a video of the woman from @image_2 walking confidently through the environment shown in @image_1. The lighting, atmosphere, and color tone of the street must match @image_1, while preserving the facial features, hairstyle, and clothing details of @image_2. The camera slowly tracks forward toward her as she walks. No cuts. Analysis: Kling 3.0 AI failed to follow the reference-based prompt and did not meaningfully incorporate the provided images, generating only a generic walking scene. Kling 3.0 Omni, by contrast, preserved subject and environmental consistency from the reference images but reversed the intended camera movement. Kling 3.0 showed fair realism in motion physics, while Kling 3.0 Omni demonstrated stronger overall realism and integration. Resolution was good in both outputs, and no clear artifacts were observed. Overall, Kling 3.0 Omni showed significantly stronger reference adherence, while Kling 3.0 struggled with image-guided generation. ## Closing Thoughts On Kling 3.0 vs Kling 3.0 Omni Across the three controlled tests, Kling 3.0 and Kling 3.0 Omni showed largely comparable performance in standard text-to-video generation. In both single-shot and multi-shot scenarios, the models delivered strong realism, high resolution, and minimal artifacts, with only minor deviations in spatial interpretation and fine-grained action adherence. The clearest difference appeared in the image-to-video evaluation. Kling 3.0 Omni preserved reference consistency and environmental integration effectively, while Kling 3.0 struggled to meaningfully incorporate the provided reference images. This suggests that, despite overlapping API workflows, architectural differences may affect how reference-based inputs are interpreted. Overall, when video reference is not used, Kling 3.0 API and Kling 3.0 Omni API perform similarly in standard T2V tasks. However, Kling 3.0 Omni shows stronger reliability in reference-guided generation, indicating more robust multimodal alignment under image-constrained conditions. For text-driven workflows, both models offer comparable quality, but for stronger reference consistency, Kling 3.0 Omni may provide a more stable foundation. Start testing both models and get your Kling API key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Kling 3.0 vs Kling 2.6: Latest Kling AI Version Comparison (2026) Compare Kling 3.0 and Kling 2.6 APIs in 2026 across prompt adherence, realism, resolution, artifacts, control, and generation speed. Learn which Kling AI API fits your workflow. Breaking Down the Differences! In February 2026 , Kling released Kling VIDEO 3.0 , the latest version of its video generation model. Kling 2.6, which preceded it, has already seen broad adoption due to its relatively stable outputs and predictable behavior across common text-to-video and image-to-video workflows. While Kling 3.0 introduces a substantial set of new capabilities, the practical value of these upgrades depends heavily on the complexity of the scenes being generated and the requirements of the production pipeline. For many teams, the decision is not whether Kling 3.0 is newer, but whether its expanded capabilities meaningfully change what can be built. In this comparison, we evaluate Kling 2.6 API and Kling 3.0 API across qualitative output differences. ## Latest Kling AI Version (2026) Comparison Table From Official Kling Website The comparison table above summarizes the functional differences between Kling VIDEO 2.6 and Kling VIDEO 3.0. At a baseline level, both models support core workflows such as: 1. Text-to-Video 2. Image-to-Video 3. Start & End Frames-to-Video 4. Native Audio generation This makes Kling 2.6 sufficient for many standard video generation tasks, particularly short, single-shot clips with limited scene complexity. However, Kling VIDEO 3.0 expands meaningfully beyond these fundamentals. Key upgrades introduced in Kling 3.0 include: 1. Multi-shot video generation 2. Start frame plus element reference 3. Multi-character coreference (3+ characters). 4. Broader multilingual support (Chinese, English, Japanese, Korean, and Spanish) 5. Support for dialects and accents 6. Supports longer video durations (up to 15 seconds) 7. Flexible duration settings These differences become increasingly important as workflows move from experimentation toward production-grade generation. ## How we evaluate Kling 3.0 vs Kling 2.6 For the qualitative comparison, we focus on the text-to-video (T2V) capability of both models. The evaluation framework is adapted from a Labelbox-style T2V assessment and evaluates each output across four dimensions: 1. Prompt adherence 2. Video realism 3. Video resolution 4. Artifacts Each example uses the same prompt for Kling 2.6 and Kling 3.0 to isolate model behavior rather than prompt variation. Although both Kling 2.6 and Kling 3.0 support native sound generation, all videos evaluated in this comparison were generated without audio. As a result, audio-related capabilities were not included in the assessment. In addition, newly introduced features in Kling 3.0 - such as multi-shot generation, start frame plus element reference, and multi-character coreference - were not tested in this evaluation. The comparison focuses specifically on single-shot text-to-video outputs to ensure a fair and consistent baseline between the two models. ## Video Comparison: Kling 3.0 vs Kling 2.6 The following examples illustrate how Kling 2.6 and Kling 3.0 behave under different types of video generation tasks. ## Example 1: Prompt adherence This test is important for story-driven scenes with detailed visual constaints. Prompt: A woman in a green trench coat walks through a rainy city street at night, holding a transparent umbrella. Neon signs reflect on the wet pavement. The camera starts behind her, then slowly moves to a side angle as she stops and looks up. Kling 2.6 Output Kling 3.0 Output For prompt adherence, both Kling 2.6 and Kling 3.0 perform relatively well, with all key elements from the prompt appearing in the generated outputs. However, a subtle difference can be observed in how environmental details are rendered. In Kling 2.6, the rainy effect is more pronounced, while in Kling 3.0 it appears more subdued. Although this difference does not significantly impact overall prompt compliance, it highlights how the two models prioritize visual emphasis differently when rendering atmospheric elements, which can influence the final look and tone of the scene. ## Example 2: Video realism This test is relevant for content where lighting, materials, and physical motion affect perceived quality. Prompt: A close-up shot of a ceramic coffee cup on a wooden table as steam rises slowly. Morning sunlight enters from a window on the left, casting soft shadows. The camera gently pushes forward. Kling 2.6 Output Kling 3.0 Output In the evaluation of video realism, both Kling 2.6 and Kling 3.0 perform strongly and produce visually convincing results. The lighting on both cups is rendered in a realistic manner, with morning sunlight entering from the left and casting soft, natural shadows across the table surface. Reflections and highlights behave consistently with the scene’s lighting conditions, contributing to a believable appearance. The ceramic material of the cup is also well represented in both outputs. Surface details shows subtle texture, which enhances the sense of physicality rather than making the object appear synthetic. Overall, this example shows that both models are capable of delivering high-quality, realistic close-up shots, particularly in scenes with controlled lighting. ## Example 3: Video resolution This test is importatnf for outputs intended for professional or client-facing uses. Kling 2.6 Output Kling 3.0 Output For the evaluation of video resolution, both Kling 2.6 and Kling 3.0 are graded highly. In both outputs, fine details in the modern workspace scene are rendered clearly, including the laptop body, keyboard, and surrounding environment. The scrolling code text on the laptop screen remains stable throughout the clip, without noticeable blurring or loss of clarity during motion. Overall, this example indicates that both models are capable of producing high-resolution video outputs suitable for professional or client-facing use cases, particularly in scenes with controlled camera movement and well-defined visual elements. ## Example 4: Artifacts This test is critical for more complex generations. Kling 2.6 Output Kling 3.0 Output For the evaluation of artifacts, several differences can be observed between the two models. In the case of Kling 2.6, the generation shows multiple deviations from the prompt. First, the specified camera movement is not followed correctly: instead of panning from left to right, the camera pans from right to left. Second, key actions described in the prompt are missing, as the video does not clearly depict the person chopping vegetables or plating the food. In contrast, Kling 3.0 exhibits significantly fewer artifacts. The model largely adheres to the prompt, including the intended camera movement and overall scene progression. However, there is a minor omission, as the action of chopping vegetables is not fully captured. Despite this, the overall output remains more consistent and coherent compared to Kling 2.6. ## Our final thoughts on Kling 3.0 API and Kling 2.6 API Based on the four examples above, both Kling 2.6 and Kling 3.0 demonstrate strong baseline capabilities across prompt adherence, visual realism, and resolution. In controlled scenarios with limited motion or complexity, the two models often produce comparable results, with differences appearing mainly in how visual emphasis and temporal consistency are handled. Kling 3.0 generally shows more consistent behavior in complex scenes, particularly in reducing artifacts and maintaining correct camera movement over longer or more detailed sequences. In addition, Kling 3.0 offers greater control over its outputs through an expanded feature set, enabling more precise handling of scene structure, subject relationships, and generation constraints. This makes it better suited for production-grade workflows where consistency and controllability are important. At the same time, it is important to note that Kling 2.6 API generates videos significantly faster than Kling 3.0 API . This performance difference makes Kling 2.6 a practical choice for rapid iteration, early-stage experimentation, and high-volume generation workflows where turnaround time is a key consideration. In practice, the two models serve complementary roles. Kling 2.6 API is well suited for quick prototyping and prompt refinement, while Kling 3 API is more appropriate for selected final renders that require higher stability, stronger adherence, and finer control over the generated output. Choosing between them depends not only on output quality, but also on workflow priorities such as speed, control, and production requirements. Start testing both models and get your Kling Avatar API key via PiAPI today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Creating Educational & Infographic Visuals with Z-Image Turbo API: A Lightweight AI Image Generation Guide with Examples Create educational visuals and infographic posters using Z-Image Turbo API. Learn features, use cases, and example prompts with this lightweight AI image model. PiAPI makes it simple! Educational visuals don’t need to be complex or resource-heavy to be effective. In many cases, clarity, structure, and speed matter far more than cinematic detail. That’s exactly where Z-Image shines. As a lightweight image generation model, Z-Image is designed to help educators, developers, and content creators quickly generate clear, practical visuals for learning and communication. In this guide, we’ll explore what Z-Image is, its key strengths, and how it can be used to create educational content and poster-style graphics - along with a few example prompts to get you started. ## What is Z-Image AI? Z-Image is a lightweight 6-billion-parameter foundation model designed around speed, efficiency, and practical visual outputs. Within the Z-Image AI family, Z-Image Turbo is the released and production-ready variant. Unlike large, compute-intensive models that prioritize visual complexity, Z Image AI is built to produce clean, and purpose-driven visuals . As a result, Z-Image Turbo is especially well suited for educational and informational visuals. To make these capabilities accessible in real-world applications, Z Image is available through our Z-Image Turbo API , allowing developers and platforms to integrate fast, lightweight image generation directly into their products. All you need to get started is your Z-Image API key and start expressing your ideas! ## Key Features of Z-Image What Makes Z-Image particularly useful for educational and poster content? Lightweight and Fast Z-Image requires fewer resources and generates images as quickly as a second, making it cost-effective and easy to scale. World Knowledge With strong semantic understanding, Z-Image excels in image generation involving famous landmarks, well-known characters, and specific real-world objects, helping ensure visuals remain accurate. Strong Text-to-Visual alignment The model handles diagrams, labels, and explanatory layouts well - ideal for expressing concepts and ideas. Bilingual Text Rendering and Composition Z-Image demonstrates strong text rendering and layout capabilities, particularly for poster-style visuals. It supports clear and readable text in both English and Chinese . These key features of Z-Image make the model a reliable choice for educational and informational visuals through purpose-driven image generation. ## Use Cases: Z-Image Turbo API Use Cases for Educational and Infographic Content Below are common use cases where its lightweight architecture and strong text-to-image alignment are especially valuable. Educational Diagrams & Learning Materials Z-Image AI is well suited for generating structured diagrams across subjects such as science, geography, and mathematics. The model’s emphasis on clarity and accurate text-to-image alignment makes these visuals suitable for slides, worksheets, and online learning platforms, where information needs to be communicated precisely. Informational Posters & Public Communication For informational posters, the focus is on readability and layout consistency rather than visual complexity. Z Image AI can generate poster-style visuals that present messages clearly, making it suitable for notices, awareness materials, and instructional content intended for broad audiences. Corporate Training & Internal Documentation In corporate environments, Z Image API can support internal knowledge sharing by generating visuals for onboarding materials, training decks, and documentation. Its lightweight design allows teams to produce multiple diagrams or explainer visuals efficiently across internal systems and platforms. Product Documentation & Explainer Content Z-Image Turbo API integrates naturally into product and developer documentation workflows, where visuals are used to reinforce written explanations. It is particularly useful for generating diagrams and explanatory graphics that help users understand features, processes, or system behaviour without overwhelming them visually. When to Use Z-Image Turbo vs Other Image Generation Models Z-Image Turbo is best suited for educational, informational, and poster-style visuals where clarity, structure, and readable text matter most. For highly stylized artwork, cinematic imagery, or photorealistic scenes, larger general-purpose image models may be more appropriate. In practice, Z-Image Turbo complements these models by handling structured visual communication efficiently and at scale. ## Z-Image Turbo Prompt Examples and Best Practices Below are text-to-image examples that demonstrate how Z Image Turbo API can be used across educational and informational scenarios. These prompts are designed to produce clear, structured and readable visuals. ## Example 1: Educational Poster In this first example, we’ll craft a prompt for a clean, educational ready diagram . Prompt: Create a simple educational diagram showing the life cycle of a plant arranged from left to right with three stages, and add a centered title at the top in both English and Chinese reading “Plant Life Cycle / 植物生长周期”. Stage 1 is labeled “Seed / 种子” and shows a small seed planted in soil, Stage 2 is labeled “Young Plant / 幼苗” and shows a small green plant with several leaves growing from the soil, and Stage 3 is labeled “Mature Plant / 成熟植物” and shows a fully grown plant with a strong stem, multiple leaves, and visible flowers. Connect each stage with clear arrows to indicate progression, and use a flat illustration style with soft natural colors, a white background, consistent icon and illustration style, and clear, readable bilingual labels placed under each stage. We prompted the model to generate a simple plant life cycle diagram with clear stages and bilingual labels. This reflects how educational teams create structured visuals for lessons and worksheets, where clarity and progression are key. ## Example 2: Informational Poster This example demonstrates how Z Image Turbo API can be used to generate a public-facing informational poster , where layout clarity and message hierarchy are essential. Prompt: Design an informational fire safety poster with three vertically stacked sections. Design an informational fire safety poster with three vertically stacked sections on a white background. The first section has the title “Emergency Exit” with the description “Follow exit signs to leave the building safely” and a simple flat safety icon showing a person running toward an exit door with an arrow. The second section has the title “Fire Extinguisher” with the description “Use only if the fire is small and you are trained” and a flat vector icon of a fire extinguisher next to a small flame. The third section has the title “Do Not Use Lift” with the description “Use stairs instead” and a flat warning icon showing an elevator with a fire symbol and a diagonal prohibition line. Use high-contrast safety colors such as red, white, and black, large readable sans-serif text, consistent flat icon style, clear spacing between sections, and a clean public-safety poster layout. Use high-contrast safety colors (red, white, and black), large readable sans-serif text, consistent flat icon style, clear spacing between sections, white background, and a clean public-safety poster layout. Here, we ask for a fire safety poster with high-contrast sections, clear icons, and concise instructions. This mirrors real-world public information design, where messages need to be understood quickly at a glance. ## Example 3: Corporate Training Visual Here, the prompt targets internal training and onboarding materials , where visuals support structured explanations and need to remain consistent across documents. Prompt: Create a corporate compliance training visual about data privacy responsibilities using a clean 2×2 grid layout on a white background. Add a centered top title reading “Data Policy” in bold sans-serif typography. Create four equal rectangular panels with subtle rounded corners, consistent padding, and uniform spacing. Each panel contains an icon at the top, a bold heading, and very short bullet-style text lines. The panels are: “Data Collection” with the line “Collect only necessary data” and a clipboard icon; “Data Storage” with the line “Store in secure systems” and a locked database icon; “Data Access” with the line “Role-based access only” and a key or ID badge icon; and “Data Sharing” with the line “Use approved channels” and a shield or share-with-lock icon. Use a professional color palette with navy or blue accents, dark gray text, light gray panel backgrounds, crisp readable typography, and no gradients, shadows, or decorative backgrounds. In this case, the prompt focuses on a grid-based data privacy training visual. This aligns with how organizations present compliance information internally in a clear, scannable format for onboarding and training. ## Example 4: Product Documentation & Explainer In this final example, the prompt is designed for product documentation , where visuals are used to reinforce written explanations without adding unnecessary complexity. Prompt: Create a product explainer diagram for an ergonomic office chair designed for long working hours using a centered layout on a white background with a title at the top reading “Ergonomic Chair Key Features.” Display a front-facing illustration of the chair in the center of the layout. Around the chair, place four labeled callout boxes with thin connector lines pointing to specific parts of the chair. The callouts are: “Adjustable Headrest” with the sub-label “Neck Support,” “Lumbar Support” with the sub-label “Lower Back Comfort,” “Seat Height Control” with the sub-label “Maintain Posture,” and “Flexible Armrests” with the sub-label “Reduced Arm Strain.” Use a flat vector illustration style, neutral professional colors, clear readable labels, consistent icon style, minimal visual noise, and a layout that resembles a product manual or brochure explainer. For this example, we generate a product explainer highlighting key ergonomic chair features using labeled callouts. This matches how product teams design documentation visuals that support manuals. ## Using Z-Image API with PiAPI Implementing from our Z-Image API docs is seamless and straightforward. In your backend, you choose the Qubico/z-image model, pass the prompt string, and optionally specify other parameters. At PiAPI, we offer the customisability of the negative prompt, flow shift, size of output, batch size and seed parameters. Typical JSON-style request body Alternatively, head over to our on-page playground to experiment with the model and put your ideas into action. ## Conclusion Z-Image Turbo shows that high-quality educational and informational visuals don’t have to be complex or resource-heavy. With its emphasis on clear layouts, accurate labelling, and consistent composition, it’s well suited for diagrams, posters, training materials, and product explainers that need to be generated at scale. Through the Z Image API, teams can integrate image generation directly into their content pipelines, automate visual creation, and maintain consistency across materials. Unlock the power of 20+ AI models with PiAPI - image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Qwen Image API Prompting Guide: Multilingual Prompts & Best Practices Explore Qwen Image AI and Qwen Image API in action. See how we use Qwen AI’s 26+ language support with PiAPI to turn multilingual prompts into production-ready visuals. Most AI image generation guides assume one thing - You ideate and create only in English. But that's not the complete picture. Teams around the world operate in various languages. Designers in Shanghai think Chinese. Marketers in Tokyo write copies in Japanese. Creators in São Paulo brainstorm in Spanish. Translating every brilliant idea into English to create with an AI model can be laborious and resource-intensive. But Qwen Image AI changes that: it understands prompts in 26+ languages, letting you generate visuals directly in the language you think in - no translation friction, no creative delay. In this article, we'll explore why multilingual image generation is a game-changer, see how Qwen Image's 26+ language support transforms real workflows, and share practical prompts you can use immediately. ## What is Qwen Image? Qwen Image is a text-to-image or T2I model from the Qwen AI family that generates high-quality images from natural language prompts. Unlike many models that are mainly tuned for English, Qwen Image features a robust understanding on a wide range of languages, including (but not limited to): 1. English 2. Chinese 3. Japanese 4. Korean 5. Spanish 6. French 7. German 8. Portuguese 9. and others The key idea: you don’t have to translate your thoughts into English first. You describe what you want in your language to Qwen Image AI and that's it. All you need to get started is your Qwen API key and leave the rest to your imagination! ## Why multilingual support actually matters for Qwen API? "Supports 26+ languages" sounds like a spec line - until you look at how people really work. Below are 3 reasons why Qwen Image is ideal for your workflow! 1. Less friction for non-English-first teams If your team speaks Chinese, Japanese, Spanish, Arabic, or any other language day-to-day, English prompts are just another barrier. You think in your language, translate to English, hope the translation captures what you actually meant, paste it into the model, and then spend time debugging whatever weird outputs came from awkward phrasing. It's a lot of friction. With Qwen Image AI, you skip that whole loop - you prompt in your native language and get straight to iteration. 2. Better local nuance and cultural context Here's the thing: some ideas just don't translate cleanly. "ins风日系咖啡馆" has a specific vibe in Chinese internet culture that "Instagram-style Japanese cafe" completely misses. Same with "治愈系插画"-"healing-style illustration" loses all the emotional weight. Throw in local festivals, regional street food names, slang across Spanish, Japanese, Korean, and you realize how much nuance gets lost when you're forced to work in English. When you can prompt in your native language, the model actually gets what you're going for. 3. Global products, local visuals If you're running a brand across multiple markets, you don't just need one hero visual - you need local versions. Different text, different regional aesthetics, different cultural references. Multilingual prompting means each market team can create visuals tailored to their audience without needing someone who's fluent in English-first prompting. ## Qwen Image API Examples We’ll generate a few Qwen-image examples across different languages and use cases, so you can see how AI image generation performs optimally with our Qwen Image API. ## Example 1: Chinese social media post For this first example, we’ll generate an image using a Chinese prompt . In English, it roughly translates to: “ A cozy interior shot of a Japanese-style, Instagram-aesthetic café at dusk, with wooden tables and chairs, warm yellow lighting, and light rain falling outside the window. The atmosphere feels soft and comforting, and the image is suitable as a social media promotional visual. ” Prompt (中文): 傍晚时分的日系ins风咖啡馆室内照，木质桌椅，暖黄色灯光，窗外下着小雨，氛围温柔治愈，用于社交媒体宣传图 In this example, we use a Chinese prompt to brief a cozy, Japanese-style café scene for social media. Instead of translating “日系ins风” or “治愈系氛围” into awkward English, our team can describe the vibe directly in Chinese and let Qwen Image handle the rest. This keeps both cultural nuance and creative speed in our daily content workflows. ## Example 2: Japanese kawaii-style illustration Next, we’ll switch to a Japanese prompt . Translated into English, it reads: “ A cute pastel-colored illustration of a cat character working on a laptop. The mood is gentle and friendly, with a composition that works well as a social media icon. ” Prompt (日本語): パステルカラーのかわいいイラストスタイルで、猫のキャラクターがノートパソコンで仕事をしている様子。やさしい雰囲気、SNS用アイコンとして使える構図 Here, we prompt a pastel, kawaii-style cat character working on a laptop entirely in Japanese. Our designers can write naturally - using phrases like “やさしい雰囲気” and “SNS用アイコン” - without switching mental context into English. That means faster production of mascots, icons, and character assets that still feel authentically Japanese. ## Example 3: Korean beauty banner For our third example, we’ll generate an image using a Korean prompt . In English, the prompt is: “ A clean advertising image with Korean skincare products neatly arranged on a white background, lit by soft natural light. The overall look is minimal and modern, with empty space at the top to add a Korean slogan. ” Prompt (한국어): 깔끔한 화이트 배경에 한국 스킨케어 브랜드 제품이 정갈하게 배치된 광고 이미지. 부드러운 자연광, 미니멀한 느낌, 상단에는 한글 슬로건을 넣을 수 있는 여백 In the Korean example, we brief a clean skincare product shot with soft natural light and space for a Korean slogan. This mirrors exactly how our team would describe a K-beauty banner internally, just now fed straight into Qwen Image. The result: ad-ready hero images and PDP visuals generated from a single Korean sentence. ## Example 4: Spanish food festival poster Now let’s look at a Spanish prompt . Translated into English, it says: “ A colorful poster for a Latin American street food festival, with illustrations of tacos, arepas, empanadas, and fresh juices. The style is modern and eye-catching, with space at the bottom to add the date and location. ” Prompt (Español): Póster colorido para un festival de comida callejera latinoamericana, con ilustraciones de tacos, arepas, empanadas y jugos naturales. Estilo moderno y llamativo, espacio en la parte inferior para poner fecha y lugar For Spanish, we generate a colorful poster for a Latin American street food festival with tacos, arepas, empanadas and fresh juices. Our marketing team can brief the asset in fluent Spanish, keeping local food names and tone natural. Qwen Image turns that into a ready-to-iterate festival poster without forcing us into English-first thinking. ## Example 5: Arabic elegant event graphic Finally, we’ll generate an image using an Arabic prompt . In English, this prompt means: “ An elegant design for an evening event invitation, with a dark background and simple gold ornaments, leaving space in the center to write an Arabic title. Prompt (العربية): تصميم أنيق لدعوة حفل مسائي، خلفية داكنة مع زخارف ذهبية بسيطة، مساحة في الوسط لكتابة عنوان بالعربية، أسلوب عصري وفخم مناسب لوسائل التواصل الاجتماعي. In the Arabic example, we ask for an elegant evening event invitation with a dark background, gold accents, and space for an Arabic title. This matches how our team designs premium social posts and invites for MENA audiences. Qwen Image allows us to brief directly in Arabic while maintaining a modern, luxurious visual style. More examples can be found in on our Qwen Image API page! ## Qwen Image API with PiAPI Once your prompt is ready, wiring it into your PiAPI integration is just as straightforward. In your workflow, you select the Qwen Image API , pass the prompt string (in any supported language), and optionally specify parameters like seed value, image size, number of steps, or style preferences. With our Qwen Image API, you stay in control of the creative intent, while we handle the heavy lifting behind the scenes. Typical JSON-style request body We keep the developer experience simple by managing all the backend image generation for you. From there, your team can decide whether to send the generated image into a CMS, plug it into a design system, feed it into a creative automation flow, or embed it directly in whatever internal tools you use to ship campaigns. ## Conclusion For our team, Qwen AI API’s support for 26+ languages isn’t just a nice line in the spec sheet - it changes how we actually work day to day. Designers, marketers, and product folks in different regions can brief visuals in their own language, keep cultural nuance intact, and still rely on a single, consistent AI image generation stack. Instead of forcing everyone to think and prompt in English, we let people describe what they want in the way that feels natural to them and our Qwen API meets them there. If you’re building for multiple markets, it’s worth trying the same thing: write your next prompt in your own language, not English, and see how far you can go. Get started with your Qwen AI API key today! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Google Veo 3 Prompt Guide: Best Practices, Templates & Example Prompts 1. Learn how to write effective prompts for Google Veo 3 video generation. Explore best practices, structured prompt templates, and example prompts to improve AI video results. Powered by Google , Veo 3 is a high-quality video generation, and with the Veo 3 API you can turn text-to-video (T2V) or image-to-video prompts (I2V) into cinematic clips in just a few lines of text. But Veo AI is only as good as the instructions you give it. In this guide, we’ll walk through Google’s official Veo prompt best practices and turn them into a practical, developer-friendly process you can follow when calling the Veo 3 API via PiAPI. In this blog, we will cover: 1. How Veo's safety filters influence which prompts are allowed 2. The key elements every Veo prompt should contain 3. A simple step-by-step framework to build strong T2V / I2V prompts 4. Examples and a sample copy-paste template you can adapt for your own Veo API workflow ## What is Veo 3 & Veo 3 API? Veo 3 is a generative AI video model from Google designed to create rich, cinematic video from natural language and images. It understands subjects, motion, style and camera language well enough to approximate real-world filmmaking. The Veo 3 API , also referred to as the Veo AI API, exposes this capability to developers. Through PiAPI, you send a text prompt or image and text prompt, and receive rendered video clips that you can embed into apps, landing pages or creative tools. The clearer and tighter your prompt, the more on-brand, consistent and production-ready your video generation will be. ## Quick Veo 3 Prompt Template Structure: Subject + Action + Style + Camera positioning and motion + Composition + Focus and lens effects + Ambiance + Audio cues Example: A lone traveler standing on a mountain ridge, slowly looking across the valley below, cinematic documentary style, slow drone push forward from behind the subject, subject centered with expansive mountains in the background, shallow depth of field with a 50mm lens and soft bokeh, warm golden sunset lighting with mist rolling through the valley, soft wind sounds and distant birds. ## Safety Filters & Responsible Prompts Although your generation possibilities are broad, some safety measures are in place to guide responsible use. Every Veo request runs through Gemini safety filters before any video is generated. If a prompt includes vulgar, violence, sexual content, hate, illegal activity or targets real individuals in harmful or deceptive ways, the request can be blocked or the output may be refused. For a stable integration, treat safety as part of your prompt design. Keep scenarios brand-safe and neutral. Focus on products, environments, fictional characters, and abstract or cinematic scenes. Avoid celebrity likenesses, political messaging, and anything that could be interpreted as targeted harassment or misinformation. If a prompt is rejected, do not fight the system, simplify it. Strip out sensitive details, keep the creative idea, and reframe it as something Veo is allowed to render- for example, shifting from a real person to a fictional character, or from a controversial event to a generic city scene. ## Best practise for prompting Google Veo 3 According to Google API docs , good prompts are descriptive, intentional, and cinematic. A simple workflow looks like this: decide what the video is for, describe what is in the shot, what is happening, and what it should sound like, then layer in style and camera language. Start with the purpose. Are you generating a product demo for a landing page, a short teaser for social, a piece of looping B-roll behind UI, or a portrait-style character shot? Once the use case is clear, it becomes easier to decide how tight the framing should be, what mood you want, how much motion you need, and whether the soundtrack should feel quiet, energetic, or atmospheric. From there, think in terms of a few core elements: 1. Subject is what the camera sees. This could be a neon-lit city street, a fitness coach in a studio, a sleek smart speaker on a desk, or a drone flying over a forest. 2. Action is what happens in the scene. A character can walk toward the camera, a barista can pour a latte, steam can rise from a cup, or the camera itself can glide past skyscrapers. 3. Style sets the visual direction. You might want a cinematic sci-fi look, a film noir aesthetic with strong shadows, a bright playful cartoon style, or a realistic documentary feel. 4. Camera positioning and motion describe how the viewer experiences the scene. You can place the camera at eye level or above, ask for a slow dolly-in toward the subject, request an aerial top-down shot of a city, or have the camera orbit around a product. 5. Composition tells Veo how close or wide the shot should be. A wide establishing shot sets the environment. A medium shot balances subject and context. A tight close-up on a logo or face pushes attention to one detail. A two-shot keeps two people in frame at the same time. 6. Focus and lens effects control sharpness and perspective. Shallow focus with a softly blurred background makes the subject stand out. A macro lens emphasizes small product details. A wide-angle lens stretches space and captures more environment. 7. Ambiance finishes the mood with lighting and color. Warm golden-hour light feels inviting and natural. Cool blue tones work well for night scenes or tech aesthetics. Soft morning fog or light rain can add atmosphere without needing extra characters. 8. Audio cues (dialogue, SFX, ambient sound) help Veo 3 generate a synchronized soundtrack. With Veo 3, you can provide cues for sound effects, ambient noise, and dialogue directly in your prompt. Use quotes for specific lines of speech, for example: "This must be the key," he murmured. Describe sound effects explicitly, such as "tires screeching loudly, engine roading" or "crowd cheering in the distance". For ambient noise, describe the environment’s soundscape, like "a faint, eerie hum resonates in the background" or "soft cafe chatter and clinking cups." You do not need every element in every prompt. But if you consistently cover subject, action, style, and at least one cinematic detail such as camera, composition, focus, ambiance, or audio cues, Veo has enough information to generate consistent, controllable results in both the visuals and the soundtrack. ## Veo 3 API Examples We’ll generate a few complete Veo 3 prompts that combine visuals and audio cues, so you can see how AI video generation performs optimally with Veo 3 API. For I2V tasks, we first generate the input image using our Nano Banana Pro API . Check it out for superb quality AI image generation! ## Example 1: Product demo in an office We will begin with a T2V task. In this first example. we'll craft a Veo 3 prompt for a clean, landing-page-ready product demo shot. Prompt: A sleek silver laptop on a wooden desk in a bright modern office, clean fintech commercial style, eye-level camera with a slow dolly-in, medium shot, shallow focus with the laptop perfectly sharp and the background softly blurred, warm afternoon sunlight streaming through large windows, soft office ambiance with quiet keyboard typing and distant chatter, subtle UI notification sound as a new transaction appears on screen. ## Example 2: Cinematic street scene with dialogue For the second example we will go with T2V task. In this example, we will create a cinematic scene, which Veo 3 absolutely excels in. Prompt: A young man in a dark hoodie standing under a flickering streetlamp on a rainy neon-lit city street at night, cyberpunk cinematic style, close-up shot from the chest up, raindrops hitting his shoulders, shallow focus with sharp detail on his face and eyes, cool blue and magenta reflections on the wet pavement, he whispers ‘This must be the key,’ as a faint synth drone hums in the background and distant traffic noises echo softly down the street. ## Example 3: Café lifestyle shot with ambient sound Here, we have done a I2V generation with 2 inputs, an image and a text prompt. We first created a warm café lifestyle shot that focuses on environment, mood, and ambient sound. A warm café lifestyle shot Then, together with a text prompt, we generated a wonderful scene of a café with ambient sound. Prompt: A cozy café interior with a barista preparing a latte behind a rustic wooden counter, warm lifestyle commercial style, medium shot from behind a customer sitting at the bar, soft golden morning light coming through the windows, shallow focus on the latte art as the barista finishes the pour, gentle café ambiance with low chatter, clinking cups and the quiet hiss of the espresso machine in the background. Generated Veo 3 Video ## Example 4: Action scene with strong SFX Here, we have also done a I2V generation with 2 inputs, an image and a text prompt. We first created a red sports car racing along a coastal highway at sunset, cinematic action movie style. A red sports car along a costal highway at sunset Then, together with a text prompt, we generated an action packed scene perfect for a movie clip. Prompt: A red sports car racing along a coastal highway at sunset, cinematic action movie style, dynamic tracking shot from behind and slightly above the car, wide-angle lens to capture the sweeping curves of the road and crashing waves below, warm orange and pink sky reflecting off the car’s body, loud engine roaring, tires screeching as it drifts around a sharp corner, wind rushing past the camera and distant waves crashing against the rocks. Generated Veo 3 Video ## Veo AI API with PiAPI Once your prompt is ready, wiring it into your PiAPI integration is straightforward. In your workflow, you choose the Veo model, pass the prompt string, and optionally specify other parameters. With Veo AI API , we offer the customisability of the aspect ratio, duration, resolution of the video output. Typical JSON-style request body We make it simple by handling all the backend processes throughout your AI video generation with our Veo API. From there, you decide whether to pipe it into a CMS, a landing page builder, a creative automation flow, or a custom tool your team uses internally. ## Conclusion Based on the four examples above, it’s clear there’s no single “perfect” Veo 3 prompt format - but each structure plays a specific role in your workflow. Product demos, cinematic street scenes, café lifestyle shots, and high-energy action clips all lean on the same core building blocks, just tuned differently for framing, motion, mood, and sound. Once you see these patterns, Veo 3 stops feeling random and starts behaving like a controllable, production-ready tool. In practice, the best approach is to treat these as reusable prompt templates: iterate quickly by swapping subjects, styles, and audio cues, then refine your strongest versions into “hero” prompts you reuse across campaigns. With Veo 3 exposed through a single PiAPI integration, it becomes easy to test variations, standardise your best-performing patterns, and plug consistent video generation directly into your product or content pipeline. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Wan 2.5 vs Wan 2.2: Which Wan AI API Is Better for Production in 2026? Compare Wan 2.5 vs Wan 2.2 for AI video generation. See key feature differences, performance improvements, and which Wan model is recommended for production workflows in 2026. PiAPI Breaks Down the Decision! In September 2025 , Alibaba launched Wan 2.5, its most advanced multimodal video model to date. It's a significant leap forward, but that doesn't automatically mean it's the right choice for everyone. Official announcement on X about Wan 2.5 If you're building AI video workflows today, you're probably wondering: should I upgrade from Wan 2.2 to Wan 2.5? Both models are available through PiAPI , but they're built for different use cases - and the answer depends on what matters most to your project. In this breakdown, PiAPI compares Wan 2.2 API and Wan 2.5 API across four areas: cinematic-level aesthetics, instruction adherence, smoother motion generation, and native audio synchronization , which the official Wan 2.5 documentation highlights as key upgrades . For each, we’ll show side-by-side videos generated from the same prompt, so you can quickly see where Wan 2.5 really pulls ahead - and where Wan 2.2 is still more than good enough for AI video generation. ## The Final Verdict: Wan 2.5 or Wan 2.2 Wan 2.5 is the better choice for production-grade video generation when cinematic visuals, smooth motion, and native audio matter. Wan 2.2 remains a strong, cost-efficient option for prompt-driven drafts and internal testing. In practice, teams benefit most from a hybrid workflow - iterating with Wan 2.2 and finalizing with Wan 2.5. ## Key Features of Wan 2.5 API The Wan 2.5 API is built as the cinematic, production-ready option, with four capabilities that matter most for developers and content creators. - Cinematic-Level Aesthetics Wan 2.5 produces richer lighting, composition, and colour grading. Shots feel more structured and stable, making it easier to ship client-facing work without heavy post-processing. - Strong Instruction Adherence Compared to Wan 2.2, Wan 2.5 follows complex prompts more reliably - camera moves, character actions, outfit details, and scene constraints are more likely to appear exactly as described. That means fewer retries and more predictable output for your pipelines. - Smoother Motion Generation Wan 2.5 reduces jittery camera moves and awkward character animation. Action, tracking shots, and transitions play back with more natural motion, which is critical for ads, product demos, and dynamic social content. - Native Audio Synchronization With upgraded audio-visual alignment, the Wan 2.5 API keeps voices, sound effects, and music in sync with what's on screen. Lip movements match speech more closely and cuts land nearer to the beat, so you spend less time fixing timing and an editor. This is a new feature not available on the Wan 2.2 T2V model. ## Evaluation Framework For the comparison between Wan 2.2 and Wan 2.5, we'll be using the text-to-video (T2V) feature of the two models. Regarding the evaluation framework, we have taken the framework that was adopted in our previous blog comparing Luma Dream Machines 1.0 vs 1.5 . For more information, feel free to read our previous blog. ## Video Comparison: Wan 2.2 API vs Wan 2.5 API For each dimension, we use the same text prompt on both models and then score the outputs using our adapted T2V framework. Below is how Wan 2.5 and Wan 2.2 behave side by side. ## Example 1: Cinematic-Level Aesthetics Think brand films, product launches, and hero creatives where you want every frame to look like a polished commercial. Prompt: A slow cinematic close-up of a matte black wireless earbud rotating on a reflective glass surface in a dark studio, with soft rim lighting, shallow depth of field, and neon blue highlights in the background. Both videos do a great job with realism, with light reflecting off the earbuds to create a gloss that feels close to real-world lighting. They also show dynamic movement, as the rotation of the product showcase is super smooth with no visible jump cuts. In terms of prompt adherence, both generated videos follow the prompt reasonably well, with soft rim lighting and neon blue highlights in the background. However, the video generated by Wan 2.2 was not precise enough and did not follow the prompt of a single wireless earbud, instead showing two earbuds with a case. For Wan 2.5, we see that it was not precise enough to generate a reflective glass surface, unlike Wan 2.2. Overall, we think that Wan 2.5 did a better job in terms of cinematic level aesthetics. ## Example 2: Strong Instruction Adherence Perfect for multi-character scenes and story-driven videos where outfits, actions, and camera moves all need to follow a tightly written brief. Prompt: Two friends sitting at a small red café table outdoors. The person on the left wears a bright yellow jacket and black jeans, the person on the right wears a blue hoodie and white sneakers. The camera slowly orbits around the table from left to right while both are laughing and talking. For realism, Wan 2.2 shows a very saturated colour tone in elements like the table, which reduces the natural feel of the scene, while Wan 2.5 does a much better job of capturing realistic lighting and colour. For dynamic movement, both videos perform very well - human motion and camera motion are smooth with no visible jump cuts. In terms of prompt adherence, both models perform reasonably well, but Wan 2.5 falls short in following the specified camera movement accurately. Overall, we feel that Wan 2.2 performed better than Wan 2.5 for this example as Wan 2.2 was more precise in prompt following compared to Wan 2.5. ## Example 3: Smoother Motion Generation Great for action shots, sports clips, and dynamic product sequences where jittery movement would instantly break immersion. Prompt: A skateboarder performing a kickflip down a set of stairs in an urban plaza while the camera tracks smoothly from the side, then swings around to follow from behind as they roll away. For realism, Wan 2.2 performs exceptionally well, especially in how it renders the shadows of the moving person and skateboard, while the shadows in Wan 2.5 are slightly distorted. Both videos handle dynamic movement very well. The motions are super smooth with no visible jump cuts. In terms of prompt adherence, Wan 2.2 is not precise in the skateboard movement, missing the kickflip, whereas Wan 2.5 shows better adherence to the prompt. Overall, Wan 2.5 performed better for this example as we felt that the motion generation was smoother compared to Wan 2.2. ## Example 4: Native Audio Synchronization Made for explainer videos, talking-head content, and music-led edits. For this example, click on the generated videos and see where Wan 2.5 truly outperforms Wan 2.2 with its ability to generate video with native audio. Wan 2.2: Generated Video without Audio Wan 2.5: Generated Video with Native Audio Synchronization Prompt: A presenter standing in front of a large screen with simple charts, speaking directly to the camera and occasionally gesturing with their hands. The lip movements should match the voiceover of a friendly product explainer, with subtle camera breathing and a soft background track. For realism, both models perform exceptionally well. The lighting and details on the face of the presenters are crisp and of high quality. For dynamic movements, both models perform really well , from the hand gestures to facial movements. The motions are super smooth with no visible jump cuts. In terms of prompt adherence, for other details such as the charts and soft background, both models adhered to the prompt. However, only Wan 2.5 produced an audio along with video generation while Wan 2.2 did not have an audio generation along with its generated video. Overall, Wan 2.5 clearly stood out as the winner for this example due to its native audio synchronization feature that Wan 2.2 lacks. ## Conclusion: Choosing the Right Wan AI API for Production Based on the four examples provided above, we can see that there is no single “winner” across every dimension - but each model has a clear role in a production workflow . Wan 2.5 consistently shines in cinematic-level aesthetics , smoother motion generation , and native audio synchronization , making it the stronger choice for hero content, motion-heavy shots, and talking-head videos where polish really matters. At the same time, Wan 2.2 still holds its own , especially in prompt adherence and realism, and remains a very capable T2V model for many everyday use cases. If you’re generating drafts, internal concept tests, or don’t need audio-visual sync, Wan 2.2 can be the more cost-efficient option without sacrificing too much quality. In practice, the best approach is often a hybrid strategy : explore ideas and iterate quickly with the Wan 2.2 API , then promote your best prompts to the Wan 2.5 API for final, production-grade renders—especially when you care about cinematic visuals, smooth motion, or native audio. With PiAPI exposing both models through a single Wan AI API integration, it’s easy to A/B test, compare outputs side by side, and choose the right model for each video generation job in your pipeline. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## Nano Banana WINS over Flux Kontext: AI Image Editing Showdown Compare Nano Banana API and Flux Kontext API for AI image generation. See pros, cons, pricing, and which fits your workflow. Test both instantly in PiAPI’s Free Playground — no code required. Google Gemini 2.5 Flash Image (aka Nano Banana) is turning heads in AI image editing for its precise text-based edits and impressive prompt fidelity. With Adobe integrating Nano Banana into Firefly Text to Image module , Firefly Boards , Photoshop (beta) Generative Fill , and Adobe Express , creators now have a professional-grade tool for real-world edits. But the big question remains: Can Nano Banana truly outperform Flux Kontext in image editing? We tested both models with real-world prompts to find out. ## Nano Banana vs Flux Kontext: Image Editing Capabilities Prompt: Edit the background to day time in the fall season. Add realistic autumn leaves on the ground and adjust lighting to match a crisp autumn day. ## Nano Banana Image Editor ## Strengths - 1. Lighting & Mood: Transforms rainy neon-night into early evening daylight. Pavement reflections change naturally to shadows; neon signage removed for consistency. Tone is cinematic yet calm, preserving continuity. - 2. Detail Retention: Facial features, hair strands, and dress fabric — including glitter — remain consistent. Clothing folds and shadows are preserved, maintaining realism. - 3. Atmosphere: Believable extension of the original scene; feels like the same character photographed moments later. Editorial realism is strong without stylization. ## Weaknesses - 1. Background depth is slightly flattened; signage lacks layered glow. - 2. Crowd silhouettes feel less dynamic. ## Flux Kontext Image Editor nano banana, nano banana api, image editing, ai image editing, text-to-image, image-to-image ## Strengths - 1. Lighting & Mood: Converts rainy neon city to brighter, misty dusk. Soft glow through haze creates a warm, cinematic tone. - 2. Atmosphere: Dreamy, painterly reinterpretation; poetic fall aesthetic adds artistic flair. ## Weaknesses - 1. Detail Retention: Facial features softened (“AI beauty retouching”); dress texture and fine fabric folds lost. - 2. Glitter removed; dress appears solid. - 3. Wet pavement reflections muted. - 4. Sacrifices continuity and fidelity to original scene. ## Nano Banana vs Flux Kontext: Prompt Adherence Test Prompt: Take this photo of a Coca-Cola can and integrate it into a Christmas commercial scene. Santa is sitting in a cozy living room on a single sofa chair, holding the cold Coca-Cola can in one hand. Next to him is a small round table with cookies and a child-like handwritten note that says “For Santa.” Add warm holiday lighting, a decorated Christmas tree in the background, and a festive atmosphere. ## Nano Banana ## Strengths - 1. Detail Fidelity: Realistic textures for Santa’s beard, fur on boots, eyebrow definition, and fabric creases. - 2. Props Execution: Cookies crisp and varied, chocolate chips visible. - 3. Note Clarity: “For Santa” note perfectly legible, enhancing storytelling. - 4. Overall Realism: Lighting, shadows, and details align with a polished commercial photo. ## Weakness - Slight over-sharpening on edges: Softer cinematic edits may appear less natural. ## Flux Kontext ## Strengths - 1. Atmosphere: Warm lighting and background blur create cozy holiday vibe. - 2. Facial Expression: Santa’s face softer and inviting, painterly/commercial style. ## Weaknesses - 1. Detail Fidelity: Beard texture smoothed, suit lacks realistic fur/folds, cookies appear flat. - 2. Note Execution: “For Santa” note barely legible, reducing immersion. - 3. Coca-Cola Can Integration: Can looks oversized, less naturally integrated. ## Verdict: Nano Banana API > Flux Kontext API Winner: Nano Banana – superior in realism, micro-detail retention, prop execution, and prompt adherence. Ideal for commercial and editorial projects. Flux Kontext: Strong in painterly mood, dreamy lighting, and stylized reinterpretations, but sacrifices fidelity and continuity. ## Why Nano Banana Wins: Key Takeaways - 1. Micro-detail fidelity: Maintains hair strands, glitter, fabric folds, and textures. - 2. Prompt adherence: Follows background and prop instructions accurately. - 3. Continuity preservation: Edits feel like a natural extension of the original scene. - 4. Versatility: Excels in both text-to-image and image-to-image prompts. Flux Kontext shines in mood and painterly aesthetics, but Nano Banana’s professional-grade realism and editorial consistency make it the better choice for creators. ## How to Test Nano Banana API Yourself Our FREE Playground tr ial allows creators to experiment with both Nano Banana and Flux Kontext (and many more) without coding. Try Nano Banana via PiAPI today ! ## Final Thoughts For creators prioritizing realism, precision, and professional results , Nano Banana is the clear winner . Flux Kontext offers beautiful, stylized interpretations, but Nano Banana ’s attention to detail, props fidelity, and prompt adherence sets a higher standard for AI image editing in 2025.Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## Nano Banana API vs Flux Kontext API [2025]: Pricing, Speed & Free Playground Compare Nano Banana API and Flux Kontext API for AI image generation. See pros, cons, pricing, and which fits your workflow. Test both instantly in PiAPI’s Free Playground — no code required. In this blog, we compare Nano Banana and Flux Kontext , helping you decide which model balances speed, character consistency, and professional editing. Plus, we’ll show you how to experiment with both using PiAPI’s Free Playground trial (Yes, Flux AI Playground too!). ## Why Choosing the Right AI Image Generator Matters Selecting the right AI image generator is critical for your automated content workflow, especially if you're producing daily TikToks, YouTube Shorts, or Instagram Reels. The right model impacts speed, consistency and quality. Using the wrong tool can slow production or compromise professional polish. With our unified no-code Playground, visualize outputs, test prompts and refine your approach before scaling workflows — giving you confidence in choosing the right AI image generator for your creative needs. Visit Flux AI Playground and Nano Banana Playground for free! ## Automate Your Image-to-Video Workflow with PiAPI (NO CODE) Here are the key steps: - 1. Generate Images : You can use any, but for this blog, we're focusing on Nano Banana and Flux.1 . - 2. Generate Image to Video : Send your image to a video model like Kling to create fully edited videos ready for social media. - 3. Automate Publishing : Connect PiAPI outputs to apps through Make.com templates, automatically posting to YouTube, TikTok, Slack, Google Drive, and more. See more about using Make.com here . ## Nano Banana API Pricing vs Flux API Pricing Both Nano Banana and Flux operate on a pay-as-you-go (PAYG) model, allowing you to scale with your needs. The key difference lies in speed vs refinement — and cost per image . Flux Pricing Tiers: - 1. Flux Schnell: $0.0015 per image (1–4 images per generation) - 2. Flux Dev: $0.015 per image - 3. Flux Dev-Advanced: $0.02 per image Nano Banana Pricing: - Nano Banana: $0.03 per image While Nano Banana costs more per image than entry-level Flux tiers, its speed and character consistency can offset costs in high-volume creative workflows. Flux Kontext , on the other hand, offers cheaper entry pricing but demands more time and expertise to maximize results. ## Nano Banana — Speed & Character Consistency Ideal for rapid iterations, character generation, and storyboarding , Nano Banana API delivers fast, consistent results for high-volume content. Pros: - 1. Ultra-fast image generation in seconds, perfect for iterative workflows - 2. Maintains character proportions and key traits across multiple outputs - 3. Supports conversational prompts and reference images - 4. Beginner-friendly with lower cost, making it accessible for indie creators Cons: - 1. Limited ability for complex style transfer or local image edits - 2. Lower fidelity for nuanced textures and lighting ## Flux Kontext — Realistic Refinement & Professional Editing Designed for professional campaigns, cinematic visuals, and detailed edits, Flux Kontext API is ideal for studios or creators seeking high-fidelity, polished results. Pros: - 1. Stepwise refinement ensures realistic textures and cohesive images - 2. Excels in background replacement, relighting and blending - 3. Preserves objects, characters and scene integrity across multiple edits Cons: - 1. Slower workflow due to detailed editing processes - 2. May require fine-tuning for optimal out-of-the-box results ## Final Thoughts: Both Serve Their Own Purposes Nano Banana API → Best for lightweight experimentation, stylistic consistency, and animation or comic workflows. Flux Kontext API → Best for realistic textures, lighting, and professional photo editing. The good news: you don’t need to commit to separate subscription plans. With PiAPI , you can test both side-by-side, refine prompts, and discover which API aligns with your goals. We make it simple to build with the world’s best AI models. From image generation to video, audio and more, we help developers and creators integrate cutting-edge AI into their workflows at an affordable price. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## The Best TTS API in 2025: f5 TTS API Discover the best TTS API in 2025 with f5-TTS. Get lifelike voice synthesis, zero-shot cloning, multi-language support, and pay-as-you-go pricing with PiAPI’s developer-friendly text to speech API. ## The Best TTS API in 2025: f5 TTS API Text to speech (TTS) is booming in 2025, powering everything from voice assistants to content creation workflows. f5-TTS is designed for real-world applications, combining cutting-edge voice synthesis with a developer-friendly TTS API, our f5 TTS API is the go-to choice for those who need reliable, production-ready TTS. PiAPI provides voice cloning and speech synthesis capabilities using zero-shot learning technology. This service allows you to generate speech audio using a reference voice sample, making it possible to create natural-sounding speech in any voice. ## Features Of f5-TTS API The f5-TTS API isn’t just another text to speech service — it’s designed for developers who need speed, quality, and flexibility. Here are the key features that set it apart: ## Lifelike, Expressive Voices Go beyond robotic tones. f5-TTS delivers natural speech with intonation, pacing, and clarity that adapts to real-world use cases. ## Scalable Performance Built to handle production-level demands, the API supports everything from small apps to enterprise-scale platforms. ## Low-Latency Generation Real-time use cases — such as chatbots, live assistants, and interactive experiences — are powered by near-instant audio output. ## Multi-Language Support Reach global audiences with diverse voice options and language coverage, making f5-TTS a future-ready solution. ## Easy Developer Integration With simple endpoints and clear documentation, the voice synthesis API can be added into any workflow quickly. By combining these features, f5-TTS bridges the gap between traditional TTS APIs and the evolving needs of creators, businesses and developers in 2025. ## Free TTS vs f5-TTS (Why Not Just Use ‘TTS Free’ Tools?) While we can't deny the appeal of all things free, free tools usually come with major limitations like restricted usage caps, low-quality voices and little to no developer support. For professionals, these restrictions become blockers to building scalable products. In contrast, our f5-TTS API delivers enterprise-grade capabilities at a fraction of the cost. Unlike other TTS APIs that lock you into monthly subscriptions, PiAPI gives you the freedom to pay-as-you-go. At only $0.025 per 1,000 characters , we give you access to: - 1. Zero-shot voice cloning – generate natural speech in any reference voice without retraining. - 2. High-quality speech synthesis – lifelike intonation and emotion control for professional use. - 3. Scalable pricing – designed to support both indie creators and enterprise-scale deployments. This balance of affordability and advanced AI features makes f5-TTS one of the most cost-effective options in the text to speech API market. Whether you’re producing audiobooks, powering chatbots or localizing content in multiple languages, you can scale without breaking your budget. ## How to Use the f5-TTS API: Step-by-Step Getting started with the f5-TTS API is simple: Step 1: Sign up for PiAPI and get your free API key . Enjoy FREE credits of up to $60/month when you sign up for selected plans! Regardless, we offer a free Playground trial for all. Step 2: Explore the TTS Playground to test prompts and preview outputs. Step 3: Run the API in your project. Click here to view the full guide to our f5 TTS API docs . ## Use Cases for f5-TTS The versatility of f5-TTS makes it a powerful tool across industries: - 1. Voiceovers for content creators – produce professional narration for YouTube, TikTok or podcasts. - 2. Accessibility tools – generate audio for the visually impaired or for educational resources. - 3. Real-time chatbots & assistants – power conversational AI with lifelike, fast responses. - 4. Gaming & immersive apps – create dynamic character voices and dialogue without hiring multiple voice actors. ## Final Thoughts: From Text To Speech, From PiAPI To You The demand for advanced TTS APIs is only growing, and in 2025, f5-tts stands out as the best option for developers. With its lifelike speech, zero-shot voice cloning, multi-language support, and pay-as-you-go pricing, it delivers everything you need to build scalable real-world applications. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## SkyReels API — Create AI Videos Seamlessly with PiAPI SkyReels API brings human-centric AI video to life with cinematic quality and realistic motion. Try SkyReels API on PiAPI with free credits today. AI video tools are everywhere, but few truly capture the subtlety of human expression . That’s where SkyReels V1 comes in. Developed by Kunlun and delivered through PiAPI , SkyReels sets a new standard for human-centric video generation with cinematic quality, realistic motion, and easy API integration. Try SkyReels in the Playground ## What is SkyReels? SkyReels is the first open-source foundation model designed for human-centric video creation . Instead of generic motion, it focuses on realism: - 1. 33 distinct facial expressions - 2. 400+ natural movement combinations - 3. Trained on tens of millions of film & TV clips ( HunyuanVideo ) This training gives SkyReels outputs a cinematic look and emotional depth , making it perfect for filmmakers, content creators, and brands. Explore more in the SkyReels API Docs ## Why Choose SkyReels API? The SkyReels API makes it simple to integrate this model into apps, creative tools, or production workflows. Through PiAPI , you get: - 1. Scalable performance powered by optimized infrastructure. - 2. Straightforward API documentation to get building fast. - 3. Commercial usage rights (within model licensing). Whether you’re experimenting with creative concepts or building full products, the SkyReels AI API is designed to deliver. Get Started with the SkyReels API ## Features of SkyReels V1 SkyReels V1 brings a rich set of features designed for high-quality human video generation: - 1. Human-Centric Precision — nuanced emotions and gestures. - 2. Image-to-Video — generate videos from prompts or reference images. - 3. Cinematic Aesthetics & Lighting — Hollywood-style output. - 4. Performance on Par with Leaders — competes with closed models like Kling and Hailuo. - 5. Versatile Applications — for storytelling, advertising, social media, and creative projects. - 6. User-Friendly API Integration — designed for developers who want simple, efficient workflows. Experiment now in the SkyReels Playground ## SkyReels Pricing and PiAPI Subscription Plans We offer two types of pricing plans: - 1. Affordable subscription plans with free trial credits of up to $60 when you sign up*; and - 2. Pay-as-you-go API service at $0.15/generation only ! *Terms and conditions apply Check out our full Pricing & Plans ## Frequently Asked Questions ## What is SkyReels API? The SkyReels API is provided by PiAPI , designed to facilitate developers' access to the SkyReel V1 model. PiAPI will leverage its advanced inference framework and cutting-edge hardware infrastructure to deliver highly efficient and scalable video generation capabilities. With SkyReels API, developers can easily integrate the SkyReel V1 model's human video generation features into their applications and platforms. ## What input types does SkyReel V1 Model support? SkyReels supports a diverse set of inputs, including texts and reference image, unlocking a wide range of creative opportunities ## What makes SkyReels V1 different from other video creation models? SkyReels V1 excels with its human-centric design, offering uniquely precise facial expressions, movement combinations, and cinematic aesthetics, derived from extensive training on high-quality film data. ## Final Thoughts: Start Building with SkyReels API With SkyReels V1 and the SkyReels API , you can unlock a new era of human-centric video generation. Whether you want to experiment in the playground, explore the API docs, or integrate into production apps, we have you covered. Get Started with SkyReels API on PiAPI now ! Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster and at scale. ## How to Automate Glass Fruit Cutting Videos with Make.com and Kling API Automate daily ASMR videos with Make.com and PiAPI’s Kling API. Start with the viral glass fruit cutting prompt, then scale into a self-running Shorts and Reels channel. No code needed—just prompts, automation, and PiAPI’s 20+ AI models. ## Content That Builds Itself You’ve just created a cinematic masterpiece—a glass kiwi sliced in half, gleaming with surreal detail. Now imagine that same creative spark automatically transforming into a daily YouTube Short, Instagram Reel, or TikTok video published without you lifting a finger. That’s the power of workflow automation with Make.com and Kling API : a seamless pipeline that takes any AI prompt and turns it into fully generated, edited and published content. PiAPI is your one-stop solution. ## The Rise of Automated AI Workflows Everyone's moving from one-off generations to ongoing automated pipelines for Automated Faceless shorts and reels. This tutorial builds on our earlier experiment, Glass Fruit Cutting ASMR AI Prompt (How I Made a Video Without Google Veo 3) , where we used GPT-4o-image , Kling , and MMAudio to create hyperrealistic ASMR-style fruit cutting videos. Now, instead of stopping at a single video, we’ll automate the entire workflow to run daily using Make.com through our Kling API’s cinematic video generation . ## The Exact Glass Fruit Cutting Prompt Prompt: A close-up, slow-motion video of a human hand gently slicing all the way through a translucent glass mango with a shiny steel knife. The mango has a glossy, semi-transparent surface, and reveals a smooth glass-like seed inside. As the knife completes the cut, one half of the mango slowly slides and falls onto a wooden cutting board with a soft, satisfying sound. The scene is calming and tactile, with warm ambient light reflecting off the glass texture. Macro lens, high-detail realism, ultra-satisfying ASMR style, no background distractions, peaceful tone. Variations you can use include glass red apple, glass orange, glass tomato and glass kiwi. Test out the prompts on the Playground . For more prompt inspiration, check our original Glass Fruit Cutting tutorial . ## Step-by-Step Workflow: Automating with Make.com (No Code) ## Create a Google Sheet How to set up your Google Sheet - i. Column A: fruit (optional) - ii. Column B: prompt → the fruit cutting prompts. - iii. Column C: status → dropdown with “Create” (signals pending videos). - iv. Column D: output_url → leave blank for automation. (Optional: use ChatGPT to brainstorm daily fruit variations.) ## Build Your Scenario in Make.com Log in → go to Scenarios → click Create Scenario . ## Google Sheets Module (Search Rows) How to add Google Sheet module - i. Connect your Google Sheet through adding the respective module. - ii. Select "Search Rows" - iii. Pull rows with the “Create” status. ## PiAPI/Kling Module (Generate a Video based on Text) How to add PiAPI/Kling module - i. Model: Kling text-to-video. - ii. Connect with your API key. - iii. In “Prompt,” drag the variable from Google Sheets — don’t manually type it. - iii. Leave negative prompt, cfg scale, mode duration, aspect ratio, and service mode at defaults for this tutorial. For reels and shorts, use aspect ratio 9:16. Check out Kling AI Pricing & Features (2025) for more details about our Kling API pricing, task types, versions etc. ## Google Sheets Module (Update a Row) How to add Google Sheet module - i. Connect your Google Sheet through adding the respective module. - ii. Select "Update a Row" - iii. Use the "Row number" from the first Google sheet module. - iv. Update fields status and output_url from the PiAPI data. ## Add A Filter How to add filter - i. Between Google Sheets → PiAPI Kling, add a filter to signal which tasks have yet to be generated. - ii. Condition: “status” = Create. ## Repeater Module How to add Repeater module - i. Add a "Repeater" module. - ii. Loops the workflow daily. - iii. Watch your sheet auto-fill with fresh Kling videos. ## Additional Steps For A Complete Workflow In this tutorial, you learned how to transform a single glass fruit cutting experiment into a fully automated workflow using Make.com and PiAPI’s Kling API . Instead of generating one-off videos, you now have the foundation of a repeatable pipeline that can scale into a full ASMR content channel. From here, you can expand further: combine Kling video with MMAudio sound effects for a true multi-modal ASMR experience, save outputs directly into Google Drive or Airtable, and auto-publish daily to YouTube Shorts or Instagram Reels. You can also experiment with prompt variations like ai fruit cutting prompt , asmr cutting prompts , or ai glass cutting prompt to create your own ASMR prompt library. ## Final Thoughts: From Single Prompt to Full Pipeline What started as one video can become a self-sustaining content machine. PiAPI makes it possible by offering a wide array of AI models under one roof. Explore the GPT-4o-image API , Kling API and MMAudio API docs for deeper documentation or dive into our pricing plans to start scaling today. 👉 Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## Kling API Pricing, Features, and Documentation — Everything You Need to Know (2025) Discover Kling AI pricing, features, and API docs. Learn how PiAPI simplifies Kling API integration for creators and developers. If you’ve been following the surge of AI video generation tools, you’ve probably heard of Kling AI. Kling AI has quickly become one of the most powerful video generation models of 2025, enabling creators and developers to transform text, images or even full videos into cinematic, physics-aware animations, offering unmatched realism and creative control. In this blog, we’ll break down everything you need to know about Kling API pricing, features, and documentation . ## What Is Kling AI? Developed by Kuaishou, Kling AI is a suite of video generation models, ranging from Kling 1.0 to 2.1 Master, with each version improving cinematic fidelity and controllability. Through PiAPI , you can instantly try and integrate: - 1. Text-to-video generation - 2. Image-to-video animation - 3. Video extension - 4. Virtual try-on - 5. Professional-grade camera controls - 6. Video Effects generation - 7. Lipsync generation For creators and developers, real magic lies in the Kling API , which enables programmatic video generation at scale. ## Kling API Pricing When evaluating Kling AI pricing, we offer two flexible models: ## 1. Pay-As-You-Go An overview of PiAPI's Kling API Pricing (non-extensive). Please visit PiAPI's Detailed Kling Pricing for more. This option is ideal for creators and developers who want to scale on-demand, only paying for the video duration and quality they need. For a more detailed breakdown, check the "Kling API" section under the API Service on this page . ## 2. Host-Your-Account (Flat Pricing) For teams running larger workloads or requiring multiple accounts, opt for this at only $10/seat monthly! Connect your own Kling accounts to access all Kling API features, load balancer support for multiple accounts and enjoy stable, safe and production-ready hosting! Tip: If you’re just testing ideas, start with pay-as-you-go. If you’re scaling production or running client projects, host-your-account offers predictable costs. ## 3. PiAPI's Subscription Plans When it comes to Kling API pricing , transparency is key. Here’s how it typically works through our subscription tiers. Check out our pricing plans to see which fits your needs best. PiAPI’s pricing structure ensures you pay for generation time and features you actually need — no hidden fees. ## Key Features of Kling API Our Kling API integration unlocks the full power of Kling’s model suite. Read more about the models here: Kling 2.0 Master , Kling 2.1 Standard , Kling 2.1 Pro and Kling 2.1 Master . ## 1. Image-to-Video Support Bring static visuals to life with motion and cinematic detail. ## 2. Professional Camera Movement Pan, tilt, roll, and zoom with spatio-temporal precision. ## 3. Video Continuation Extend any clip in controlled increments for seamless storytelling. ## 4. Virtual Try-On Upload single or multiple garments and preview them on a model. ## 5. Accurate Physics Simulation Real-world realism in cloth, lighting, and motion. ## 6. Asynchronous API Calls Submit tasks, get callbacks — no interruptions in your workflow. ## 7. Powerful Conceptual Illustration Kling’s diffusion transformer translates abstract ideas into vivid sequences. ## 8. Adjustable Aspect Ratios From 16:9 cinematic widescreen to 9:16 vertical shorts. ## 9. 1080p Cinematic Output High-resolution results that rival traditional editing suites. These features position Kling AI as a top-tier solution for filmmakers, marketers, and product-driven platforms. ## Kling API Documentation Getting started with Kling is easier than it looks. We provide clean, developer-friendly documentation for: 1. Basic prompt-to-video calls 2. Task type and model-specific JSON configs 3. Camera path examples 4. Debugging guides (handling errors like 404 fetch task failures) Whether you’re prototyping in a few lines of Python or building workflows in n8n/ComfyUI, the documentation is optimised for copy-paste speed and real-world production. Don't believe us? Visit our Kling API Docs and explore our Kling Playground ! Enjoy a FREE Playground trial upon registering an account. ## Why Access Kling API Through PiAPI? Here’s why creators and developers are choosing PiAPI over fragmented alternatives. PiAPI simplifies the entire process with: ## 1. Unified API Access Kling alongside a wide array of video, audio, text and LLM models like ChatGPT , Nano Banana , Flux and Veo 3 . ## 2. Transparent Pricing Clear per-video or flat monthly costs. ## 3. Docs and Examples Save time with pre-built templates and tested configs. ## 4. Scalability From indie creators to enterprise platforms. ## 5. Community & Support Active Discord and responsive updates. ## Final Thoughts: Start Creating with Kling AI via PiAPI Today Our Kling API offers the best mix of pricing flexibility, feature depth, and clear documentation. Use pay-as-you-go for experimental projects, then upgrade to host-your-account for production-scale workloads, and finally dive into Pro and Master modes for cinematic-level output. We make Kling AI accessible, affordable, and developer-friendly. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## Frequently asked questions ## What is Kling? Kling is the state of the art video generation AI model developed by Kuaishou, which is one of the world's largest video-based social network with a user base over 400 million users. This model allows users to create high quality, realistic videos from user-input text prompts, static images, or existing videos. The generated videos can be extended up to minutes in length, are physically accurate, and allows for various aspect ratios. ## What is the Kling API? Since Kuaishou currently does not offer API for their Kling model, therefore PiAPI took on initiative and created the unofficial Kling API for developers worldwide, allowing you to integrate text-to-video, image-to-video, and video-to-video capabilities into your application or platform! ## What type of videos can I make with Kling API? Our Kling API can help users generate cinematic videos all types. Our API also enables the creation of video extensions from original videos. Each extension introduces an additional 4.5 seconds of content, precisely controlled by user text prompts! ## What are the current limitations with regarding to the Kling Model? Sometimes the generated resolution is not as high as users would hope for (i.e. 1080p), but with time and as inference infrastructure become more robust from Kuaishou, it will most likely solved with more computing power. ## Can I use videos generated from the Kling API for commercial purposes? We'd recommend waiting for more information from Kuaishou regarding this issue. Currently all videos generated will have the Kling watermark, and some users might try to remove the watermark with third-party tools. However, to avoid potential copyright issues, it would be more prudent to adhere to existing and future copyright terms from Kuaishou. ## Are there refunds? No, we do not offer refunds. But when you first sign up for an account on PiAPI‘s Workspace, you will be given free credits to try our Kling API (specifically the "Pay-as-you-go" service) before making payments! ## How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! ## Automate Midjourney & Kling Videos in n8n — Free PiAPI Workflow Template (2025) Automate short videos using GPT-4o, Midjourney, and Kling APIs via PiAPI’s free n8n workflow. Step-by-step integration guide for developers and creators. Imagine sketching out a scene where a rabbit by a forest stream, golden light breaking through the leaves and watching it come alive as a short animated video in just minutes. No cameras. No editing suites. Just prompts, a workflow, and PiAPI’s AI models powering the visuals, sound, and storytelling.That’s exactly what our n8n workflow template delivers. By combining GPT-4o image generation , Midjourney , Kling , and Creatomate , you can automatically generate dynamic short videos for social posts, vlogs, or brand campaigns. ## Integrating PiAPI Into Your n8n Workflow Automation Creators and developers are increasingly turning to APIs to chain AI tools together, skipping manual clicks and tedious edits. We make this easy. We unify multiple APIs like Midjourney API , Kling API , GPT-4o API (OpenAI API) and more into a single platform. Pair it with n8n, an open-source visual workflow tool, to unlock scalable content generation. Think daily social videos, branded campaigns, or dynamic storytelling with minimal effort. ## How to Integrate Midjourney, Kling & GPT-4o in n8n (Step-by-Step Guide) Developers and creators are chaining APIs to automate creative workflows. With PiAPI , you can connect multiple AI tools — Midjourney API , Kling API , GPT-4o API , and more — through n8n , an open-source workflow builder. This guide walks you through setting up the PiAPI n8n template to automate AI-generated video creation , ideal for developers, social creators, and agencies who want to save time without losing quality. ## Free Trial & API Pricing (2025) PiAPI Subscription Plans Before we dive in, here’s what makes PiAPI’s access simple and developer-friendly: - 1. Free Trial Access: Test prompts instantly with our Playground . Pro Tip: Start with the Free Tier, then scale to Creator or Pro as your workflow grows. - 2. Transparent Pricing: Predictable usage, no token confusion. - 3. All-in-One Platform: Use 20+ APIs (Midjourney, Kling, GPT-4o, etc.) with one key. Get your FREE PiAPI key now! ## n8n Workflow Template Setup This prebuilt template connects PiAPI’s GPT-4o , Midjourney , Kling , and Creatomate APIs in one automated flow. Perfect for: - 1. Social media creators: Generate daily short videos. - 2. Developers: Prototype AI workflows. - 3. Educators: Automate animated explainers. - 4. Agencies: Produce quick product ads or social campaigns. ## Use The n8n Template Select the n8n template → “Use for free” → “Get started with n8n Cloud” (Recommended) . ## Add Your API Key In the Basic Params node, enter your PiAPI key under "X-API-key" . ## Set Your Prompts Define the scenario for both image and video prompts. ## Choose A Video Template On Creatomate How to create the Creatomate template Select a Creatomate template and make an API call in the final node, with the core and processing modules provided. Tip: Start with basic assets on Creatomate for a quick prototype demo. Once you're satisfied with the result, integrate with n8n for full runs. ## Configure and Test Fill in your account details following the provided image guideline. ## Run and Test Workflow Click on “Test Workflow” and wait 10–20 minutes for video generation. ## Optional: Add Audio or Music In this setup, you’re building a foundational image-to-video workflow with subtitle integration. From there, you can extend functionality with audio nodes with PiAPI’s models. All final assets are composited via Creatomate. Here are some audio models PiAPI offers: - 1. Mmaudio - 2. Ace Step - 3. Udio - 4. Kling Sound - 5. DiffRhythm - 6. TTS For best practice, please refer to PiAPI's official API documentation or Creatomate's API documentation to comprehend more use cases. ## Example: Midjourney + GPT-4o + Kling Workflow Output ## Params Settings Style: A children’s book cover, ages 6–10. --s 500 --sref 4028286908 --niji 6 Character: A gentle girl and a fluffy rabbit explore a sunlit forest together, playing by a sparkling stream. Situational Keywords: Butterflies flutter as golden sunlight filters through green leaves. Warm, peaceful atmosphere. ## FAQs ## Q1: How do I integrate Midjourney API in n8n? Use the HTTP Request node with your PiAPI key and Midjourney endpoint for prompt-to-image generation. ## Q2: What’s included in PiAPI’s free workflow template? The full pipeline includes GPT-4o (script generation), Midjourney (image), Kling (animation), and Creatomate (video rendering). ## Try It Yourself: Sign Up For A Free API Key Use this workflow for free on n8n with your PiAPI key and start generating animated stories in minutes. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## FREE Nano Banana API Pricing & Key Access (2025) – Google Gemini 2.5 Flash via PiAPI Get free Nano Banana API credits via PiAPI. Simple $0.03/image pricing, free API key, and Gemini 2.5 Flash Image integration — perfect for creators and businesses. Tired of expensive, slow AI editors? The Nano Banana API lets you create on-brand images faster — for free. Whether you’re replacing Flux or scaling edits across campaigns, this tool helps you cut costs without losing quality. The Nano Banana API, Google Gemini 2.5 Flash Image model, delivers ultra-fast, consistent image generation. Through PiAPI , you can now get free trial credits and unlock enterprise-grade pricing starting at just $0.03 per image . ## Nano Banana API Free Trial & Pricing (2025) While the official Gemini API is powerful, it often leads to trial-and-error costs and wasted tokens. With Nano Banana API via PiAPI, you only pay for generated images (no hidden token leakage) as pricing is simple and predictable at $0.03 per image (JPEG & PNG) . Generate up to 4 images per request. Furthermore, our Free subscription gives you access to a free Playground trial to experiment before committing! We also offer a webhook-ready, JSON-based integration for easy use. Get your free API key and enjoy FREE credits monthly under Creator and Pro plans. See also Nano Banana API vs Flux Kontext API [2025]: Pricing, Speed & Free Playground . ## Nano Banana Key Technical Strengths ## 1. Flexible edits Edit the background to day time in the fall season. Add realistic autumn leaves on the ground and adjust lighting to match a crisp autumn day. Swap, remove, or add objects while keeping everything looking natural — no awkward mismatches or broken details. See how it compares to Flux Kontext, Nano Banana WINS over Flux Kontext: AI Image Editing Showdown . ## 2. Scene + Character Identity Preservation Make the same figurine smile and wave, keeping proportions and lighting exactly the same. Maintain a consistent style and character identity across multiple generations. Perfect for brands that need recognizable mascots, figurines, or recurring visuals. ## 3. Multi-Image Workflows Generate a product photo like this using the red can of coke Combine multiple reference photos in one workflow to guide outputs more precisely. This makes it a powerful tool for complex projects and custom branding. ## 4. Product And Creative Use Cases Create a product shoot of the model standing and carrying the bag From e-commerce product shots to creative mockups, virtual try-ons, or storytelling campaigns, Nano Banana unlocks a wide range of business and marketing applications. ## Nano Banana API Documentation & Integration Guide ## 1. Get Your Free API Key Sign up for your FREE API key and explore the Playground ! ## 2. Make Your First API Request (Code Example) Integrate Nano Banana in under 5 minutes with PiAPI’s Nano Banana API Documentation . ## 3. Build Into Your Apps, Workflows or Automations Plug the API into n8n automations and many more! ## Why Developers Choose PiAPI for Nano Banana - 1. Unified key for various AI models (like Kling, Flux and MMAudio). - 2. Playground access with no setup or SDK installs. - 3. Transparent usage dashboard and rate-limit management. ## Frequently Asked Questions ## What is Nano Banana API pricing? $0.03 per image, with up to 4 images per generation (~$0.12). ## Can I use Nano Banana API commercially? Yes, outputs come with full commercial rights. ## How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! ## Final Thoughts: Try Google Gemini 2.5 Flash for Free Today Ready to try Gemini 2.5 Flash Image for free? Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## Nano Banana API Documentation & Pricing (2025) — Try Gemini 2.5 Flash via PiAPI Discover Nano Banana — Google Gemini 2.5 Flash for image generation and editing. Try the free API via PiAPI to create stunning visuals instantly. Nano Banana is no longer just a buzzword in creator forums — it’s now confirmed as Google’s Gemini 2.5 Flash Image model . Fast, consistent, and tuned for natural editing, Nano Banana is changing how developers and creators approach image generation. The best part? You can access the Nano Banana API for free via PiAPI , with transparent pricing and a no-code Playground. Here’s everything you need: documentation, pricing, and how to grab your free API key today. ## Why Choose Nano Banana? Nano Banana is Google’s dual-purpose image generator and editing model. Unlike most tools that force you to test endless prompt variations, the model understands plain, natural language. This means you don’t have to spend your credits testing five different phrasings just to get one good result. Instead, you can simply say “remove the background and make this look like a studio product shot” or “make the character wave while staying in the same style” . It’s faster, cheaper, and far more intuitive. ## Nano Banana in Action: 3D Figurine Trend One trend already taking off with Nano Banana ? 3D figurine-style renders. Gemini Nano Banana 3D Figurine of Rumi from K-Pop Demon Hunters. Example Prompt: “Create a 1/7 scale commercialized figure of the character in the photo. The style should be realistic, with clearly defined features, and placed in a real-world environment. The figure should be positioned on a computer desk, standing on a round transparent acrylic base with no text. On the computer screen, display the Adobe Illustrator modeling process of this figure. Next to the screen, place a BANDAI-style toy packaging box printed with the original photo." This is the kind of product-style output that used to take hours of manual editing — now it’s just one prompt away. Explore more stunning images here , from text-to-image, anime, line art and creating characters. ## Nano Banana API Documentation Here’s how to start: - 1. Get Your Free API Key - Sign up at PiAPI (no billing required for the Free Plan). - Access the Nano Banana API Docs . - API Request Example { "model": "gemini", "task_type": "gemini-2.5-flash-image", "input": { "prompt": "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half, revealing both the dogs head above water holding a tennis ball in it's mouth, and it's paws paddling underwater.", "num_images": 1, "output_format": "png" }, "config": { "webhook_config": { "endpoint": "https://webhook.site/8c547d77-8ff1-4cf0-8cd7-b66b18953a88", "secret": "" } } } Visit the full Gemini 2.5 Flash docs for the full params with more accurate formatting for your use. ## Nano Banana API Pricing (2025) - 1. Free Plan → Includes Playground trial & basic API access. - 2. Per Image Cost → $0.03 per image (JPEG/PNG). - 3. Bulk Requests → Up to 4 images per request. - 4. Credits Included → $10 FREE credits on Creator plan, $60 on Pro. Transparent, no token leakage: you only pay for what you generate. ## Why Use PiAPI for Nano Banana? - 1. Free API key + Playground trial (no credit card needed) - 2. Predictable pricing — $0.03 per image - 3. No-code testing before integration - 4. Webhook-ready for automation - 5. Commercial rights included . ## Try Nano Banana API for Free Today! We make it simple to test and integrate Google Gemini’s Nano Banana model into your apps, workflows, or creative projects. Start with the free trial, explore the Playground and unlock consistency at scale. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## Frequently Asked Questions ## What is Gemini 2.5 Flash Image API? Gemini 2.5 Flash is Google's latest AI image generation model, optimized for speed and quality. Our API provides easy access to this cutting-edge technology, allowing you to generate high-quality images from text prompts in seconds. ## How much does it cost to generate images? Our pricing is simple and transparent: $0.03 per image generated. You can generate up to 4 images per request, making it cost-effective for bulk generation. There are no subscription fees or hidden costs - you only pay for what you use. ## What image formats are supported? We support both JPEG and PNG output formats. You can specify your preferred format in the API request. JPEG is great for photographs and complex images, while PNG is ideal for graphics with transparency or sharp edges. ## How fast is the image generation? Gemini 2.5 Flash is optimized for speed. Most images are generated in under 10 seconds, making it perfect for real-time applications, interactive tools, and user-facing products that need immediate results. ## Can I use generated images commercially? Yes! All images generated through our Gemini 2.5 Flash API come with full commercial usage rights. You can use them in your products, marketing materials, websites, or any commercial application without additional licensing fees. ## What is the no-code workspace? Our no-code workspace at piapi.ai/workspace/nano-banana allows you to test the API without writing any code. Simply enter your prompts, adjust settings, and generate images directly in your browser. It's perfect for testing ideas before API integration. ## Do you support webhooks? Yes, we provide webhook support for asynchronous processing. You can specify a webhook endpoint in your request, and we'll notify you when your images are ready. This is ideal for long-running tasks and automated workflows. ## How do I integrate the API into my application? Integration is straightforward with our REST API. Simply make a POST request to our endpoint with your API key and image generation parameters. We provide comprehensive documentation and code examples for popular programming languages at our API docs . ## What kind of prompts work best? Gemini 2.5 Flash understands detailed, descriptive prompts. Include information about style, composition, lighting, colors, and mood. For example: 'A serene mountain landscape at sunset with vibrant orange and purple skies, photorealistic style' works better than just 'mountain'. ## Is there a limit on image generation? There are no hard limits on the number of images you can generate. However, you can generate up to 4 images per API request. For high-volume usage, we recommend implementing proper rate limiting and consider our enterprise plans for enhanced support. ## How can I get in touch with your team? Please email us at support@piapi.ai - we'd love to talk more regarding our product offering! ## Save 60% Off With PiAPI Luma Dream Machine API (2025) — Free vs Paid Plans Compared Compare Luma Dream Machine pricing (2025) with free vs paid plans via PiAPI. See pricing, features, API costs, and a Fal.ai comparison to choose the best plan for your AI video projects. Luma Dream Machine 2 has become one of the most searched AI video tools in 2025 — especially for developers looking to integrate video generation into apps and workflows. With PiAPI , you get direct API access , transparent pricing, and free credits to test it before scaling. This guide covers the Luma Dream Machine Free vs Paid Plans (2025) , API access steps, and how much credits really save you compared to alternatives like Fal.ai. If you’re new to this AI video tool, find out more about Luma AI Dream Machine in our previous blog . ## Luma Dream Machine Pricing Plans (2025): Free, Creator, Pro, Enterprise With PiAPI Here’s a clear look at PiAPI’s subscription tiers for Luma Dream Machine API access: ## Free Plan ($0/month) Great for enthusiasts who just want to try things out. You’ll get basic API access, limited task speed, and a Playground trial package to experiment with. ## Creator Plan ($15/month) Perfect for indie developers or side projects. Includes unlimited task processing speed, email or ticket support, free file-to-URL conversion and $10 FREE monthly credits. You’ll also unlock extended API access and more advanced task types. ## Pro Plan ($60/month) Built for developers running official applications. Receive $60 FREE monthly credits , all API task types, one-on-one live support, plus custom invoices and aggregated billing for smoother project management. ## Enterprise Plan ($100/month) Best for scaling teams. Includes 3× Pro concurrency, up to 100 sub-accounts, and unified team billing with balance management — ideal for large-scale projects. Pro tip: If you’re running real apps, the Pro Plan essentially covers itself with $60 in included credits ## API Service Pricing We also offer two API access methods at the following costs: - 1. Pay-as-you-go (PAYG): $0.20 per video task; and - 2. Host-your-account (HYA): $10 for per seat monthly. ## PiAPI vs Fal.ai: Video Generation Costs Compared Fal.ai is a popular API model but, they charge $0.50 per video generation. In comparison, our PAYG access for Luma Dream Machine 2 (2025) comes in at only $0.20 per generation . That’s a 60% savings! For every 100 videos generated, it'll cost $20 with PiAPI vs $50 with Fal.ai. For creators experimenting with music videos, short ads, or animations, that cost difference adds up fast. ## PiAPI Luma Dream Machine API Features We provide access to all the core Luma Dream Machine features through its API. Here’s what you can do at a glance: - 1. Text-to-Video Generation – Turn written prompts into dynamic motion content. - 2. Image-to-Video Generation – Animate still images into smooth, AI-driven clips. - 3. Add End Frame * – Perfect for looping videos with a polished start-to-end transition. - 4. Video Extension * – Extend clips seamlessly without losing context or style. - 5. Watermark Removal* – Generate clean outputs ready for production use. *Note: Add End Frame, Video Extension and Watermark Removal are available only on Creator, Pro, and Enterprise plans — not included in the Free tier. For a deeper dive, check out our complete 2025 Luma Dream Machine API documentation guide. ## Free Credits and Trial Access for Luma Dream Machine - 1. Free Playground Trial → Experiment instantly with no setup and no payment required. - 2. Creator Plan → $10 free credits monthly. - 3. Pro Plan → $60 free credits monthly (effectively offsets subscription cost). - 4. Enterprise → Custom credits & scaling. 👉 Start your free trial here ## FAQs ## What’s new in Luma Dream Machine (2025 release)? Better motion realism, longer clip stability, new developer parameters. ## How do I use the Luma Dream Machine API via PiAPI? Sign up with PiAPI, generate an API key, and integrate Dream Machine into your app or workflow. ## Where can I find the Luma Dream Machine API documentation 2025? Full docs are available through PiAPI here . ## Why Choose PiAPI for Luma Dream Machine? - 1. Lower per-video cost than Fal.ai - 2. Free credits every month* - 3. Direct API access with webhook support - 4. Playground testing before integration - 5. Commercial rights included 👉 Get Your API Key Today *Terms and conditions apply ## Experiment With PiAPI's Free Trial Try our free trial and explore the Luma Dream Machine playground ! See how easy it is to turn ideas into videos. Get started with PiAPI now and unlock a world of AI models, all in one place. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. Check out our pricing plans to see which fits your needs best. ## Flux AI Image Editing Capabilities (2025): Flux Playground, Kontext API & Pro Models Discover Flux Kontext by Black Forest Labs — the most intuitive AI image generator. Edit with Flux AI Playground or scale via Flux AI image editing capabilities API. ## A Single Prompt, Endless Possibilities with Flux AI Picture this: “replace the city skyline with a jungle backdrop” — and the edit happens instantly. Developed by Black Forest Labs , Flux Kontext delivers professional-grade workflows for creators, developers, and businesses. With Flux AI and the Flux Kontext model , advanced tools like Redux Variation, Fill, Inpaint, and Outpaint make image editing effortless. Whether you’re testing in the Flux Playground or scaling projects through the Flux AI image editing capabilities API documentation , this platform is redefining what an AI image generator and AI art generator can do. In this blog, we're focusing on it's unbeatable editing features on our PiAPI platform. Get started with FREE credits when you register via PiAPI. * *Terms and conditions apply ## Why Flux Kontext Is a Breakthrough In AI Image Editing Here’s what makes this Flux AI model stand out: - 1. Iterative Editing : Refine images step by step while keeping coherence. - 2. Character Preservation : Preserve identities, objects, and styles across edits. - 3. Fast Workflow : Ideal for creatives needing quick results. - 4. Scene Understanding : Intelligent comprehension of context, depth, and composition. Together, these features make Flux Kontext one of the most powerful image generators on the market. ## Flux API Pricing & Flux Models (2025) Pay-as-you-go (PAYG) and scale with your needs. - 1) Flux Schnell – $0.0015 per image (1–4 images per generation) . - 2) Flux Dev – $0.015 per image. - 3) Flux Dev-Advanced – $0.02 per image. Note: Dev-Advanced and Superresolution require a Creator or higher subscription plan . 👉 Check out our pricing plans and start with a FREE trial. ## Creative Extensions with Flux Playground Flux isn’t limited to basic edits but a tool for full creative exploration. Inside the Flux AI Playground , you can test prompts before scaling with our API. Select the "img2img-kontext" task type and try swapping environments, changing image art styles and more. ## Prompt Example with Flux Kontext API Here’s a test-ready command you can copy onto Flux Kontext . Prompt: Put the woman (subject) in a jungle while keeping her original position and framing.Using the Flux Kontext API, this edit preserves subject framing while transforming the environment. Try it in the Flux Playground or run it as part of your API workflow. ## Results Input vs Output Using Flux Kontext , this edit preserves subject framing while transforming the environment. Try it in the Flux Playground API or run the API as part of your AI generator workflow. ## Step-by-Step Workflow ## Step 1: Access Flux via PiAPI Register for an API key, load credits, and visit the Flux API documentation . Explore our FREE subscription plan upon registration. ## Step 2: Create Kontext Task Image Generation Flux Kontext runs as part of the model library, and Flux API through PiAPI utilises the Flux Dev model for Kontext generation. Interactive testing is also available via the Flux Playground API . ## Scale with API Calls We provide comprehensive API documentation with detailed examples in multiple programming languages including Python, Node.js, PHP, and cURL. Our interactive API explorer lets you test endpoints directly in your browser with real-time examples and response previews. Flux Dev pricing starts at $0.02 per image but models like Flux Pro and Flux Kontext Max offer higher fidelity. ## FAQs ## Who are PiAPI? At the intersection of tech and creativity, we are a group of dedicated tech aficionados who believe in the transformative power of AI. We are driven by our passion for simplifying AI and making it accessible to developers around the globe. We provide developers with API access to generative AI models including Flux API, Ace-step API and FaceSwap API, helping to develop scalable and innovative applications! ## What is Flux Kontext API? Flux Kontext API is a powerful image-to-image editing service that allows you to transform images using natural language prompts. It offers multiple model variants ( Pro , Max , and Dev ) with different speed and quality trade-offs. The API supports various editing tasks including style transfer, object modification, background replacement, text editing, and more. Built by Black Forest Labs, it provides production-ready AI image editing capabilities through simple REST API calls. Pricing starts at $0.02 per image (Flux Dev) , with premium tiers like Pro and Max offering higher fidelity. Free credits are included at signup. ## Can I use Flux Kontext for commercial projects? Yes! All Kontext models come with full commercial use rights. You can integrate them into your products, services, and client work without any additional licensing fees or restrictions. ## What image formats are supported? Kontext supports common image formats including JPEG, PNG, and WebP. Input images should be under 10MB for optimal performance. Output format matches your input format by default, but can be specified in your API request. ## Conclusion: The Go-To Image Editing AI model Flux Kontext combines the polish of professional editing with the ease of natural prompts. It delivers consistent results, all while scaling seamlessly through API. Start now with the Flux AI Playground or integrate via the Flux API . Check out our pricing plans to see which fits your needs best. Test Flux Dev, Flux Pro, or Flux Kontext Max with FREE credits and experience intuitive AI editing at scale *. *Terms and conditions apply ## How to Build Fortnite-Style Game Assets with Trellis 3D API via PiAPI Learn how to create Fortnite-style game assets using the Trellis 3D API with PiAPI. This historical guide uses a Midjourney example; PiAPI no longer provides that service. Fortnite’s cartoony yet high-fidelity assets have become the gold standard in modern game design. But creating those vibrant characters, weapons, and environments is notoriously expensive and time-consuming. Studios often spend weeks modeling, texturing, and iterating just to get a single asset ready for gameplay. PiAPI put the model to the test and generated Fortnite-inspired game assets with Trellis 3D API . The Midjourney images in this historical article are examples only; PiAPI no longer provides Midjourney. Fortnite-style house game asset Fortnite-inspired weapon asset Fortnite-style pirate character asset ## What is the Trellis 3D API? Trellis is a Microsoft-backed model designed for 3D asset creation. Instead of relying on slow, traditional modeling pipelines, we provide Trellis as an API, making it accessible for direct integration into developer workflows. With our Trellis 3D API , you get: 1. Fast generation: Convert text or image inputs into structured 3D assets within minutes; 2. Developer-friendly: Easy API integration for automated pipelines; 3. Scalable : Programmatically generate batches of assets to meet large project demands; and 4. Engine-ready: Outputs are optimized for immediate use in leading 3D engines and design environments. The real value lies in its reliability and scalability. Developers can move beyond experimentation and build consistent, production-ready 3D pipelines. ## Trellis API with PiAPI: Pricing Plan With Trellis 3D , that process gets dramatically faster. Developers can now generate, refine, and deploy game assets in hours instead of weeks. As an image-to-3D model, we make it possible to scale your workflow without scaling costs. In fact, we take this even further: one subscription unlocks access to Trellis and multiple AI models under a single, developer-friendly API. Register for an account, then jump into the Playground and test prompts immediately with our FREE Playground trial package. With PiAPI , accessing over 20 AI models is straightforward and predictable. No hidden token costs, no complex billing. Pay only for what you generate. Our Free Tier Access allows you to experiment with with basic APIs and access our Playground, while subscribing to Creator and Pro plans gives you greater benefits such as FREE credits monthly, additional API access and priority support. Check out our pricing plans to see which fits your needs best. ## Why Choose PiAPI? One of the biggest challenges with 3D content creation is juggling multiple tools. That’s where we stand out. With a single subscription, you get access to supported AI models such as Trellis in one place. Midjourney references below are historical examples and are no longer available through PiAPI: - 1. Use a currently supported image model for concept art. Midjourney via the PiAPI Playground is no longer available. - 2. Transform those images into fully-formed 3D models using the Trellis 3D Playground and run the API when you're ready. - 3. Manage everything with unified billing and consistent developer-friendly APIs. Instead of switching platforms and paying for separate subscriptions, we give you the ease to handle concept-to-3D output all in one pipeline. ## Step-by-Step: Creating Fortnite-Style Assets Here’s a simple guide you can follow to replicate a Fortnite-style asset pipeline with PiAPI : ## 1. Concept Creation Use a currently supported PiAPI image model to create stylized concept art for characters, weapons, or environments if you don't have a sketch. Midjourney via the PiAPI Playground is no longer available. ## 2. 3D Generation Feed your concept images into the Trellis 3D API , which generates structured, game-ready 3D models. ## 3. Deploy in Gameplay Export models for seamless use in your preferred game engine. In just a few steps, you can turn an idea into a playable Fortnite-style asset — without weeks of manual modeling. ## Benefits for Developers ## 1. Speed Reduce asset creation time from weeks to hours. ## 2. Cost One PiAPI subscription unlocks access to multiple AI models. ## 3. Flexibility Combine a supported PiAPI image model with Trellis for a full creative-to-3D pipeline. Midjourney is a historical example and is no longer provided by PiAPI. ## 4. Scalability Batch-generate assets via API for faster iteration and prototyping. ## Conclusion: Generate Game Assets Faster and Smarter The Trellis 3D API makes building Fortnite-style game assets more efficient and scalable. And with PiAPI , you get all-in-one access to over 20 AI models, enabling you to go from concept to game-ready assets without leaving one ecosystem. Start building with Trellis 3D and unlock the fastest path to creating your own Fortnite-style assets. See current PiAPI plans for supported models. Midjourney is no longer available through PiAPI. ## From Image to 3D Models: How Trellis is Changing the Game Discover how Microsoft’s Trellis, available via PiAPI, turns images into 3D models in minutes. Learn how image-to-3D technology accelerates game design, AR/VR, e-commerce, and more with fast, scalable workflows. In today’s digital world, visuals drive experiences. From blockbuster video games to immersive AR/VR environments, high-quality 3D models have become the backbone of interactive content. But here’s the challenge: creating 3D assets from scratch is still one of the most resource-heavy tasks in design pipelines. With the introduction of image-to-3D technology, the digital gaming space is emerging. Microsoft’s Trellis , now available via PiAPI , is leading the way. ## What Does “Image to 3D” Mean? At its core, image-to-3D refers to the process of transforming a 2D image (like a concept sketch, reference art, or even a real-world photo) into a usable 3D model. Instead of manually sculpting every polygon, algorithms analyze depth, structure, and texture to generate a digital object that can be immediately refined or deployed.It’s a breakthrough because it bridges the gap between concept and creation that turns flat ideas into fully dimensional assets faster than ever. ## Why Trellis 3D Stands Out Trellis isn’t just another experimental AI tool but a production-grade model built for speed, scale, and developer workflows. We offer: - 1. Faster Prototyping – Go from image concepts to interactive 3D in minutes. - 2. Scalable Workflows – Generate large batches of models programmatically. - 3. API-First Integration – Automate asset pipelines for games, simulations, and digital twins. - 4. Consistent Output – Structured 3D geometry optimized for real engines. Instead of spending hours on manual modeling, PiAPI makes Trellis a quick solution for scalable 3D asset creation, whether you’re prototyping a single object or generating assets at production scale. This makes it ideal not only for creative studios but also for enterprises, indie developers, and researchers who need reliable 3D generation at scale. Don't believe in the power of Trellis 3D? Check out this blog to see how Trellis compares to others! ## Real-World Use Cases of Image-to-3D With Trellis AI The power of Trellis extends far beyond gaming. Some examples include: - 1. Game Studios : Rapidly creating characters, props, and environments. - 2. E-Commerce : Turning product images into interactive 3D showcases. - 3. Education & Training : Generating realistic models for simulations. - 4. AR/VR : Building immersive worlds from simple image prompts. In each case, the shift from manual pipelines to automated image-to-3D workflows allows teams to focus more on creativity and less on technical overhead. Learn more about Trellis API requests with PiAPI here and start integrating Trellis into your workflow today. ## How To Use Trellis via PiAPI Getting started is straightforward. PiAPI allows access to both text-to-3D and image-to-3D endpoints with a simple API call—no specialized training or infrastructure required. ## Sign Up For PiAPI: Simple, Transparent and Developer-Friendly PiAPI Subscription Plans Sign up for an account and upload an image through PiAPI's Playground to test prompts immediately with our FREE Playground trial package. Alternatively, run the API , send the request and download your structured 3D model that’s ready for refinement in your pipeline or direct deployment into a game engine. Our Free Tier Access allows you to experiment with with basic APIs and access our Playground, while subscribing to Creator and Pro plans gives you greater benefits such as FREE credits monthly, additional API access and priority support. With PiAPI, accessing over 20 AI models is straightforward and predictable. No hidden token costs, no complex billing. Pay only for what you generate. Check out our pricing plans to see which fits your needs best. ## Conclusion: Explore Trellis 3D with PiAPI The ability to transform an image into a 3D model has long been a bottleneck in digital creation, but Trellis 3D eliminates that. From gaming to retail to AR, the workflows of tomorrow are being built today with faster, scalable, and more accessible 3D generation. If you’ve ever imagined turning your 2D ideas into fully-realized 3D assets, Trellis is here to make it real. Subscribe now and unlock fast, scalable access to the AI models that power your next creative or developer project. Unlock the power of 20+ AI models with PiAPI — image, video, chat, music, and more. Sign up today and start building smarter, faster, and at scale. ## Building Open-World Game Assets with Hunyuan Video AI — A GTA-Style Example Discover how Hunyuan Video AI, powered by Tencent and available via PiAPI, helps developers generate consistent GTA-style game assets — from characters and vehicles to immersive city environments. Creating a sprawling open-world game like Grand Theft Auto requires thousands of consistent, high-quality assets — from characters and vehicles to environments and NPCs. Traditionally, this demands large teams and extensive development time. But with Hunyuan Video AI , developers can now rapidly prototype game assets while maintaining visual consistency across frames and variations. ## What is Hunyuan Video AI? Hunyuan Video AI , powered by Tencent , is a multi-modal, subject-consistent AI platform that generates video and 3D assets from text, image, audio, or video prompts. Unlike other AI tools, Hunyuan ensures that characters, objects, and environments remain recognizable and uniform throughout the project — a critical requirement for game development . ## PiAPI Hunyuan API Service With PiAPI , developers can access Hunyuan Video AI via a developer-friendly API, enabling easy integration into games, apps, or creative projects. This flexibility allows developers to generate consistent 3D assets, character animations, and cinematic scenes without complex setup. PiAPI supports a variety of task types, including: - 1. hunyuan-txt2video: Generate videos directly from text prompts - 2. hunyuan-txt2video-lora: Apply fine-tuned styles with LoRA models - 3. hunyuan-fastvideo-txt2video: Quickly prototype animations and scenes - 4. hunyuan-img2video-concat: Generate images into videos - 5. hunyuan-img2video-replace: Replace characters or objects in existing footage Check out the Hunyuan Playground and explore the API Docs to start experimenting today! Find out which task to use more accurately here . ## Why Consistency Matters in Open-World Games Open-world games like GTA involve multiple scenes, animations, and interactive objects. Any inconsistency in a character model or environment can break immersion and increase manual correction time. With Hunyuan Video AI, developers can: - 1. Maintain consistent character design across multiple animations. - 2. Generate vehicles and props in a uniform style. - 3. Produce city environments and NPCs that match the overall art direction. ## Case Study: GTA-Style Asset Generation ## Character Models Generate main characters and NPCs that remain consistent across different poses and animations. Prompt Example A realistic 3D character model of a young street racer wearing a leather jacket, sneakers, and sunglasses. Full body, neutral T-pose, consistent proportions, game-ready style. ## Vehicles Design cars, motorcycles, and other vehicles with style consistency for city streets or racing sequences. Prompt Example A detailed 3D model of a sports car inspired by Los Angeles street racing culture. Sleek design, metallic red paint, consistent proportions. Game-ready rendering. ## Environments Generate immersive city streets, parks, and interiors that align with the game’s visual theme. Prompt Examples A realistic 3D city street environment at night, neon signs, parked cars, graffiti walls. Cinematic lighting, consistent building style across frames. A 3D urban park with palm trees, benches, and skate ramps. Playful vibe, game environment asset pack. ## Animations Produce multiple animations for characters without losing style consistency. Prompt Example Character runs forward, arms swinging naturally, proportions and style consistent across frames. ## Hunyuan vs Other AI Tools While tools like Genie 3 can generate 3D assets, Hunyuan’s advantage lies in subject consistency and multi-modal control. Deve lopers can refine prompts iteratively without breaking style uniformity, making it ideal for GTA-style asset generation and other large-scale game projects. ## Conclusion: Experiment With Hunyuan API Now! From characters and vehicles to city streets and NPCs, Hunyuan Video AI empowers developers to prototype and animate GTA-style worlds faster than ever . Its ability to maintain consistent asset design across multiple scenes and frames not only saves time but also ensures a polished, immersive experience for players. Developers and indie studios can experiment with Hunyuan to accelerate game development pipelines , create immersive open-world environments, and maintain professional asset quality with minimal manual intervention. Sign up for PiAPI today to access a wide array of powerful AI models and start building your next project! Explore our API documentation s, review pricing plans , and join a growing community of developers pushing the boundaries of AI-powered creativity. ## The Future of Hailuo AI: Storytelling, Ads & E-Commerce Made Easy Discover how Hailuo AI empowers creators to make cinematic videos, engaging ads, and shoppable e-commerce content. Learn how to start in the playground and scale with the API. ## Video Ads That Actually Convert With Hailuo AI Most ads get skipped. Why? Because they feel like ads. With Hailuo by Minimax, creators can design cinematic ad spots that feel like mini-movies, not interruptions. - 1. Faster ideation: Type a prompt like “urban rooftop, neon skyline, model holding product, slow cinematic zoom” and instantly generate ad footage. - 2. Endless variations: Swap backdrops, moods, or angles without booking a new location. - 3. High engagement: Short, polished AI videos hold attention better than static content, making them perfect for TikTok, Reels, or YouTube Shorts. Instead of spending weeks on a single ad shoot, you can create multiple versions in hours, test what resonates, and double down on the winning cut. See some sample prompts and generations we really like here ! ## Storytelling for Creators Made Easy Through Minimax Every creator knows the challenge: you have a story in your head, but not the budget or tools to film it. Hailuo AI bridges that gap. - 1. From script to screen: Write out your story in a few sentences, and Hailuo turns it into a multi-scene narrative video. - 2. Keep characters consistent: Want the same protagonist across shots? Upload a reference image, and Hailuo makes sure your hero stays recognizable. - 3. Experiment freely: Play with genres (sci-fi, fantasy, slice of life) and get instant cinematic results through the PiAPI Playground. For creators, this means less worrying about cameras, actors, or expensive edits — and more time focusing on your voice, your story, your creativity . ## E-Commerce That Sells Through Emotion Through Hailuo AI VideoProduct videos don’t just show items anymore, they tell stories . Hailuo AI helps e-commerce brands and creators create shoppable stories that connect emotionally. - 1. Showcase a sneaker in action on the court , not just on a shelf. - 2. Place a skincare product in a luxury spa setting without ever booking a location. - 3. Build themed campaigns — holiday vibes, futuristic drops, or lifestyle aesthetics — at a fraction of the cost. The result? Scroll-stopping content that drives clicks, boosts conversions, and makes your product stand out in crowded feeds. ## Integration Guide: From Hailuo Playground to Hailuo API Most creators will be fine starting in the Hailuo playground — type, generate, download, and post. But for power users and teams, the Hailuo API unlocks serious workflows: - 1. Batch-generate assets for multiple campaigns. - 2. Automate content pipelines, like daily TikTok uploads. - 3. Integrate into creative tools (e.g., Notion, Figma, or your CMS). ## Getting started is simple: - 1. Sign up for a PiAPI plan that suits your needs and explore our free subscription. - 2. Experiment with prompts in the playground . - 3. Once you know your style, plug into the API for scale . This gives you an opportunity to start small as a solo creator and scale like a production studio when you’re ready. ## Final Thoughts: Make Ads, Tell Stories and Sell Products Hailuo AI is a creative accelerator. Use the playground for quick results, then scale through the API when you’re ready to push your content further. Sign up for PiAPI today and get full access to Hailuo API’s powerful capabilities! Explore our Hailuo API documentation, review pricing plans , and join a growing community of developers pushing the boundaries of AI-powered creativity. ## The Rise of Video AI: Why Models Like Hailuo AI Are Leading the Next Wave Discover Hailuo AI by Minimax — the next wave of video AI. From text-to-video to Hailuo 2’s 1080p generation, see why creators and developers are switching. The AI world is moving fast, but one frontier is stealing the spotlight: video AI. Models like Hailuo AI by Minimax, are rewriting the playbook for how creators, developers, and businesses think about video. From generating cinematic visuals on command to enabling seamless integration through APIs, video AI is quickly becoming the next big leap in creative technology. ## What Is Hailuo AI ? If you’ve been following AI trends, you’ve likely heard about Hailuo AI . Developed by Minimax , Hailuo represents a new generation of video AI models designed not just for still-image creation, but for fluid, dynamic storytelling. While models like Midjourney and Flux have dominated the image space, Hailuo’s focus on motion sets it apart. Think of it as the shift from photography to cinema. It doesn’t just capture a frame but brings an idea to life in motion. Although, this is your reminder that with PiAPI , you don't have to choose! Enjoy access to over 20 AI models in one place. ## Why Hailuo AI Video Is the Next Big Shift Video has always been one of the most powerful mediums for communication. But until now, high-quality production meant expensive cameras, skilled crews, and days of editing. With video AI models, that bottleneck is disappearing.Here’s why models like Hailuo are leading the way: - 1. Scalability for creators: Marketers, YouTubers, and designers can generate professional-looking video without Hollywood budgets. - 2. Flexibility for developers: Through the Hailuo API , apps and platforms can integrate advanced video generation directly into their workflows. - 3. Accessibility: Many users start by experimenting with PiAPI's free or pay-as-you-go access , lowering the barrier to entry for anyone curious about Minimax’s video AI. Don't believe us? Try generating some videos through our Hailuo Playground ! ## Inside the Hailuo Ecosystem: Features That Matter What makes Hailuo more than just another “video generator” are its developer- and creator-friendly features : - 1. Text-to-Video: Turn prompts into dynamic, cinematic clips. - 2. Image-to-Video: Bring still photos to life with motion. - 3. Subject Reference: Keep characters or products consistent across scenes - 4. Recreate Mode: Easily spin variations of a generated clip until you find the perfect one. - 5. Bulk Generation: Scale campaigns or pipelines with high-volume video creation. - 6. Asynchronous Calls: Developers can integrate video generation without slowing down workflows. - 7. Commercial Use & Watermark Removal (via premium plans): Production-ready outputs for ads, campaigns, and branded content. Read more about it and see some prompts and examples here . ## Enter Hailuo 2: Raising the Bar Minimax recently introduced Hailuo 2 , a next-gen model that takes things further: - 1. Native 1080p Resolution: Crisp, professional-grade video. - 2. Extreme Physics Mastery: Realistic motion for complex dynamics (from dance to natural phenomena). - 3. Prompt Precision: Unmatched adherence to detailed, multi-layered instructions. - 4. Record-Breaking Cost Efficiency: Thanks to its Noise-aware Compute Redistribution (NCR) architecture, Hailuo 2 delivers more power without raising costs. - 5. Developer-Friendly: Accessible via the PiAPI Hailuo 2 API , making advanced video generation as simple as a few API calls. This evolution shows how quickly video AI is moving from “experimental” to production-ready creative technology . Check out some examples here . ## The Bigger Picture: Minimax and the Video AI Ecosystem Minimax is an AI lab that's growing a reputation as one of the most innovative players in multimodal AI. Hailuo AI and Hailuo 2 are proof of that vision: video-first models built for creators, businesses, and developers alike. As video AI adoption accelerates, one thing is clear: the next generation of storytelling will be AI-native. From TikTok-style short videos to cinematic campaigns, the barrier between imagination and execution is breaking down. ## What's Next? Explore Hailuo API! If you’re just discovering Hailuo AI , this is the moment to explore. Start by learning how the model works, compare it to others in the video AI space, and experiment with free or trial-based access where available. But if you’re a developer or startup builder, don’t stop there — look into the Hailuo API . Getting your API key is the first step to building apps, tools, or services that harness the full power of video AI. Sign up for PiAPI today and check out our Hailuo API to move from exploration to implementation! Explore our Hailuo API documentation, review pricing plans , and join a growing community of developers pushing the boundaries of AI-powered creativity. ## The Best Face Swap API — PiAPI’s Fast, Reliable, and Affordable Solution Discover PiAPI Faceswap API — the fast, reliable, and cost-effective AI face swap solution for images, videos, and multi-face projects. Easy integration, low latency, bulk support, and free credits to get started. If you’ve been searching for the best face swap API , you’ve likely come across PiAPI Faceswap — a developer-friendly solution designed for image, video, and multi-face swapping at scale.Unlike free face swap tools that are slow, glitchy, or limited, PiAPI delivers fast, reliable, and cost-effective face swapping through a RESTful API that’s easy to integrate into your apps, creative workflows, or large-scale projects. ## Why Choose the PiAPI Faceswap API ? PiAPI’s AI face swap API is built to handle production-grade use cases with: - 1. Low latency - Lightning-fast processing for real-time apps. - 2. Asynchronous tasks - No blocking; fetch results once ready. - 3. Automatic face detection Detects and swaps faces with high accuracy. - 4. High concurrency - Stable performance even under peak loads. - 5. Bulk generation - Handle high-volume projects and batch processing. - 6. Standard RESTful design - Integrate easily across any tech stack. - 7. Webhooks - Get notified instantly when tasks are completed. ## What Can You Do with Faceswap API ? The use cases are endless for all developers, creators, or businesses: - 1.Image Face Swap - Swap faces in single or bulk image uploads. - 2.Video Faceswap API - Edit videos frame-by-frame with stable, realistic swaps. - 3.Multi-Faceswap Swap multiple faces across groups or datasets.Developers are already using PiAPI to power: - 1.Social media apps with custom face filters. - 2.E-commerce tools for personalized product previews. - 3. Creative agencies running ad localization at scale . - 4. Entertainment platforms for meme and content generation. ## Pricing: Simple & Straightforward PiAPI makes scaling affordable with pay-as-you-use pricing : - 1. Standard Plan: 0.02 /call (includes access to state-of-the-art models). - 2. Custom Dedicated Deployment: Contact us for high-volume, low-latency needs with priority access. Enjoy FREE credits upon sign-up to test the API risk-free before scaling! Check detailed pricing here . ## How to Get Started with PiAPI Faceswap 1. Sign up on PiAPI and get your API key. 2. Send a POST request with the source and target images. 3. Receive a swapped image or video URL in the response. For a step-by-step walkthrough, check out our guide on how to use Faceswap API from PiAPI with Postman . You can also explore the PiAPI Faceswap Playground for quick experimentation before running the API. ## Testimonials From Developers "The face detection feature in the API is pretty spot-on, it is very accurate! This helped us build the face swap feature in our app!” Sanjay “ Handling large-scale projects was quite easy with Faceswap API. The bulk generation feature was also nice for our one-off, high-volume project.” DreamEngine “ Thanks for helping us with our peak load situation! We appreciate the extra-support for the high concurrency!” BlueSyntax “With Faceswap API, we delivered an image altering feature for this social media app that we are working on. We like the low latency feature!” Farid A. “Good custom server solution for the Faceswap API, we needed something that offers higher Concurrency and minimum latency, this worked great!” Brevo Labs ## Ready to Build with PiAPI Faceswap? Want to start working on apps, creative tools, or enterprise-scale personalization? The PiAPI Faceswap API gives you the flexibility, speed, and scalability you need. Get started today with free testing credits and explore PiAPI Faceswap API Docs. ## Wanx 2.1 vs Wan 2.2: A Logo Transformation Ad Comparison Compare Wan 2.1 API vs Wan 2.2 API with a cinematic McDonald’s logo transformation. See how each model handles structured logo animations, fries, and Big Mac hero shots. Explore PiAPI’s Playground and try it yourself! ## Wan 2.2 API Viral Presets The X and Reddit communities have been buzzing about the release of over 30 Wan 2.2 presets, and one in particular caught our attention: the “Logo Transformation” preset. It's groundbreaking to see a single image and just one click can generate an ad that would've cost thousands of dollars to produce. Sure, it’s easy to upload an image and let AI do all the work but this blog is for the creators who want to see every frame of their vision come alive. With the recent model update from Wan 2.1 to Wan 2.2 , the big question on everyone’s mind is: Is the upgrade really worth it? At PiAPI , we decided to put it to the ultimate test. PiAPI offers both Wan 2.1 API and Wan 2.2 API for your Wan API integration needs. We ran a side-by-side comparison through PiAPI's Playground , using a personalised prompt to craft a McDonald’s logo transformation ad. The goal? Watch how each version interprets a structured logo animation into fries and a mouthwatering Big Mac hero shot. ## The Prompt A bold cinematic logo transformation ad. The golden McDonald’s “M” arches appear in the center against a vivid McDonald’s brand red background. Gentle wisps of steam rise upward, like the heat from freshly cooked fries, swirling dramatically around the glowing arches. Beams of warm light cut through the steam, creating a cinematic glow. The arches smoothly transform into a McDonald’s fries box, golden fries bursting upward in slow motion with steam drifting naturally from them. The fries shimmer and morph seamlessly into a rotating Big Mac hero shot, with realistic juicy textures, sesame seeds glistening under cinematic lighting, and steam rising from the burger. A subtle camera push-in adds depth and motion stability. Red and yellow glowing particle effects fill the scene, perfectly matched to McDonald’s branding. The background remains bold red throughout, with smooth transitions, lens flares, and high-fidelity textures. Dynamic, high-energy, eye-catching, cinematic commercial style. ## Results ## Wan 2.1 ## Wan 2.2 ## Key Observations ## Consistency Across Frames Wan 2.2: The transformations stayed coherent, with the logo and key elements remaining central and stable. Ideal for structured visual tasks like logos or icons. Wan 2.1: Frames were consistent, especially in the background, but the cinematic elements sometimes overpowered the structure, which can be visually interesting but less precise. ## Prompt Adherence Wan 2.2: Maintained the symmetry and iconic shape of the McDonald’s “M” arches. Transformations followed the prompt closely—steam,and fries, and Big Mac appeared where expected, with smooth motion transitions. Wan 2.1: Also maintained the symmetry and iconic shape of the McDonald’s “M” arches but over-interpreted some aspects. - 1. Fries were thrown into the air cinematically, but steam and some brand-specific cues were missing. - 2. The Big Mac transition was fluid but lacked the dynamic rotation described in the prompt. In short, it felt cinematic but not precise enough for an ad-ready transformation. ## Visual Quality Wan 2.2: Sharper, cleaner, and more coherent visuals. Best for centered, brand-focused content. Wan 2.1: Trades coherence for cinematic flair. Fluid and atmospheric, but less structured for tasks that demand strict prompt adherence. Overall, Wan 2.2 is the clear winner for structured, brand-focused, content with greater prompt adherence. Logos, icons, and centered visuals shine here. On the other hand, Wan 2.1 excels when you want cinematic energy and atmosphere, but it can drift away from strict prompt fidelity. Perfect for more experimental or dramatic video prompts.If you’re aiming for an eye-catching ad that sticks to brand guidelines, Wan 2.2 is your go-to. But if you want to push the boundaries of motion, cinematic flair, and unexpected creativity, Wan 2.1 still has its place. ## Wan API FAQs ## What makes Wan 2.2 stand out from previous versions like Wan 2.1 ? Compared to Wan2.1 , Wan2.2 significantly upgrades generation quality through a larger dataset and the innovative Mixture-of-Experts (MoE) architecture, which allows for advanced control over video attributes without increasing computational load. ## What is Wan API ? The Wanx API is provided by PiAPI , designed to facilitate developers' access to the Wanx 2.1 and 2.2 models . With Wan API , developers can easily integrate the Wan 2.2 model's exceptional video generation capability. ## Can I use videos generated from the Wan API for commercial purposes? PiAPI does not put any restrictions on the legal usage of the video generator and its API, and PiAPI allows video generated to be use for legal commercial purposes to the extend of the original model licensing agreement. ## Try Wan 2.2 Yourself! Curious how Wan 2.1 and Wan 2.2 handle your own prompts? Sign up for PiAPI and explore our Playground to experiment with images and cinematic transformations. See the difference for yourself! Sign up & explore PiAPI's wide range of AI Models from text, images, videos and audio! ## Luma Dream Machine API Documentation (2025): Complete Guide to Docs, Prompts & Updates Looking for the official Luma Dream Machine API documentation in 2025? Here’s your complete guide to the docs, prompt guide, free plan details, and developer resources. ## What is the Luma Dream Machine API? The Luma Dream Machine API via PiAPI lets developers generate cinematic-quality videos directly through programmatic calls. Instead of using the front-end interface, you can integrate Dream Machine into your own apps, workflows, or research projects via API endpoints. For developers searching for Luma Dream Machine API documentation, this guide pulls together all the key resources, links, and prompt usage notes into one place. See related blog: Luma Dream Machine Pricing & Features (2025): Free Plan, API & Release Updates via PiAPI ## Luma Dream Machine API Docs (2025) Here’s what’s currently available through our PiAPI Luma Dream Machine API docs 2025 : - 1. API Documentation → Covers authentication, endpoints, request formats, and response structure. - 2. Prompt Guide → Explains text prompt structuring, supported modifiers, and best practices for consistent video outputs. - 3. Developer Resources → Includes SDK references, sample code snippets, and links to changelogs. The API docs are continuously updated, so checking the official documentation page is recommended for the latest changes. ## Supported Models in Luma Dream Machine API ## Text-to-Video - Input: Descriptive language prompts. - Output: Short cinematic video clips (up to 5–10s). - Use Case: Storyboards, ad concepts, educational explainers. ## Image-to-Video - Input : Still image + optional text prompts. - Output : Animated sequence or cinematic motion effect. - Use Case: Turning product photos, logos, or sketches into dynamic clips. Try out PiAPI's Luma Dream Machine Playground before scaling in the API documentation! ## Luma Dream Machine Prompt Guide (2025) This guide explains how to: - Structure descriptive text prompts for cinematic outputs. - Use modifiers like camera angles, motion cues, and style tags. - Experiment with seed settings for reproducibility. - Combine natural language with technical keywords for precision. Follow our instructions for full details. Here’s the exact prompt we used to generate a florist campaign concept: Create a 4-frame video showing a bouquet in different advertising scenarios to test concepts quickly without extra production costs. Each frame should feature the same bouquet in a unique setting: Floating in a rectangular pool with soft ambient lighting and gentle reflections on water; resting in a sand garden under natural sunlight, serene and minimalistic aesthetic; laying on top of vintage newspapers with warm tones and nostalgic cozy mood; on a dining table with a note that says 'love you', romantic and inviting atmosphere. Focus on realistic textures, natural lighting, and appealing composition. Optional subtle animations: water ripples, shifting sand, paper fluttering, or soft light changes to make each frame dynamic. Luma AI via PiAPI is the fastest way to test creative campaigns without investing in expensive shoots, and you can explore our Dream Machine Playground as well. ## Luma Dream Machine API Pricing Overview (2025) PiAPI offers flexible pricing for creators, developers and businesses through subscription plans (including a FREE plan) alongide a Pay-As-You-Go Plan. Table comparison of Subscription Plan vs Pay-As-You-Go Plan This allows both indie creators and developers to scale campaigns affordably. See more details and the wide array of models you can utilise with our plan here ! ## FAQ: ## What is Dream Machine? Dream Machine is developed by Luma Labs is an AI model that makes high quality, realistic videos fast from text and images. The generated videos physically accurate, showing consist characters, and has natural but impact shot from Luma AI. ## What is the Luma Dream Machine API Documentation 2025 ? Since Luma Lab currently does not offer Dream Machine API as part of their Luma API , therefore PiAPI took on initiative and created the unofficial Dream Machine API for developers worldwide, allowing you to embed the state-of-the-art text-to-video / image-to-video capabilities into your application or platform! ## What type of videos can I make with the Dream Machine API? Dream Machine can help your users create all types of high-quality videos, whether the starting point is a simple prompt or an image. Dream Machine can iterate incredibly fast (generating 120 frames in about 120 seconds), and output videos would have accurate real-world physics, consistent character between frames, and optimized camera trajectory matching the intent of the scene! ## Where can I find Luma AI API documentation (2025)? Documentation and quickstart examples are available here and in the PiAPI Playground. ## How much does the Luma Dream Machine API cost in 2025? Plans range from free (limited credits) to enterprise solutions with full customization. ## Explore Luma AI With PiAPI Key features of Luma AI with PiAPI Whether you’re exploring AI video creation or building professional apps, PiAPI’s Luma API offers flexible options for everyone. PiAPI offers a range of subscriptions, from Free plans to Enterprise plans. If you’re curious about where AI video could fit into your projects, PiApi's Luma Dream Machine Playground is a great place to start experimenting. Ready to explore what AI video can do? Try Dream Machine with PiAPI and see how easy it is to turn ideas into videos. Get Started with Luma API via PiAP I ! ## Luma Dream Machine API Pricing (2025) — Free Plan & Features via PiAPI Compare Luma Dream Machine’s Free, Pro, and Enterprise plans for 2025. Explore API pricing, features, and release updates — plus how to get started instantly via PiAPI. PiAPI lets developers and creators turn real-world objects with into photorealistic 3D assets Luma AI — fast, accessible, and ready for any pipeline. ## Luma Dream Machine API Pricing Plans (2025) Sign up for our Pay-As-You-Go-Plan at $0.20/call, or explore our subscription plans! Enjoy instant API access, scale automatically. Try PiAPI's FREE plan here! ## Luma Dream Machine API Features Explained ## Key 2025 Features of Luma Dream Machine via PiAPI - 1. Text-to-Video : Describe a scene; Dream Machine builds it. - 2. Image-to-Video : Upload a still image and bring it to life. - 3. Style Control : Realistic, cinematic, animated, or artistic looks. - 4. Fast Rendering : Get results in minutes, not hours. - 5. Video Extend & Key Frames : Control pacing, clip length, and creative continuity. ## Release Highlights - 1. Sharper motion handling for fast-action sequences - 2. Expanded developer JSON parameters for customization - 3. Improved rendering stability for long clips - 4. Enhanced natural lighting & material realism For both developers and creatives , this means smoother workflows and more control without increasing production costs. ## How Luma Dream Machine Video Generation Works (2025 Update) With the Luma API Docs , using Dream Machine is as simple as: - 1. Text-to-Video: Describe your scene, and Dream Machine generates it automatically. Note: To use text-to-video, set "key_frames": null in your JSON body parameters. - 2. Image-to-Video: Start with an image and bring it to life with: - i. Apply style controls (cinematic, animated, artistic). - ii. Add start/end frames to control scene flow. - iii. Use video extend to expand clips seamlessly. ## Top Use Cases 2025 - 1. Social Media Content: Quick, engaging videos to boost interaction. - 2. Advertising & Marketing: Test multiple ad concepts rapidly. - 3. Creative Brainstorming: Generate inspirations when ideas run dry. - 4. Game & Film Prototyping: Transform storyboards into animated or live-action clips. Case Study: Indie studio prototypes immersive gameplay in minutes using Luma Playground: real-world objects animated, cinematic energy effects applied, and prototype video shared instantly. ## FAQ: Luma Dream Machine Pricing, Free Plan & API (2025) ## What are Luma Dream Machine pricing plans in 2025? PiAPI's Free, Pro, and Enterprise subcscription plans, as well as a Pay-As-You-Go-Plan at $0.20/call. ## What’s new in Luma Dream Machine 2025 release? Better motion realism, longer clip stability, new developer parameters. ## How do I use the Luma Dream Machine API via PiAPI? Sign up with PiAPI, get your API key and integrate Dream Machine into your apps or workflows. ## Explore Luma AI With PiAPI Key features of Luma AI with PiAPI PiAPI’s Luma API gives developers and creators flexible options for AI video generation — from Free plans to Enterprise subscriptions. Experiment in the Luma Dream Machine Playground or integrate the API into your professional projects. Get Started with PiAPI ! ## Flux Kontext API (2025): Features, LoRA Training & Pricing via PiAPI Discover Flux AI Pro and Kontext API via PiAPI — fast, prompt-accurate image generation with LoRA training, Max presets, and enterprise-ready automation. If you need more than just an AI image generator, Flux Kontext Pro and Flux Kontext API via PiAPI deliver premium speed, resolution, and precision, alongside Flux LoRA for custom styles and Flux Kontext Max for maximum control. Built by Black Forest Labs and integrated through PiAPI , this setup gives developers and creative teams the tools to generate, edit, and scale Flux AI images in minutes. In this blog, we’ll break down Flux Kontext API and why PiAPI is the best way to start experimenting. ## About Flux Kontext API? Flux Kontext API offers: - 1. High-resolution output . - 2. Commercial licensing . - 3. ControlNet compatibility for guided generation. - 4. LoRA training support for brand-specific visuals. Through PiAPI’s Flux API , expect: - 1. Text-to-image and image-to-image generation in seconds. - 2. Advanced editing models . - 3. Enterprise scaling to generate thousands of assets programmatically. - 4. Unified API access to Flux as well as over 20 other AI models. ## Flux Kontext API — Pro, Max, LoRA and ControlNet - 1. Flux Kontext Pro: ensures precise prompt adherence and editing accuracy. - 2. Flux Kontext Max : provides maximum precision, control, and speed. Enhanced typography, prompt fidelity, and consistency — surpasses existing models across all quality benchmarks. - 3. Flux With LoRA and ControlNet : steers generation with accurate creative structure and details. ## Flux API Pricing & Commercial Use (2025) No downloads, no deployment, no licensing hassles. Ready to Use at only $0.02 per image and pay only for successful generations. Explore full PiAPI Flux API pricing . Start with FREE credits, no credit card required. ## Get started with Flux Kontext - 1. Access PiAPI Flux API — Playground or programmatic API. - 2. Generate AI images — text-to-image, image-to-image. - 3. Edit & refine — inpainting, outpainting, redux variation. - 3. Integrate LoRA — integrate brand or campaign style. - 4. Scale at enterprise level — thousands of assets programmatically. Try Flux Playground and see the magic for yourself! ## FAQ ## What image formats are supported? Kontext supports common image formats including JPEG, PNG, and WebP. Input images should be under 10MB for optimal performance. Output format matches your input format by default, but can be specified in your API request. ## How do I train Flux LoRA models? We are constantly expanding our available LoRA and ControlNet models. If you have any suggestions or requests for specific models, feel free to join our Discord community and submit your feature requests. Help us upload more LoRAs and ControlNets to enhance the capabilities of our API! ## What’s new in Flux API(2025)? LoRA workflows, improved ControlNet, Kontext tutorial mode, and updated pricing. ## Final Thoughts: Unlock the Full Potential of Flux AI For access to Flux AI’s powerful models — from Flux Kontext API and Flux Dev to Flux LoRA and beyond — PiAPI’s Flux API is your go-to solution. Ready to unlock Flux AI’s full potential? Sign up for PiAPI now and explore AI image generation projects! ## Flux AI Generator Tutorial (2025) — Learn LoRA, Kontext & Step-by-Step Image Editing Master Flux AI Generator in 2025: step-by-step guide to LoRA training, Kontext editing, and API workflows. See examples, prompts, and use cases for marketers & developers. ## Quick Start: Flux AI in 5 Steps Flux AI Generator combines text-to-image , image-to-image , inpainting, outpainting , Flux LoRA , Flux Kontext and ControlNet into one powerful suite. Whether you’re a developer, designer, or marketer , this 2025 update will show you exactly how to: - Generate your first image in the Flux Playground . - Write effective prompts that balance creativity and precision. - Train custom styles with Flux LoRA . - Refine images using Flux Kontext editing . - Scale projects through PiAPI's Flux API integrations. Sign up for PiAPI today to start experimenting instantly. ## Flux AI Step-by-Step Tutorial (2025 Update) ## 1. Visit PiAPI's Flux Playground Head to the Flux Playground to test prompts and experiment interactively. Open Flux Playground ## 2. Write a Simple Text Prompt Example: "A classic glass ketchup bottle with rich, glossy red ketchup inside, featuring a bold, vintage-inspired label with bright red and white colors, set against a rustic wooden table background. The bottle is slightly tilted with ketchup dripping artistically down the side, bathed in warm natural light. The scene feels nostalgic yet fresh." Try this prompt now → Open in Playground Sign up for PiAPI to unlock API-level workflows. ## 3. Explore the API Documentation Move beyond the Playground by using the Flux API : - 1. Programmatically generate text-to-image and image-to-image outputs. - 2. Attach Flux LoRA or ControlNet models. - 3. Customize parameters for higher fidelity. ## 4. Send the API Request Sample request with LoRA settings: { "model": "Qubico/flux1-schnell", "task_type": "txt2img", "input": { "prompt": "A ketchup bottle made entirely out of sliced ripe tomatoes stacked vertically, stems as the cap, photorealistic lighting", "width": 1024, "height": 1024, "lora_settings": [{ "lora_type": "mjv6" }] } } ## 5. Retrieve the Response & Edit with Flux Kontext Generated image result of the ketchup brand's marketing campaign. Try editing this example → Experiment in Kontext . Flux Kontext allows precise edits like: "Change the red background to mustard yellow color." Flux Kontext-edited image, with the background changed. Kontext modifies only what you specify, keeping everything else consistent. ## How to Use Flux LoRA and ControlNet Flux LoRA and ControlNet , giving you two powerful ways to customize generation. 1. Use LoRA when you want to apply specific styles or aesthetics (e.g., anime, Disney, collage art). LoRAs let you swap styles quickly without retraining entire models. 2. Use ControlNet when you want to guide structure (e.g., depth maps, edges, poses) while still applying a creative style. ControlNets help you keep the composition consistent while Flux handles the artistry. See the extensive list of available LoRA and ControlNet for our Flux API. ## Best Practices for Crafting Your Prompt - 1. Use clear, descriptive language . - 2. Add style/mood keywords when Flux LoRA . - 3. Prototype in Flux Playground then scale via API. - 4. Leverage ControlNet for maximum detail. ## Top Use Cases of Flux AI - 1. Background replacement for product shots. - 2. Branding updates with perfect typography preservation. - 3. Hairstyle changes in portraits. - 4. Transforming photos into 90s cartoons. - 5. Prototyping product designs. - 6. Personalized campaign assets with Flux LoRA . - 7. Fast creative generation for marketers & content creators. See examples here for Flux AI use case gallery. ## FAQ ## What is Flux Kontext API? Flux Kontext API is a powerful image-to-image editing service that allows you to transform images using natural language prompts. It offers multiple model variants (Pro, Max, and Dev) with different speed and quality trade-offs. The API supports various editing tasks including style transfer, object modification, background replacement, text editing, and more. Built by Black Forest Labs, it provides production-ready AI image editing capabilities through simple REST API calls. ## What image formats are supported? Kontext supports common image formats including JPEG, PNG, and WebP. Input images should be under 10MB for optimal performance. Output format matches your input format by default, but can be specified in your API request. ## How do I train Flux LoRA models? We are constantly expanding our available LoRA and ControlNet models. If you have any suggestions or requests for specific models, feel free to join our Discord community and submit your feature requests. Help us upload more LoRAs and ControlNets to enhance the capabilities of our API! ## What’s new in Flux API(2025)? LoRA workflows, improved ControlNet, Kontext tutorial mode, and updated pricing. ## Conclusion Flux AI Generator makes image generation and editing faster, more precise, and more customizable than ever. Sign up for PiAPI today and get full access to Flux API’s powerful capabilities! Explore our Flux API documentation , review pricing plans , and join a growing community of developers pushing the boundaries of AI-powered creativity. ## Cinematic AI at Your Fingertips — Wan 2.2 via PiAPI Create stunning cinematic AI videos using Wan 2.2 — the open-source video generator from Alibaba. Learn how to use the Wan API via PiAPI. From smooth camera panning to highly detailed character close-ups, cinematic AI is no longer just a trend—it’s a technical reality. Wan 2.2 , the latest release in Alibaba's powerful video generation model line , has offically been released. With Wan API access now available via PiAPI , developers and creators can finally bring their own visual storytelling dreams to life using Wan's signature photorealistic and emotionally rich video generation.In this blog, we’ll break down exactly what makes Wan 2.2 a leap forward from Wan 2.1 , how to access it, and why it’s becoming the go-to cinematic AI for both experimental creators and production-ready developers. ## Wan AI Popular Use Cases If you haven't seen it already, you might want to check out Alibaba Cloud's official video trailer on the model showcasing the incredible .Essentially, the new update is great for: - Animated shorts and indie film pre-visualization - Dynamic branded video ads - Interactive storytelling in games, AR, and VR - Video-to-video AI pipelines chained with other tools Best of all: Wan 2.2 is openly accessible through PiAPI ! ## How to Generate Your Video With WanAPI: A Step-by-Step Tutorial Getting started is fast and easy. Check out the PiAPI doc for examples and base API calls here . Whether you’re a filmmaker, animator, or social creator, Wan 2.2 empowers you to generate Hollywood-style motion from a single prompt — no storyboarding or 3D skills required.Run the JSON payload via our workspace or through the doc — and within minutes, you’ll have a high-quality cinematic video clip showcasing fluid motion and lighting effects. ## Step 1 — Create a New Task PiAPI docs, create WanX task 1. In the sidebar (left side of the PiAPI interface), click "Post" under Create Task 2. From the Task Type dropdown, select either: - Text-to-Video (for generating videos from written prompts), or - Image-to-Video (for animating an existing image). 3. You can choose between Wan 2.1 and Wan 2.2 models depending on your needs. ## Step 2 — Find a BaseJSON Example Select task type + model on PiAPI docs, and copy JSON 1. Browse the provided example JSON payloads in the PiAPI docs. 2. On the right-hand side of the screen, click the Play icon to test the example. 3. Hover over the code block until you see the Copy icon — click it. ## Step 3 — Send Your API Request 1. Paste the copied JSON into the "Body" section of the request panel. 2. Edit "prompt" as you'd like Tips for Video Generation i. Use film-like descriptive prompts: “wide shot,” “golden hour lighting,” “slow zoom in” ii. Experiment with LoRA styles to apply aesthetic filters like cyberpunk or fantasy iii. Avoid vague prompts — the more detailed, the better the output iv. Combine with image and audio AI for full cinematic pipelines PiAPI's wide array of image and audio models like Midjourney , Kling , MMAudio , GPT-Image . 3. Click Send . ## Step 4 — Retrieve the Task ID Copy your Task ID 1. If the request is successful, the Response Status will show "200". 2. In the JSON response, find the field labeled task_id . 3. Copy this Task ID for the next step. ## Step 5 — Get Your Video Find "Get Task" in the Sidebar 1. In the response, scroll down to the output section.Locate the video URL . 2 . Ctrl + Click (Windows) or Cmd + Click (Mac) to open it in a new tab. 3. Right-click the video and select Save Video As… to download it. ## Wan 2.1 vs Wan 2.2: What’s New? Table comparison of Wan 2.1 vs Wan 2.2 Wan 2.2 is a major upgrade over Wan 2.1, improving both quality and control for cinematic video creators. The upgrade means less flicker, better scene continuity, and richer emotional expression in generated clips — ideal for filmmakers and storytellers aiming for true cinematic impact. ## Examples of Wan Ai Videos via PiAPI ## Cinematic Ad or Marketing Videos Prompt: "A vibrant group of young women who are friends, run energetically along a sun-drenched golden beach at sunset, hair flowing wildly in the wind. The sky bursts with vivid purple, pink, and orange streaks, illuminating the shoreline with a bold, cinematic glow. The camera tracks them in dynamic slow motion, capturing their infectious laughter, radiant energy, and the thrilling freedom of the moment—perfect for a high-impact commercial." Prompt: "A dark, foggy forest at midnight with twisted trees and eerie glowing eyes watching from the shadows. A lone figure slowly walks through the mist, flashlight flickering. The atmosphere is tense and unsettling, with cold blue lighting and sudden flashes of movement. Cinematic horror film style, with slow zooms and ominous camera pans." ## Conclusion: Wan 2.2 via PiAPI — Open Cinematic AI at Your Fingertips Wan 2.2 generates seamless video loops with cosmic particle motion and hyper-stylized enhancements. Motion remains silky smooth, lighting is finely balanced, and every detail of the prompt is faithfully captured. Cinematic AI video generation is HERE. With Wan accessible through PiAPI’s easy API and no-code playground, everyone can unleash new storytelling possibilities without setup headaches. Ready to get started? Thanks to PiAPI, developers get access with: - Batch processing and concurrent generation - Seamless model chaining with over 28 AI models . - Usage monitoring, logging, and analytics PiAPI offers a FREE subscription plan and paid plans. Find out more at our pricing page .Try Wa n AI today on PiAPI ! ## The Hype Around ChatGPT Agent Mode And What Developers Need to Know ChatGPT Agent Mode is trending—but what does it mean for devs? Here's a breakdown and how PiAPI users can still build custom AI workflows without it. ## ChatGPT Agent Mode Is All Over Developer Threads If you’ve checked X or Reddit this week, you’ve probably seen it: Agent Mode. The breakout feature from OpenAI’s July rollout has developers buzzing with excitement—and confusion.But here’s the catch: Agent Mode is only available for ChatGPT Plus users on the official OpenAI platform. There’s no direct API access, no docs, and no agent SDK. That means if you’re a developer looking to integrate this functionality outside the ChatGPT web app—you’re out of luck (for now). ## What Is ChatGPT Agent Mode ChatGPT Agent Mode is designed to help users delegate complex tasks. Some key features include: - Access to secure web browsing - Integration with connectors like email, calendar, and cloud drives - Ability to schedule recurring tasks (e.g., weekly reports) - Real-time progress narration allowing users to monitor and pause tasks - Explicit user confirmation before sensitive actions, such as sending emails or purchases These capabilities mean ChatGPT can effectively function as an autonomous AI assistant, handling workflows with minimal input. ## How To Use Agent Mode ChatGPT Open ChatGPT (Plus or Pro subscription needed). - In the composer, click the Tools dropdown and select Agent Mode . - Describe the task you want done, for example, “Prepare a slide deck on quarterly sales” or “Plan a dinner party.” - Watch as ChatGPT narrates what it’s doing, letting you step in or pause if necessary. Keep in mind, for privacy and security, Agent Mode requires explicit permission before taking critical actions and offers “Watch Mode” for active supervision. Learn more on OpenAI’s official page . For visual instructions, watch Open AI has uploaded a video guide. ## Real-World Use Cases - Client Meeting Prep – Summarizes your calendar, drafts a brief, and creates a slide deck. - Public Transit Benchmarking – Researches multiple global transit systems and compares them. - Expense Reporting – Submits expenses with justification summaries and calculations. - Novel Planning – Designs a six-course dinner inspired by historical literature. ## PiAPI's ChatGPTAPI Powerful AI Access: Your All-in-One Platform While PiAPI doesn’t offer the flashy autonomous Agent Mode that’s making waves right now, what we do provide is reliable, powerful access to the core ChatGPT API and other top-tier AI models like GPT-4, GPT-4o, Claude, and DeepSeek. The API cannot autonomously browse the web or schedule meetings, but it does offer dependable, scalable access to advanced language models that power everything from chatbots to content generators.Read more about our ChatGPT models here . ## Why Developers Are Choosing PiAPI PiAPI is more than just API access to ChatGPT—it’s a full-stack AI platform built for developers, creators, and businesses who want to move fast and build smarter. With one subscription, you get unified access to top-tier language models like GPT-4.1, GPT-4o-mini, and Claude , plus powerful multimodal APIs for image, video, and audio generation. PiAPI supports it all with a robust infrastructure, fallback routing, global availability, and affordable pricing that scales with your needs.While features like ChatGPT’s Agent Mode hint at the future of AI autonomy, PiAPI lets you build that future now. No API gatekeeping. No Plus-only exclusivity. Just powerful tools you can integrate today.Ready to build smarter? Get started with PiAPI . ## Midjourney Loop Video Update (Aug 2025) Discover Midjourney’s August 2025 update: new video loop feature, start-end frame control, API access via PiAPI, and pricing updates. Full tutorial with examples. Midjourney has just rolled out one of its biggest creative updates of 2025 — looping video generation with start and end frame control. This July feature release makes it possible to create smooth, seamless video loops without visual glitches, opening up fresh use cases for design, storytelling, and motion graphics. At the same time, developers are asking: what about the Midjourney API? While an official video API is still in progress, PiAPI already gives you full access to Midjourney image generation — plus video generation via other leading models like Luma, Wan, and Kling. In this post, we’ll break down: - 1. What’s new in the August 2025 Midjourney update - 2. How the loop & frame control feature works - 3. Midjourney API access via PiAPI - 4. Midjourney pricing updates - 5. Example generations you can try ## What the Loop Feature Enables - Seamless video loops - Start/end frame control - Smoother visual storytelling - Expanded use cases for motion design and animation ## Midjourney API (August 2025): What Developers Need to Know While the Midjounrey video API isn’t available yet (stay tuned), PiAPI offers full-featured access to Midjourney image generation . That’s where PiAPI steps in. With Midjourney API key, you can: - 1. Generate images programmatically (no Discord logins required) - 2. Chain tasks like /imagine , /upscale , /variation , /inpaint , /outpaint or /zoomout , /pan , /reroll or /rerun , /describe , /seed and /blend . - 3. Automate workflows for creative teams and platforms - 4. Access AI generation through Midjourney as well as other models like Luma , Kling and Wan (which also offers looping video generation here ) ## Midjourney Pricing Updates (August 2025) Overview of PiAPI’s Midjourney Price Plans, including Pay-as-you-go and Host-your-account options. Wondering how much it costs to scale Midjourney content generation? Visit PiAPI’s Midjourney pricing page to see updated 2025 pricing plans, including options for startups, agencies, and enterprise teams. ## Midjourney Image Examples Here are some outputs created via PiAPI’s Midjourney API access: 4mm fisheye distortion warps a gleaming white salon where a barber in streetwear holds trimmers above a client buried under 15 comically exaggerated layers of shaving foam. Only the subject's laughing eyes and stretched fabric of their matching hats remain recognizable in this raw, chaotic composition. Luxury product shot of a smooth aerodynamic helmet featuring quilted leather panels in blush pink and powder blue. Soft studio lighting highlights brushed gold strap hardware and mother-of-pearl finishes, evoking Chanel meets modern urban cycling See more examples here . ## Bring Your Vision To Life via Midjourney API Whether you’re building AI-powered creative tools, automating workflows, or experimenting with new design techniques , PiAPI gives you programmatic access to Midjourney image generation — and soon, video generation. Get started with PiAPI's Midjourney API and explore our ecosystem of text, image, video, and audio AI tools. Explore PiAPI’s wide ecosystem of AI tools across text, image, video, and audio! Check out how we created a glass fruit-cutting ASMR video without Google Veo 3 or dive into the Midjourney + Luma Marvel multiverse experiment in our blog posts. ## Kling AI Censorship Explained (2025): Why Developers Switch to PiAPI for Creative Freedom Frustrated by Kling’s restrictions? Discover why developers are switching to PiAPI for unrestricted AI video generation—with access to models like GPT-4o and Luma. AI video generation is evolving fast — but not every tool lets developers fully explore their ideas. If you’ve tried Kling AI, you’ve probably seen this frustrating message: "Prompt contains sensitive words." Whether you’re experimenting with satire, storytelling, or even harmless parody, Kling’s strict censorship can bring iteration to a halt. In this blog, we’ll break down exactly what's censored in 2025, why it matters for developers, and why PiAPI’s unified access is becoming the go-to alternative. ## What Does Kling AI Censor in 2025? Kling’s content filters are among the strictest in AI video generation. Here’s what typically gets blocked: - Political figures & satire (e.g., Trump parodies, world leaders, controversial topics). - Romantic & NSFW content (even stylized or cartoonish affection). - Violence or edgy humor (even slapstick, parody, or implied scenarios). The problem isn’t just that some content is off-limits — it’s that the filters often overreach , blocking prompts with valid creative intent. Developers end up with no output at all, stalling prototypes and wasting iteration cycles. ## The South Park Trump Episode Test Case: Kling AI vs Luma Dream Machine vs Wan When the South Park Season 27 premiere aired, parodying Trump, Satan, and network politics, fans rushed to recreate the scene with AI. Most hit the same wall. ## Kling When it comes to Kling , the harsh reality is: you often get no output at all. Its ultra-restrictive content filters frequently block valid prompts, leaving users stuck without any generated video or meaningful feedback. ## Luma Dream Machine Prompt: "A muscular red devil with horns sits on a bed in a warmly lit bedroom. A small cartoon man with blonde hair joyfully hugs the devil. The devil smiles and catches him, wrapping him in a tight, affectionate hug. They passionately kiss. The animation stays cartoonish for the characters with emotional, romantic, playful vibe." Luma Dream Machine, accessible via our Luma API , delivers surprisingly clear and humorous results. It delivered surprisingly clear and humorous results, capturing a South Park-like aesthetic with minimal distortion and excellent preservation of cartoon style. It’s a favorite for those who need clean animation with emotional storytelling. ## Wan Prompt: "A muscular red devil with horns sits on a bed in a warmly lit bedroom. A small cartoon man with blonde hair joyfully jumps from under the bedsheet into the devil’s arms. The devil smiles and catches him, wrapping him in a tight, affectionate hug. The animation stays cartoonish for the characters, but the room and bed are realistic, with red curtains and soft ambient lighting. Emotional, romantic, playful vibe. Known for realistic motion handling, WanX was fun to experiment with. Using the Wan API , Wan excelled at motion, lighting, and character emotion, striking a balance between realism and cartoon charm. It also preserved prompt intent—without censorship. Prompt: Create an animated video featuring a muscular red devil with horns sitting on a bed, while a small cartoon man with blonde hair joyfully jumps from under the bedsheet into the devil's arms. The scene should convey an emotional, romantic, and playful vibe, with the devil smiling warmly as he catches the small man and wraps him in a tight, affectionate hug before they share a passionate kiss. Ensure that both characters have distinct features—the devil should be muscular with prominent horns, while the small man should have exaggerated cartoonish traits—and use vibrant colors to enhance the playful atmosphere. As they kiss, transition to a humorous moment where Trump playfully goes under the sheets, hinting at an intimate act without being explicit, while maintaining a lighthearted and fun tone throughout the animation. Impressed by the first video, we ran a second, more detailed variation of the prompt including a Trump cameo under the bedsheets. While the model slightly distorted his face (a common issue with real people), the video retained humor, pacing, and strong visual storytelling. ## Overall Winner: Wan AI Kling refused to generate results. By contrast, Luma Dream Machine delivered a clean, satirical animation in a South Park-like style, whereas Wan balanced realism and cartoon charm, capturing motion and lighting while staying true to the prompt intent. The takeaway? Kling shut the door, while other models opened creative possibilities. ## Final Thoughts: Unlock Creative Freedom with PiAPI AI video tools are only as useful as the freedom they allow. Kling’s censorship may protect the platform, but for developers, it limits creativity. PiAPI gives you flexibility, consistency, and access to multiple models in one place. If you’re building storytelling apps, UGC platforms, or short-form content workflows , PiAPI is the developer-first choice. Start generating with PiAPI today! ## Build Viral AI Videos with Kling + GPT-4o via PiAPI Access Kling, GPT-4o, and more via one API. Learn how developers use PiAPI to build custom AI video workflows—no platform limits, just full model control. ## Recreate the Coldplay Kiss Cam Fail with AI: A Step-by-Step Tutorial Using PiAPI’s Kling API What happens when your not-so-secret date makes it onto the Kiss Cam... and you’re not supposed to be there? Let’s just say: liftoff. Using PiAPI’s unified AI API, you can recreate viral moments like this Coldplay concert fail—complete with rocket launches, dramatic lighting, and AI-enhanced chaos. Here’s how: - Upload the image into PiAPI's Kling AI . - Explore the various Kling Effects here to find out how you generate the video. - We used this animation prompt: At a Coldplay concert, the cam focuses on a young couple sitting in the crowd. Suddenly, his face turns into utter shock and a rocket launches, emitting bright orange Mach rings and thick smoke from under his seat. The crowd behind them reacts in disbelief. The scene remains centered on the launch, with cinematic lighting, detailed crowd movement, and saturated colors. Visual style is dramatic and humorous, with realistic smoke and a high-res concert stadium backdrop. - Click “Run” and retrieve your animated masterpiece. Here's our results: A rocket launches with Byron and Kristin disappearing in the fog. ## Join the Ghibli Trend with GPT-Image 1.5 - Upload the image into GPT-1.5 Image with PiAPI's GPT-Image 1.5 API. - Insert prompt into GPT-1.5 Image: Make into studio ghibli art. - Download the output. Here's our results: Studio Ghibli generation of Coldplay Kiss Cam scandal Read more about the Studio Ghibli trend in our previous blog here ! ## Coldplaygate: Our Favorite Reactions - Instagram video : AI kiss cam re-enactment with CEO’s stunned reaction. - YouTube : Astronomer turns scandal into stunt—featuring Gwyneth Paltrow (yes, really) with Ryan Reynolds’ ad agency. ## Final Thoughts: Remix Reality with PiAPI Kling Video Generation Whether it’s launching Andy into orbit or turning Kristin into a Ghibli heroine, PiAPI gives you full developer access to over 28 AI models—including Kling API and ChatGPT . Remix real moments, prototype fast, or generate batch content with one API. PiAPI offers developers much more flexibility when working with Kling than the native Kling UI, especially for backend control and batch processing. Watch a developer tutorial here: How To Access Both Luma and Kling APIs for AI Video Generation ## Glass Fruit Cutting ASMR AI Prompt (How I Made a Video Without Google Veo 3) Create viral ASMR glass fruit cutting videos using AI — without Google Veo 3. Here's how we used PiAPI, Kling, and MMAudio to build it step-by-step. ## Try These Glass Fruit Cutting ASMR Prompts The satisfying slice. The crystal-clear crunch. The shimmering slide of a mango half in slow motion. If you’ve been down the ASMR fruit cutting rabbit hole , you’ve probably seen the viral glass fruit cutting trend. In this guide, I’ll show you the exact prompt I used to create a glass fruit cutting AI ASMR video — without Google Veo 3. We’ll walk through the prompt, step-by-step workflow, and tips so you can generate your own satisfying AI ASMR videos using Kling AI , ChatGPT image generation, and MMAudio — all accessed via PiAPI , your one-stop API for premium AI tools across video, image, audio, and more. ## The Exact Prompt for Glass Fruit Cutting ASMR (Copy & Paste) Here's the =proven prompts= I used — just copy, paste, and run it inside PiAPI’s GPT-4o image model : A close-up photo of a realistic human hand slicing through a translucent glass mango with a sharp knife. The mango is semi-transparent with a shiny, glass-like surface and a visible seed inside. The lighting is soft and cinematic, highlighting the glossy textures. The background is minimal and wooden, like a kitchen cutting board. The composition feels satisfying and ASMR-friendly, with high realism and detail. Swap “mango” with peach, watermelon, kiwi, or orange to create your own variations. ## Step-by-Step: How I Generated the Video Without Veo 3 A crystal-clear still of a glass mango just beginning to be cut — a teaser of PiAPI’s Kling AI precision in action. ## Step 1: Create the Base Image You can start by crafting a prompt through ChatGPT . - i. Open the LLM workspace on PiAPI - ii. Select the gpt-4o-image model - iii. Submit this prompt: A close-up photo of a realistic human hand slicing through a translucent glass mango with a sharp knife. The mango is semi-transparent with a shiny, glass-like surface and a visible seed inside. The lighting is soft and cinematic, highlighting the glossy textures. The background is minimal and wooden, like a kitchen cutting board. The composition feels satisfying and ASMR-friendly, with high realism and detail. - iv. Once the image is generated, download it for the next step ## Step 2: Animate with Kling AI via PiAPI Now that you have your image, it’s time to bring it to life: - i. Upload your glass fruit image into PiAPI's Kling AI - ii. Use this animation prompt: A close-up, slow-motion video of a human hand gently slicing all the way through a translucent glass mango with a shiny steel knife. The mango has a glossy, semi-transparent surface, and reveals a smooth glass-like seed inside. As the knife completes the cut, one half of the mango slowly slides and falls onto a wooden cutting board with a soft, satisfying sound. The scene is calming and tactile, with warm ambient light reflecting off the glass texture. Macro lens, high-detail realism, ultra-satisfying ASMR style, no background distractions, peaceful tone. - iii. Run and download the AI-generated clip You’ll get a clean, cinematic ASMR-ready animation that perfectly visualizes the sensation of cutting through a glass fruit. ## Step 3: Add ASMR Audio with MMAudio Next, I used our MMAudio to generate sound that matched the visuals. - i. Open the MMAudio workspace and select the " video2audio " task type - iii. Upload the video from Step 2 - iv. Enter the following prompt: Ultra-clean ASMR audio of cutting a translucent glass mango in close-up. The track begins in near-silence, capturing delicate hand movements and a soft fingertip press against the smooth glass surface. A stainless steel knife gently touches the glass fruit — a crisp, high-pitched tap echoes subtly. As the knife slices slowly through, capture soft, satisfying friction: shimmering, crystalline textures, like tempered glass parting with fine internal tension. Include delicate creaking and micro-crackling as the blade glides through the semi-hollow fruit. Reveal a realistic inner mango seed made of frosted glass with natural, grainy striations — enhance the textural audio of the seed being exposed. End with a soft clink as the sliced half lands gently on a wooden cutting board. No background noise, no music — just pure, intimate, tactile glass ASMR. The result? Pure, crisp ASMR audio that matches every movement in the cut. ## Variations of the Glass Fruit ASMR Prompt Try these variations to expand beyond mango: - Glass Peach: “A slow-motion video of a crystal-clear knife cutting a glass peach in half on a wooden board.” - Glass Watermelon: “Macro shot of a glass watermelon sliced, reflective seed patterns visible, cinematic shadows.” - Glass Kiwi: “Cinematic ASMR of slicing a glass kiwi — soft crunch, visible frosted core.” - Glass Orange: “Realistic ASMR video of slicing a glass-like orange, shimmering surface, satisfying resistance.” Pro tip: Add “macro lens” or “slow-motion” for extra realism. ## Why I Used PiAPI Instead of Google Veo 3 Google Veo 3 is impressive, but PiAPI gave me more flexibility : - i. Use Kling AI for ultra-realistic, cinematic video generation - ii. Generate crisp imagery with GPT-4o image tools tools - iii. Design satisfying ASMR audio with MMAudio - iv. One subscription without juggling multiple vendors With PiAPI, I only paid for what I needed — perfect for experimenting with ASMR projects. Find out more about Veo 3's pricing and features here , and PiAPI's here . ## FAQs: AI ASMR Video Prompts ## What is the best AI prompt for fruit cutting ASMR videos? Use detailed prompts like “A close-up video of slicing a translucent glass mango, cinematic slow motion, ASMR style.” Adjust fruit type for variations. ## Can you make glass fruit cutting ASMR without Google Veo 3? Yes. Using PiAPI, you can combine Kling AI (video), GPT-4o image (stills), and MMAudio (sound). ## How do AI ASMR prompts work? Prompts describe visual and auditory details. The more tactile, textural, and cinematic your description, the more satisfying the AI output. ## Final Thoughts Whether you’re chasing viral TikToks, YouTube Shorts, or personal ASMR experiments , the glass fruit cutting AI prompt is a must-try. Plans start as low as $15/month with usage credits. PiAPI gives you one portal to rule them all. Explore our plans and try out our free features here ! With PiAPI, you can generate images, video, and audio all in one place — no Veo 3 required. Try the prompt now in the Kling AI Playground on PiAPI. ## Ghibli Style Image Generator using GPT 4o image generation API In this blog we will be using out GPT 4o image generation API to replicate the Ghibli Style, creating a Ghibli image generator! Hey everyone! Unless you've been living under a rock for the past week, you've likely heard that OpenAI has launched their new ChatGPT image generation model. The 4o image generation model was launched on March 25th, 2025, with the announcement posted on their website. And as the leading AI API provider, we at PiAPI already have our very own GPT 4o image API . Official announcement of 4o Image Generation on OpenAI's website And we have to say, the 4o image generation by ChatGPT looks very impressive, easily overshadowing most text-to-image and image-to-image AIs in the market. In this blog, we’ll be generating both text-to-image and image-to-image examples in the popular Studio Ghibli style. Many people have been using the new ChatGPT 4o image generation model as their own personal Ghibli image generator, and it's been pretty fun to watch! So we decided to test our own GPT 4o image generation API this way and see the results. ## Examples ## Trump Tariffs For our first example, we’ll imagine what the recent Trump Tariffs announcement might look like if we were living in the fictional world of Studio Ghibli. We'll use the image below as our input, along with an appropriate prompt. Image of Donald Trump introducing the recent tariffs that will be used as input And now let's see what it looks like after we have ChatGPT 4o Ghibli -fied it! Prompt: "can you recreate this image into studio ghibli style" We must say that it definitely looks amazing. While it didn’t get all the numbers on the board correct—especially the ones at the bottom—it still shows impressive text adherence, especially compared to most other AI image generation models, which don’t even come close. ## A Minecraft Movie For our second example, we are going to use Jack Black playing Steve in the minecraft movie and put him through our chatgpt 4o image api. We're excited to see what a minecraft movie would look like if it was made by the talented people at studio ghibli! Image of jack black as minecraft steve that will be used as input And now let us look at how it turns out after putting it in our Ghibli image generator! Prompt: "can you recreate this image into studio ghibli style" This one also turned out really good! The overall image quality, combined with its strong text adherence, really makes it stand out. And it doesn't have any trouble with the fingers too, which is always nice to see. Yet another impressive result from the new ChatGPT image generation model. ## Comic Panel For our final test, we’re creating an AI-generated comic panel—something we've seen quite a few people experimenting with on Reddit. We wanted to give it a try ourselves using our GPT4o image API. In this example, we'll be using text-to-image with the following prompt: Can you create a comic panel with 4 scenes, make the entire comic in studio ghibli style: the first scene (top left) is a guy looking smug and pointing at two guys talking (the two guys talking have the openAI logo on their shirts), the smug looking guy says that "AI art is not art" the second scene (top right) is just the two guys with the openAI logo on their shirts looking back at the smug looking guy awkwardly the third scene (bottom left) is one of the two guys giving the smug looking guys a thumbs up before returning to their conversation the fourth scene (bottom right) is the smug looking guy now angry, shouting at the two guys who are now ignoring him, the smug guy is shouting "STOP HAVING FUN" And now let's look at how the comic panel turned out! The AI generated comic panel generated by 4o image generation API This one turned out really well! It nailed the human facial expressions, got the OpenAI logo right, and showed excellent text adherence. It really feels like you could create an entire manga or comic by only using our GPT 4o image generation API, the new ChatGPT image generation definitely feels like a huge leap forward. ## Conclusion I think that it can objectively be said that the new ChatGPT 4o image generation is really amazing, as it can replicate a ton of different art styles very well, including the Ghibli style which we used for the examples in this blog. We are looking forward to how else OpenAI would improve their ChatGPT image generation, and their GPT model in general! As always, if you want to test it out yourself, you can use our GPT 4o image API right now! And if you are interested, please check out our other Generative AI APIs ! ## Creating an AI Music Video Generator using Wan 2.1 In this blog we are going to create an AI Music Video Generator using Wan 2.1 Hey everyone! In this blog, we’ll be creating an AI music video generator using the Wan 2.1 model , which is readily available on PiAPI . You can start using with it right now in our workspace ! Building an AI music video generator requires a variety of AI tools, including a text-to-video generator, an image-to-video converter, LLMs, and an AI song generator. Luckily for you, all these different AI tools are all readily available on PiAPI ! ## Creating the AI Music Video Generator ## Step 0: Brainstorm ideas for the music video Before even beginning with the process, we must first brainstorm an idea and script for the video. Luckily, we have the LLM tools for that, such as GPT 4o-mini and Deepseek which you can use in our workspace . I used these tools to generate lyrics for a hip-hop song and even come up with creative concepts for the music video scenes. ## Step 1: Audio If you don't have a song ready, you can start by using Ace-step or Udio , which are also both readily available on PiAPI. For this example, I used Ace-step and input the lyrics generated by ChatGPT 4o. For the prompt, I simply specified the genre as hip-hop, without adding any negative prompts. Here are the two results I got from Ace-step We are going to use the first example for the music video creation process. ## Step 2: Image Generation (optional) The next step is image generation. While it's optional—since you can directly use text-to-video with models like Wan 2.1—it's highly recommended to use image-to-video generation instead. For current image-generation access, use Flux ; PiAPI no longer provides the Midjourney service, so visit LegNext for a similar alternative. First we will need a consistent character. The image below is a historical Midjourney example; PiAPI no longer provides that service. Prompt: "Black Hip Hop artist" Below are three images we've generated, which we’ll be using for the AI music video generator. Prompt: "A close-up shot of the artist’s feet stepping onto the stage, with the light gradually illuminating their body. The room remains shadowy, with neon colors reflecting off the floor, symbolizing the moment of decision --cref https://cdn.midjourney.com/43b577d6-09cb-447c-937d-7587b1283508/0_1.png" Prompt: "An aerial shot of the cityscape, zooming out to show the artist surrounded by glowing clouds, floating in a vast and colorful sky. The artist's body moves in slow motion, blending seamlessly with the dreamlike environment. --cref https://cdn.midjourney.com/43b577d6-09cb-447c-937d-7587b1283508/0_1.png" Prompt: "Close-up of the artist's face with determination and calmness. The wind blows through his hair as he looks toward the horizon. The background fades as the camera pulls back, showing the artist standing at the edge, the sunrise framing the shot --cref https://cdn.midjourney.com/43b577d6-09cb-447c-937d-7587b1283508/0_1.png" ## Step 3: Video Generation For the third step, we’ll use Wan 2.1 as our image-to-video generation model in the PiAPI Workspace . We'll use an appropriate prompt along with the historical image example as input. Prompt: "Animate a slow-motion zoom-in on an artist stepping confidently into a spotlight, walking through a dark room with flashing neon lights. The neon reflections move fluidly as the artist’s footsteps create soft echoes, focusing on his intense expression. The camera moves in a smooth tracking motion to capture his slow but purposeful approach." Prompt: "Animate a sweeping aerial shot zooming out from the city to reveal the artist floating in a vast sky filled with glowing clouds. The artist's movements should be slow and fluid, interacting with the environment. The surrounding colors should gradually shift as the scene feels otherworldly and surreal" Prompt: "Animate a close-up of the artist’s face as he stands on the rooftop. The wind should blow through his hair as he gazes at the horizon. The camera should pull back slowly, revealing the vast city around him, as the sun rises. The motion should be gradual, symbolizing a new beginning." Make sure to create enough videos for the video! ## Step 4: Editing The final step to using AI to create a video for your song involves bringing everything together—combining all the videos you generated with the audio. While you can use any editing software for this process, we’ve chosen to use CapCut. In this stage, you can adjust the speed of each clip, add smooth transitions, and refine the video as needed. It’s the stage to fine-tune your creation and ensure the visuals and audio sync perfectly to create a polished, professional-looking music video. Editing the music video in Capcut ## Step 5: Results And now, it's time to check out the results generated by a ton of our AIs including Wan 2.1! ## Conclusion We personally think that the results are pretty good, as Wan 2.1 generated some amazing and visually appealing videos! It is the perfect AI video generation tool to create an AI music video maker where you can add audio and video easily. Moreover, given the open-source nature of Wan 2.1 and the impressive results we've seen so far, we're excited to see how the open-source ecosystem will continue to evolve and push the boundaries of AI video creation. We hope you found this blog useful! Also, if you are interested in other AI API that PiAPI provides , please check them out! ## Using Kling API to animate toys like Popal In this blog we will be using Kling API to animate toys like Popal Hey guys! Today, we'll be using the Kling API to bring toys to life through animation. In this blog, we’ll be using Popal ’s brick figurines as the examples to demonstrate how Kling API animate toys. For those of you unfamiliar with Popal, they are a company focusing on creating brick figurines of your favorite characters. Popal website homepage In this blog, we’ll be using the Kling API to animate figures like the ones from Popal. We’ll explore three different aspects to evaluate whether Kling API is an effective tool for animating toys, such as brick figures. The three aspects we’ll be examining are: - 1. Movement Realistic movement is essential for making brick figures feel alive and interactive. When animating a toy, you'd naturally want it to perform some form of movement to catch the attention of the viewer. - 2. Physics Physics ensures that toy animations react naturally to real-world physics like gravity and momentum, creating believable and authentic actions that make the animation feel grounded. We also wanted to see how it would do when interacting with different objects. - 3. Facial Expressions Facial expressions are key to revealing the personality and emotions of the toys, deepening the emotional connection between the viewer and the animation. Now that you know what the key elements that we are focusing on, let's first start with our results from trying to animate a Popal brick figurine's movements. ## Movement For movement, we are going to use this Mai Sakurajima figurine (Rascal Does not Dream of Bunny Girl Senpai) from Popal. Below is an image of the brick figure which we will be using as input into Kling API alongside a prompt to see how well can Kling animate the toy's movements. Image of Mai Sakurajima Figure that will be used as input We will have Kling API animate the brick figurine with two types of movements: one where it runs, and the other where it jumps. ## Movement 1: Running Prompt: "The brick figurine running intensely" As you can see above, Kling API does a great job at animating the brick figure. The running motion feels quite realistic, with the figurine’s arms and legs moving naturally. Additionally, the bunny ears move in sync with the running action, which is a nice attention to detail from Kling API. ## Movement 2: Jumping Prompt: "The brick figure jumps up and down, jumping for joy" The jumping animation is done very well. As you can see, the movement appears natural, with the figurine’s body lifting off the ground in a realistic way. Just like the running animation, the bunny ears react in sync with the jump, adding an extra layer of realism. The attention to detail in how the ears move with the figure showcases Kling API’s ability to handle detailed animations. ## Physics For the physics test, we’ll use the same figurine as input, but this time place a brick building toy beside her. The goal is to have her punch the building and see if the animation realistically shows the building breaking in response. Image of the Mai Sakurajima Figure beside a toy brick building And below are the results. Prompt: "The female brick figure in one swift motion, punches the building beside her intensely, like in an action sequence. The lego building then crumbles onto the floor after the punch." As you can see, the animation still looks good, but not quite as impressive. The brick figure's actions resemble more of a strangling motion than a punch. However, the brick building does break apart in a believable enough manner, showcasing a reasonable level of destruction despite the error in the figurine’s movements. ## Facial Expressions For the facial expressions section, we’ll be using Kling API to convey three different emotions: joy, sadness, and anger. Our goal is to evaluate whether Kling API can effectively capture and express these emotions through the toy’s facial expressions.We will also be using the same image as in our movement section. ## Facial Expression 1: Joy Prompt: "Animate the brick figurine, making it smile widely and laugh with a joyful expression." As you can see, the emotion of joy and laughter is portrayed quite well in the video. The figurine’s facial expression clearly shows a smile, and its laughter is visibly conveyed, making the emotion feel genuine and lively. The animation does a great job of capturing the emotion of happiness, with the figurine's face reflecting a natural, joyful response. ## Facial Expression 2: Sadness Prompt: "The brick figurine crying hysterically as tears flow out of her eyes" The figurine expresses sadness quite well. You can see tears flowing from her eyes, while her head lowers slightly, conveying the expression of sadness quite well. The combination of movements and tears really helps convey the emotion of sadness. ## Facial Expression 3: Anger Prompt: "Animate the brick figurine with a furrowed brow, clenching its fists and showing an angry expression." In this video, you can clearly see the figurine expressing anger, with her fists tightly clenched and her eyebrows furrowed in frustration. The intensity of her facial expression, combined with these hand movements, effectively conveys the emotion of anger. Her face shows a strong sense of determination and irritation, making the expression effectively convey anger. ## Other Tests Before we get to our conclusion, let's quickly review the other attempts we made. Although we didn't include these in the main points of the blog, we hope they'd help you understand how different prompts influence the outcomes as shown by our tests below. ## Movement Other "Movement" tests we did using Kling API M1: The brick figure walking off screen to the right M2: The brick figure walks off to the right M3: The brick figure moving its legs in an exaggerated manner, walking offscreen to the right M4: The brick figure move its legs in an exaggerated manner, lifting it up one by one as it does a walking animation to the right of the screen M5: The brick figure move its legs in an exaggerated manner, lifting it up one by one as it does a walking animation and walks in place, lifting his legs up and down M6: The brick figure move its legs in an exaggerated manner, walking towards the viewer M7: The brick figure turns to the right and starts running M8: The brick figure turns 90 degrees to the right, then starts running ## Physics Other "Physics" tests we did using Kling API P1: The brick figure destroying the building beside her, punching it and breaking it into two P2: The brick figure destroying the building beside her, punching it and breaking it into two P3: The brick figure punches the building beside her in a quick intense motion as the building explodes into lego pieces P4: The brick figure punches the building beside her and it explodes ## Facial Expressions Other "Facial Expressions" tests we did using Kling API FEJ1: The brick figurine smiling FEJ2: The brick figurine laughing, as she points at the screen, she is laughing uncontrollably FES1: Transform the brick figurine, showing tears in its eyes as it sobs, its face filled with sadness FES2: Animate the brick figurine, showing tears in its eyes as it sobs, its face filled with sadness FEA1: The brick figurine has a scornful expression, with her face showing intense anger FEA2: Create a video of the brick figurine with a furrowed brow, clenching its fists and showing an angry expression. ## Conclusion Overall, we can say that Kling API does quite a good job animating toys. It is really good at conveying facial expressions, bringing out emotions with impressive details. The movement animations also stand out, with the figurines demonstrating realistic motions as they move. It's weakest point is when it comes to physics and interactions with other objects. While the building destruction test showed some promise, the overall physics interactions felt a bit off compared to the other aspects. Still, Kling API is a great AI tool for animation, especially for animating facial expressions and movement. If you are interested in the brick figures we used in this blog as examples, check out Popal ! And if you are interested in any of our other APIs, feel free to check them out at PiAPI ! ## Kling API vs Pika API (Kling Effects vs Pikaffects) In this blog we will be comparing Kling's Kling Effects with Pika Art's Pikaffects! Hi developers,On January 21st 2025, Kling released their Kling Elements (or magical elements as some call it!) feature which many of you are already familiar with, and for those who aren't we have previously written a blog explaining and testing it. While the Kling Magic Elements update was the primary focus of the update, Kling also introduced Kling Effects, which, although not the main highlight, represents a significant advancement in AI video generation. And both Kling Elements and Kling Effects are both available for our users through our Kling API . Official announcement on the Kling website about Kling Elements and Kling Effects So what is the Kling Effects feature? Basically, you insert an image into Kling API and pick an effect such as inflating it or squishing it, then Kling API will generate the output video with the chosen effect. This is very similar to the pikaffects feature from pika art which which our Crush it Melt it blog covered where we compared Kling API, Luma API, and Pika API to see which AI API performed the best. Now that Kling API has introduced its own effects feature, we decided to compare Pika API and Kling API once more to see which performs better. For this comparison, we'll revisit the examples from our Crush It Melt It blog using Pika Art API, and input the same images and effects to Kling API for a direct comparison. ## Kling's MochiMochi vs Pika Art's Squish It For our first example, we are going to use the alien plou plush like last time. Below is an image of the alien plou plush, which we’ll use alongside the MochiMochi Kling Effect or the Squish It (Or squishy maker as some call it!) Pikaffect as input for the two AI models we are going to compare. The image of the alien plou plush that will be used as input into the APIs We will see how Kling API's MochiMochi compares to Pika artificial intelligence API's Squish it. Below are the output videos of Kling Effects' MochiMochi and Pika Art AI's Squish It. Kling's MochiMochi vs Pika Art's Squish It for the squishing example Pika Pika did really good in squishing the figurine but as we said before, the fingers on the hands are quite messed up. As we said in our previous blog the issue isn’t with how the plush is being squished, but with the hands that does the squishing. When they first appear, it looks like two thumbs are pressing down, which is unnatural since a human’s fingers couldn’t start in that position. Another problem is the fingers pressing on the eyes seem to phase through the plush. Lastly, the pinky fingers look deformed with noticeable bulges. Kling Kling actually did really well, in both the figurine being squished and the finger movements as well. Kling Effects API actually animated the fingers a lot better than Pika API as you can see from the ways the hands and fingers squished the plush. Another thing it did better is the squishing effect itself, it looks like the plush is squishier than the one generated by Pika API. ## Kling's BoomBoom vs Pika Art's Inflate It For the second example, we are going to use a Squid Game plush.Below is an image of the squid game plush, which we’ll use alongside the MochiMochi Kling Effect or the Squish It Pikaffect as input for the two AI models we are going to compare. The image of the squid game plush that will be used as input into the three APIs. We will see how Kling API's BoomBoom compares to Pika API's Inflate it. Below are the output videos of Kling Effects' BoomBoom and Pika Art's Inflate It. Kling's BoomBoom vs Pika Art's Inflate It for the inflating example Pika Pika did a great job with its video output. We can see the Squid Game plush inflating like a balloon and floating away until it vanishes. Another cool detail is how the plush’s shadow follows it as it flies upwards. But one thing of note is that only the head inflates, instead of the whole body. Kling Kling also did an impressive job. We can see the entire Squid Game Plush inflating and rising like a balloon. Notably, the outer layer of the plush, once inflated, takes on a rubbery texture, resembling an actual balloon. ## Conclusion In conclusion, Kling and Pika are quite similar in terms of quality, with Kling edging out in the 'Squish It' category, particularly when it comes to animating hands. However, it's important to note that Kling Effects currently offers only two options, which we used as examples in this blog. In contrast, Pika Art provides a much broader selection, with a total of 31 different Pikaffects to choose from. But we can definitely say that Kling can be counted as one of the top tools like pika. As always, we are excited as to where Kling and Pika are headed with these fun effects and are looking forward to what new effects both Pika and Kling will choose to do. And if you are interested, check out our collection of generative AI APIs from PiAPI ! ## Bring Historical Figures to life using AI like Kling API In this blog we use Kling API to bring historical figures to life! If you've been on the internet in the past few years you’ve likely seen the viral AI historical figures videos popping up all around social media. And if you're curious on how to recreate these cool videos with Artificial intelligence historical figures, you're in the right place. With PiAPI , it's easier than ever to create your very own AI generated historical figures videos and bring history to life in a whole new way using Kling API ! In this blog, we'll explore how all this works and showcase three examples of historical figures brought to life through AI. ## How it works Here’s how it works: First, we upload an image or multiple images of historical figures into Kling API or any AI video generation model of your choice. Next, we add a prompt specifying what we want the historical figures to do in the video. And finally, we just wait for Kling API or the AI video generation model to generate the video for you! ## Example 1: Cleopatra For our first example, we’ll focus on the most recognizable pharaoh, Cleopatra. We’ll be using the famous painting "Cleopatra" by John William Waterhouse, created in 1888. This masterpiece is currently housed in the Tate Britain Collection in London. Painting of Cleopatra which will be used as input into Kling API Next, the image will be put into Kling API, along with the prompt shown below the GIF. We find it really impressive how fluid and smooth the motions are. We’re especially drawn to the fingers of the right hand, which point directly at the viewer. Unlike many AI-generated visuals, where fingers can often be a weakness, in the video of Cleopatra generated by AI we made, they are perfectly generated and animated. Prompt: "The woman looks to the camera then points at it" ## Example 2: George Washington For our second example, we’ll be using this iconic portrait of George Washington, which serves as his presidential portrait. Painted by Gilbert Stuart in 1803, this renowned portrait is titled simply "George Washington." It is now housed in the National Gallery of Art collection in Washington, D.C. Painting of George Washington which will be used as input into Kling API Next, we uploaded the image of George Washington into Kling API with the prompt shown below. We believe the animation is both smooth and realistic, particularly the way George Washington smiles and laughs. The subtle wrinkles around his eyes as he smiles add a touch of realism. Prompt: "George Washington nodding his head then laughing" ## Example 3: Alexander the Great For our final example, we are going to do something a little bit different. nstead of a painting, we’ll be using a photograph of the statue Alexander the Great, signed by Means of Pergamon, son of Aias, who is believed to be the sculptor. Created in the 3rd century BCE, this statue is now housed in the Istanbul Archaeology Museum. Statue of Alexander the Great Next, we uploaded the image into the Kling API along with the prompt, which you can see below the video. The vide looks pretty cool and aesthetically pleasing. The fingers are generated without any issues along with the shadow that the hand casts. Prompt: "The Alexander the great statue pulling out a cigarette and smoking it" ## Conclusion As you can see from the three examples above AI is quite good at bringing historical figures to life, especially the Alexander the Great and Cleopatra videos. Although this is not an AI reconstruction of the historical figure (We might do this in a later blog!), AI image to video generation has come a long way. We're excited to see how people will keep using AI in even more fun and creative ways, just like this! And if you're interested in our other APIs , feel free to check them out! ## 3D Trellis API through PiAPI PiAPI has recently launched our very own 3D Trellis API! ## Introduction TRELLIS is a large-scale 3D asset generation model open-sourced by Microsoft's Trellis Research Team that supports high-quality 3D content generation from text or images. It employs a structured 3D latent space approach to achieve scalable and versatile 3D generation.Key Features: • Supports both image-to-3D and text-to-3D generation(soon) modes • Uses structured 3D latent space approach for higher generation quality • Provides multiple 3D representation formats (Gaussian point clouds, radiance fields, meshes, etc. • Open source and easy to deploy • Supports export to standard 3D file formats like GLB/PLY The Trellis API is offered by PiAPI based on the Trellis model. Given their accelerated hardware infrastructure and custom-developed inference framework, PiAPI is able to provide APIs for these two models at very market competitive costs while maintaining performance and minimizing latency. ## So what makes Trellis a good 3D Model? To understand the practical differences and capabilities of TRELLIS, let’s compare it with another AI model , Meshy AI by exploring how each generates GIFs of knee-high Converse shoes. This example will illustrate not only the visual quality of outputs but also highlight the efficiency and versatility of TRELLIS’s structured 3D latent space compared to its counterpart. ## Knee High Converse Image of Knee High Converse that will be used as input Comparison of Meshy AI and Treliis 3d API of the Knee High Converse Example Comparing the two Models: Model Design and Realism: While Meshy AI’s model attempts to replicate the form of a sneaker, it lacks refinement, with rough edges and inconsistent proportions that detract from its overall realism. The design feels unfinished, and the exaggerated textures give it a somewhat artificial appearance. Trellis3D offers a more structured and defined design, with a consistent and cohesive shape. While it isn’t perfect, the overall form aligns more closely with the natural proportions and flow of a real sneaker, making it slightly more believable in terms of structure. Texture and Detail: In Meshy AI, the textures are overly dramatic, with exaggerated shading and surface marks that can make the model feel less cohesive. The material representation lacks subtlety, and the surface details do not align naturally with real-life fabric or rubber. The textures in Trellis3D are simpler but more balanced. While not highly detailed, the model avoids the harsh highlights and oversaturation seen in Meshy AI, giving it a more understated and realistic appearance. Cohesion and Completeness: In Meshy AI, the elements of the model, such as the laces, sole, and fabric, feel disjointed. The lack of polish in its construction results in a model that appears incomplete and inconsistent. Trellis3D delivers a more cohesive presentation. Although less intricate, the elements are better aligned and feel more harmonious, providing a sense of completeness despite the lack of fine detailing. In summary , while Meshy AI showcases an ambitious design with intricate textures, its lack of polish and cohesion limits its effectiveness. Trellis3D, although simpler, provides a more balanced and structured model, making it the better choice for realism and overall presentation in this comparison. While the first comparison highlighted TRELLIS's ability to deliver realistic textures and details for modern objects like shoes, its strengths become even more apparent when applied to historical or thematic objects. Let’s now compare TRELLIS and Meshy in generating a medieval weapon model to further illustrate its capability to balance ruggedness, detail, and realism. ## Medieval times weapons wood Image of a medieval times weapons wood that will be sued as input Comparison of Trellis 3D API and Meshy AI of the medieval times weapons wood example Comparing the two Models: Model Design and Realism : In Trellis 3D , the model features rugged textures and a worn appearance, which gives it an aged and historical look. However, the form appears less refined, with a somewhat irregular shape that may detract from its believability as a weapon. In Meshy AI, the model has a sleek and well-defined structure, with smooth yet balanced textures that reflect modern design standards while still retaining a worn, medieval aesthetic. Texture and Detail : In Trellis3D, textures are rugged and emphasize wear and tear but appear overly simplistic and lack finer details such as grain or specific surface imperfections. In Meshy AI, the textures are more detailed, with shading and subtle surface marks that provide depth and a realistic appearance. The material representation (wood/metal) is also more recognizable in Meshy AI. Cohesion and Completeness : In Trellis 3D, while the aged texture aligns with medieval themes, the model lacks structural precision and detail, which may compromise its overall believability. In Meshy AI, the model is more cohesive and complete , with proportional design and polished finishing, making it more convincing as a weapon. In summary, Meshy AI is the better model overall due to its superior structural refinement, detailed textures, and balanced realism. While Trellis3D captures ruggedness, it lacks the precision and detail required to compete with the more polished and visually engaging Meshy AI model. Building on the comparison between Trellis3D and Meshy AI, a similar focus on texture detail, realism, and structural refinement can be seen when evaluating Slitherwing Dragon Egg using Trellis3D and another image-to-3D Model,Tripo3D. ## Slitherwing Dragon Egg Image of Slitherwing Dragon Egg that will be used as input Comparison of Trellis 3D API and Trippo 3D of the Slitherwing Dragon Egg Example Comparing the two Models: Model Completeness: The layering and arrangement of the petals in Trellis are more cohesive and natural, closely mimicking the input image . Each petal appears distinct, with clear thickness and spacing, showing meticulous design work that conveys the model's completeness. Texture: The Tripo3D texture appears smoother, with fewer harsh highlights or reflections. This provides a more refined and polished look compared to Trellis3D. Tripo3D displays better control over lighting and surface reflection, preventing oversaturated bright spots. This makes the texture more visually appealing and realistic. In Tripo3D, the color distribution seems more uniform and less patchy, enhancing the object's overall natural appearance. Tripo3D’s textures have a subtle gloss that gives the object a more organic, life-like feel, compared to the overly reflective or shiny look in Trellis3D. In summary, Trellis demonstrates greater attention to sharpness and high-contrast design, whereas Tripo3D provides better realism and natural texture. ## Jean Claude Van Damme from bloodsport with red shorts Image of Jean Claude Van Damme tfrom bloodsport with red shorts that will be used as input Comparison of Trellis 3D API and Trippo 3D for the Jean Claude Van Damme from bloodsport with red shorts example Comparing the two Models: Model Design and Realism: In Trellis 3D, the model features sharper and more defined contours, with clear outlines and visible muscle structure. However, the lighting is harsher, making the model appear slightly less natural and organic. In Tripo3D, the model has a smoother and softer design, which gives it a more cohesive appearance. However, the lack of detail in textures and overly smooth surfaces detracts from its realism and believability. Texture and Detail: In Trellis 3D, textures are well-defined and exhibit noticeable contrast, particularly in the highlights and shadows, enhancing the model's features. However, the highlights may appear exaggerated, making the surface feel less lifelike. In Tripo3D, textures are more uniform and subdued, lacking the intricate highlights and shadows necessary for dynamic depth. This results in a flatter appearance and reduces the overall visual impact. Cohesion and Completeness: In Trellis 3D, the model demonstrates better definition and structure, with visible attention to proportion and details. However, the lighting contrast might feel overly sharp in certain areas, affecting its cohesion. In Tripo3D, the smoother and less detailed design gives a cohesive but overly simplified look. The absence of distinct textures and fine details impacts the model’s completeness and realism. In summary, Trellis3D is the better model due to its sharper design, clear textures, and better definition, which make it more visually appealing and believable. While Tripo3D offers a smoother and more cohesive appearance, it lacks the detail and realism needed to compete with the refined and dynamic Trellis3D model. ## Conclusion The comparisons throughout this analysis demonstrate the nuanced strengths and weaknesses of Trellis3D, Meshy AI, and Tripo3D in 3D model generation. Trellis3D stands out in many cases for its sharpness, structural clarity, and ability to deliver realistic, detailed models, making it a reliable option for producing polished assets. While Meshy AI and Tripo3D excel in smoothness, lighting balance, and modern aesthetics, their lack of refinement and cohesion in certain models highlights room for improvement. Trellis3D’s capability to achieve rugged realism, proportional accuracy, and cohesive design across different scenarios—from shoes to medieval weapons—showcases its versatility and adaptability for diverse applications. This combination of detailed textures and structural completeness gives Trellis3D an edge over its competitors in specific use cases. Ultimately, the choice of model depends on the priorities of the user—whether precision, realism, or stylistic elements are paramount. However, the examples explored in this blog underline Trellis3D’s capacity to deliver consistent and high-quality outputs, making it a compelling option in the realm of 3D content generation. And if you are interested in any of the other AI APIs that PiAPI provides , feel free to check them out! ## Kling Elements through Kling API Using the Kling Elements Feature through Kling API Hi Developers! On January 21st, 2025, the team behind Kling AI has just released Kling Elements, a major update for Kling, making the announcement in a post on X! And here at PiAPI , we already have Kling elements available for our users through our Kling API ! Official announcement on X about Kling Elements So how exactly does Kling Elements improve on the current image to video AI generation model? Kling Elements allows users to upload up to 4 images as elements (such as people, animals, objects, or scenes) and describe their actions and interactions within the prompt. Kling API then generates a video based on the elements (images used as input) and the prompt, ensuring that the video maintains a consistent style and appearance. This feature is especially useful for achieving consistency in character design and objects, ensuring they remain consistent with the images uploaded when the Kling API generates a video. ## Evaluation Framework Regarding the evaluation framework used for this comparison, we have taken the AIGCBench (Artificial Intelligence Generated Content Bench) . Although this is a framework designed to be used by computers, we've adjusted it for human evaluation, adapting it from an automated system into a manual process. This is the same framework we had used in our previous blog about Kling Motion Brush, namely: - 1. Control Video Alignment (Prompt adherence) - 2. Motion Affects - 3. Temporal Consistency - 4. Video Quality For what each of these categories means and how they could be rated, feel free to read our previous blog for more detail. ## Testing Kling Elements through Kling API For the upcoming tests, we will first present the images that will be used as input into Kling API using the Kling Elements feature. Afterward, we’ll showcase the video generated, along with the corresponding prompt. ## Example 1: Drake vs Kendrick For our first example, we’ll focus on Drake and Kendrick Lamar, especially since Kendrick recently performed at the Super Bowl. Given that the topic of Drake vs Kendrick has been the talk of the town lately, we thought this would be an interesting choice. Below is an image of the 4 Kling elements we are going to use as input into Kling API, alongside a description of each element: Element 1: A picture of Kendrick Lamar Element 2: A picture of Drake Element 3: A picture of the super bowl stadium Element 4: A picture of the Grammy award The 4 Kling elements that are going to be used as input into Kling API for the Drake vs Kendrick example And here is the output video, along with the prompt shown in the description. Prompt: "Kendrick Lamar and Drake in the super bowl stadium fighting over a Grammy award" Control-Video Alignment For control-video alignment, the result closely follows both the prompt and the provided images as both Kendrick Lamar and Drake have the same facial features and are wearing the exact clothes from the images used as input. However, it is far from perfect. A few noticeable issues include the a minor chain that Kendrick Lamar is wearing, which doesn’t resemble a lowercase "a" as seen in the image, but rather looks more like the number "8." Another major issue is with the Super Bowl stadium, which appears more like a concert venue, as there is no visible football field. Motion Affects In terms of motion effects, the movements of both Kendrick Lamar and Drake appear quite realistic and dynamic. Kendrick turns towards Drake at the beginning, and Drake’s hands move noticeably as he shifts back and forth. Temporal Consistency For temporal consistency, there are two noticeable artifacts. The first is that the person standing behind Kendrick Lamar appears as a garbled, humanoid mess, making it unclear what it is supposed to be. Our best guess is that Kling was attempting to create a mascot character, possibly due to the mention of a "Super Bowl stadium" in the prompt. The second, less noticeable artifact occurs midway through the video when Kendrick Lamar raises his left hand for the first time—he seems to be holding what looks like a microphone, but it suddenly disappears. Video Quality For video quality, there is a noticeable issue at the beginning of the video, where Kendrick Lamar's face and hands are heavily blurred for some reason, before returning to normal later on. Overall, even though the output for this example is very impressive for character consistency, there are still quite a few errors and artifacts present in the video. ## Example 2: Duolingo Owl Death and Revival For our second example, we’ll attempt to revive the Duolingo Owl, following the recent announcement of the Duolingo Owl Death on their official Twitter account. Using the Kling API, we’ll try to bring the owl back to life. Below is an image of the two Kling elements we are going to use as input into Kling API, alongside a description of each element: Element 1: A picture of the Duolingo Owl Element 2: A picture of a cartoon graveyard The 2 Kling elements that are going to be used as input into Kling API for the Duolingo Owl Death and Revival example And below is the output video, alongside the prompt shown in the description. Prompt: "A cartoon-style green Duolingo owl rising from a grave in a graveyard like a zombie, first sticking a green wing out of the grave before fully popping out" Control-Video Alignment For control-video alignment, both the Duolingo owl and the cartoon graveyard are generated very impressively in the video. However, it doesn’t fully follow the prompt. The Duolingo owl is positioned atop the grave from the start, instead of rising like a zombie and sticking its green wing out of the grave as prompted. Motion Affects In terms of motion effects, the video is pretty dynamic, with the camera slowly zooming in and the Duolingo owl flapping its wings slightly before flying towards the viewer. However, the dust effects during its jump appear too realistic and don't match the cartoonish style of the Duolingo owl and the graveyard background. Temporal Consistency For this example, there appear to be no issues with temporal consistency, and everything seems to be good. Video Quality Throughout the video, there are moments where the Duolingo Owl's eyes appear slightly blurred; aside from that, there are no issues. Overall, it is similar to the last example, the character and background consistency is very impressive but has some minor issues quality-wise. ## Example 3: Donald Trump wearing a Philadelphia Eagles jersey For our third example, we’ll have Donald Trump wearing a Philadelphia Eagles jersey, celebrating the Eagles super bowl wins in 2025. Given the Super Bowl season, or "Super Bowl time" as some would call it, we thought this would be a fun and fitting example. Below is an image of the 4 Kling elements we are going to use as input into Kling API, alongside a description of each element: Element 1: A picture of a Philadelphia Eagles jersey Element 2: A picture of Donald Trump Element 3: A picture of a bald eagle Element 4: A picture of the super bowl stadium The 4 Kling elements that are going to be used as input into Kling API for the Donald Trump wearing a Philadelphia Eagles Jersey And below is the output video, alongside the prompt shown in the description. Prompt: "Donald Trump wearing a green Philadelphia Eagles jersey, with a bald eagle perched on his shoulder, gazing down at a Super Bowl stadium filled with cheering fans" Control-Video Alignment This example appears to have followed the prompt and input images very closely. The only issue is with Trump’s Philadelphia Eagles jersey, where the text adherence is off, and the back of his shirt differs from the provided image. Motion Affects The motion effects are dynamic, with the bald eagle settling on Trump’s shoulder at the start of the video, followed by the camera circling from in front of him to behind. Temporal Consistency There is one minor artifact, though it's well-hidden. In the middle of the video, when showing Trump's side view, a man holding a camera appears behind him, but the camera has two lenses, which looks unnatural. Video Quality There are two issues with video quality: first, the eagle’s face is slightly blurred throughout, and second, the crowd of people around is heavily blurred. Overall, this is the best example of the Kling Elements feature among all the examples, and it’s very impressive in terms of what it generated. ## Example 4: Captain America For our fourth example, we’ll feature Chris Evans as Captain America battling the Red Hulk, given the recent buzz surrounding Captain America 4 . Below is an image of the 4 Kling elements we are going to use as input into Kling API, alongside a description of each element: Element 1: A picture of Chris Evans Captain America Element 2: A picture of a LEGO Captain America Shield Element 3: A picture of the Red Hulk Element 4: A picture of an asteroid The 4 Kling elements that are going to be used as input into Kling API for the Captain America example And below is the output video, alongside the prompt shown in the description. Prompt: "Chris Evans Captain America, wielding a LEGO Captain America shield, battling the Red Hulk on a moving asteroid" Control Video Alignment This example seems to be missing one element entirely, specifically element 3, the Red Hulk, which is not featured in the video. Additionally, it doesn't follow the prompt accurately, as Captain America is not fighting the Red Hulk. However, aside from that, it’s still quite impressive. The rest of the prompt and input images are followed closely, and if you look carefully, you can see that Captain America's shield is made of LEGO, exactly as instructed, and matches the LEGO Captain America shield from the provided image. Motion Affects In terms of motion effects, the video is quite dynamic, with the camera zooming in on Chris Evans as Captain America, while the asteroid flies in the background. By the end of the video, it appears that Captain America is preparing for battle. Temporal Consistency Regarding temporal consistency, everything seems to be in order, with no visible artifacts. Video Quality The video quality for this example is quite good, with minimal blurring. Overall, the character consistency is quite good in this example, but it missed one element, which is a significant issue. ## Example 5: Anime girl with a black cat For our final example, we’ll test how well the Kling Elements feature works with the anime art style. Below is an image of the 4 Kling elements we are going to use as input into Kling API, alongside a description of each element: Element 1: An image of an AI generated goth anime girl Element 2: An image of an anime black cat Element 3: An image of a baby wearing a Christmas hat Element 4: An image of an anime background depicting a rainy day in a traditional Japanese town The 4 Kling elements that are going to be used as input into Kling API for the Anime girl example And below is the output video, alongside the prompt shown in the description. Prompt: "Anime style, a goth girl petting an anime black cat wearing a Christmas hat in her lap, with everything— including the cat and the rainy traditional Japanese town— portrayed in 2D anime style." Control-Video Alignment For control-video alignment, the AI Christmas animated black cat is shown in a realistic style, instead of the anime art style as specified in the prompt. Aside from that, everything else is quite impressive. The AI generated goth anime girl matches the image provided exactly, along with the background and Christmas hat. Even though the cat is shown in a realistic style instead of an anime one, it still retains the red scarf as seen in the image used as input. Motion Affects The video is dynamic, with the AI generated goth anime girl's hand petting the cat and the cat's head moving in sync with her hand. However, the Christmas hat on the cat appears to be stuck to the girl’s hand instead, as it would have realistically fallen off if it were following real-world physics. Temporal Consistency There are several noticeable artifacts in the video. The first is the Christmas hat, which is on the AI generated goth anime girl's hand instead of the cat, as mentioned in the motion effects. The second is that the girl's hand, the one petting the cat, appears more realistic than anime style, which looks out of place. Video Quality The only issue with video quality in this example is that the AI Christmas animated black cat's face appears slightly blurred throughout the video. Overall, this example is quite impressive, as the video maintains strong consistency with the images, despite a few issues. ## Conclusion Based on the five examples provided, the strengths of the Kling Elements feature are clear. Its character consistency is particularly impressive, with characters appearing exactly as they do in the images or as described in the prompt, including their clothing. The faces of the characters are also quite impressive, closely resembling those in the provided images. However, there are still some weaknesses, such as the blurring of animal faces—in all examples with animals, their faces were either slightly or heavily blurred throughout the video and the artifacts, which, to be fair, are present in most AI-generated videos. Another big issue is that it doesn't always follow the prompt exactly, as seen in example 2, and it missed an element entirely in example 4. In conclusion, the Kling Elements feature is still very impressive, and we look forward to Kuaishou further developing Kling and ironing out the kinks. Also, if you are interested in other AI APIs that PiAPI provides , feel free to check them out! ## Kling 1.6 Model through Kling API Use the Kling 1.6 API through PiAPI, in this blog we will compare the differences between Kling 1.5 and Kling 1.6 Hi developers! On December 19th 2024, Kuaishou , the team behind Kling AI , has just released its new model, Kling 1.6, making the announcement in a post on X! And here at PiAPI , we already have the Kling 1.6 API available for our users! Official announcement on X about Kling 1.6 With the release of Kling 1.6, Kuaishou has claimed that the new model has improved prompt adherence, more consistent and dynamic results, and a 195% overall improvement rate when compared with the Kling 1.5 model. As Kling API (before the update) is already one of the top AI video generation tools like Pika API or Luma Labs Dream Machine API , the new improvement can be a major update enhancing img2vid prompting for AI movie creation. However, at PiAPI, we have done our own versions 1.5 and 1.6 comparisons for both text-to-video and image-to-video, with the evaluation frameworks used shown below and the subsequent results shown in the blog. ## Evaluation framework ## Text-to-video For the text-to-video comparions between Kling 1.6 API and Kling 1.5 API we are going to be using the comprehensive text-to-video evaluation from Labelbox plus "text adherence". This is the same framework we had used in our previous blog comparing Luma Dream Machines 1.0 vs 1.5 namely: - • Prompt Adherence - • Text Adherence - • Video Realism - • Artifacts ## Image-to-video For the image-to-video comparisons between Kling 1.6 API and Kling 1.5 API we are going to be using the AIGCBench (Artificial Intelligence Generated Content Bench) .This is the same framework we used in our previous blog about using the Kling Motion Brush through Kling API namely: - • Control-Video Alignment (Prompt adherence) - • Motion Affects - • Temporal Consistency - • Video Quality ## Text-to-video Comparison For the text-to-video comparisons between Kling 1.6 API and Kling 1.5 API, we are going to simply put the exact same prompts into both models then directly compare the resulting videos side by side. ## Example 1 Prompt: "The camera rotates around a large, decorated Christmas tree as snow falls gently. It slowly zooms in on the glowing golden star at the top of the Christmas tree." Both videos have some issues with adhering to the prompt. In the video generated by the Kling 1.5 API, the golden star is not placed on top of the Christmas tree. Meanwhile, in the video generated by the Kling 1.6 API, the camera fails to rotate around the tree as expected. While both videos are quite realistic, the Kling 1.6 video loses some points due to the snow falling indoors, which doesn't follow real-world physics. Apart from that, neither video exhibits any noticeable artifacts. Overall, for this particular example, Kling 1.5 performs slightly better than Kling 1.6. ## Example 2 Prompt: "A woman is running on a track field, holding a bottle of water and drinking from it. She is wearing a shirt with the word 'NFL' clearly printed on it" Both videos have a woman running on a track field and holding a bottle of water, but Kling 1.6 API does not have the woman drinking from the water bottle, whereas Kling 1.5 follows the prompt by including this detail. Both models exhibit poor text adherence, as the words on the woman's shirt in both videos do not resemble "NFL" in any way. Despite this, both videos are realistic and free of visible artifacts. Overall, Kling AI 1.5 API performs better in adhering to the prompt compared to Kling AI 1.6 API. ## Example 3 Prompt: "A cartoon-style bird, visibly sick with the flu, sneezing into a tissue. Behind it, a billboard reads 'Bird Flu Medicine' in bold, clear text." Both videos have a cartoon-style bird sneezing into something, but they differ in how they follow the prompt. Kling 1.6 adheres more closely to the prompt by having the bird sneeze into a tissue, while Kling 1.5 shows the bird sneezing into a pink towel. As for text adherence, neither video fully follows the prompt as both videos do not display the words "bird flu medicine" on the billboards behind the birds. In terms of animation style, both videos look good within their respective cartoon animation styles. However, there are some visible artifacts in each. In the Kling 1.6 AI API video, the hand holding the tissue appears too humanoid, resembling a human hand rather than a bird wing. Additionally, the fingers seem to pass through the tissue. In the Kling 1.5 API video, a flickering artifact briefly appears in the bottom left corner of the screen during the middle of the video. Overall, both videos are similar in quality; with a little bit more improvement, Kling 1.6 API could even be a good animated movie generator! ## Image-to-video Comparison For the image-to-video comparisons between Kling 1.6 API and Kling 1.5 API, we will first generate an image using Midjourney API. This image, along with an identical prompt, will then be used as input for both models. ## Example 1 Below is the image generated using Midjourney API, which was then used as input for Kling API in this example. Prompt: "A hyper-realistic side view of Superman flying through a clear blue sky, his red cape flowing dramatically behind him, arms fully extended forward in a classic flying pose." Now that we have an image generated by Midjourney API, we will be inserting that image alongside a new prompt into both Kling 1.5 API and Kling 1.6 API, and below are the videos generated Prompt: "Superman is flying through the sky at high speed. The camera follows him, smoothly tracking his movement. As he continues flying, the camera gradually zooms in, focusing on his face until it fills the frame." The videos generated by both Kling 1.5 API and Kling 1.6 API closely follow the prompt, having Superman flying with the camera zooming in on his face. Both videos exhibit a similar level of dynamism, particularly with the capes flowing in the background, and maintain consistent motion throughout. There are no noticeable artifacts in either video. Overall, the quality of both videos is similar. ## Example 2 Below is the image generated using Midjourney API, which was then used as input for Kling AI API in this example. Prompt: "Steve Harvey standing behind the Family Feud podium, holding a fluffy white cat in his arms. The Family Feud logo is prominently displayed on the front of the podium." Now that we have an image generated by Midjourney API, we will be inserting that image alongside a new prompt into both Kling 1.5 AI API and Kling 1.6 AI API, and below are the videos generated Prompt: "A man is holding a cat in his hands. Suddenly, the cat leaps out of his hands, jumping toward the camera. The man looks shocked and surprised as the cat jumps away from him, moving quickly toward the viewer." Kling 1.6 AI API adheres more closely with the prompt than Kling 1.5 AI API. In the video generated by Kling 1.6, the cat jumps directly toward the camera, while in the video created by Kling 1.5, the cat jumps to the left. Both videos are dynamic, but the Kling 1.6 video is a lot more realistic. The expression on Steve Harvey's face looks more natural, and both his movements and the cat's movements appear more natural. Additionally, the Kling 1.6 video is free of visible artifacts, unlike the Kling 1.5 video, where a noticeable artifact appears after the cat jumps from Steve Harvey's hands—specifically, a small, cat-like creature that seems to appear out of nowhere, which is clearly unrealistic. Overall, the video produced by Kling 1.6 is far better in terms of quality when compared to the one generated by Kling 1.5. ## Example 3 Below is the image generated using Midjourney API, which was then used as input for Kling API in this example. Prompt: "Close-up of an anime-style girl with a focused expression, standing on a wooden dock over a calm lake, holding a fishing rod. The serene water and soft, scenic background fade out, focusing on her face and fishing action." Now that we have an image generated by Midjourney API, which looks straight out of an anime art ai generator library, we will be inserting that image alongside a new prompt into both Kling 1.5 API and Kling 1.6 API, and below are the videos generated. Prompt: "Anime-style video, A girl uses all her strength to reel in a big fish from the lake, then pumps her fist in the air in triumph after catching it." Neither video fully adheres to the prompt. In the video generated by Kling 1.5 API, the girl fails to pump her fist in the air after catching the fish. Meanwhile, in the Kling 1.6 API video, there is no fish at all. Both videos feature dynamic motion effects. The lake's water in the background moves realistically, and the girl moves dynamically in both clips. However, the animation in the Kling 1.6 video appears much smoother overall. That said, in the Kling 1.5 video there is a very visible artifact the girl's face undergoes unnatural morphing. Overall, Kling 1.6 is slightly better than Kling 1.5 for this example. ## Conclusion Based on the six examples provided, it's clear to see that there are minimal differences between Kling 1.5 API and Kling 1.6 API for text-to-video generation. In fact, considering the three examples we’ve analyzed, you could even argue that Kling 1.5 outperforms Kling 1.6 in this area. However, when it comes to image-to-video generation, the Kling 1.6 API shows significant improvements, particularly in terms of movement quality and how well the model follows prompts related to movement. With that being said, we still don't believe that it is a 195% improvement when compared to Kling 1.5 like Kuaishou claimed, but these improved aspects would make the tool very valuable for workflows such as creating a motion meme. With this major leap, we can see that Kling 1.6 is on par or even exceeds other tools on the market, such as the popular Sora video generator. It can reimagine an image to video using AI very well, it can be a tool for artwork creation (ex. if developers want to create an AI frame generator), and it can even be a great tool for lip sync AI. We hope that you found our comparison useful! And if you are interested check out our other generative AI APIs from PiAPI! ## Sora API vs Kling API - a comparison of AI video generation Models OpenAI's Sora just released, and PiAPI is investigating whether or not we want to launch Sora API, as part of this investigation we will be comparing Sora API to Kling API It's finally here! On December 10th 2024, OpenAI , the team that pushed AI to the forefront of technology, officially announced the release of Sora , their highly anticipated AI video generation model, in a post on X! In their tweet, OpenAI also revealed that Sora offers features such as text to video generation and image to video generation, alongside the option to extend, remix, or blend videos you already have. OpenAI's announcement on X about the launch of Sora As the leading AI API provider, we at PiAPI are currently exploring the possibility of launching Sora API in the future. Since we already have Kling API and Dream Machine API , our investigation is focused on evaluating whether or not Sora API outperforms Kling API and Luma API while also determining the level of demand for what the AI video generation model offers. In this blog, we’ll be comparing the generation quality of our Kling API with that of Sor a API to see how they measure up. Due to the high demand for Sora API, which has led to supply issues, our comparison will rely on videos showcased by OpenAI on their website for Sora. We will be comparing these to videos generated using Kling API v1.5 Pro. Here’s how the process will work: we’ll take a screenshot of the initial frame from a Sora-generated video, input it into Kling API v1.5 Pro, and generate a video using a prompt that we think is best. Finally, we’ll compare the results from both to evaluate their performance. ## Comparison For the following comparisons, we’ll be using the same image-to-video evaluation framework detailed in our previous blogs, which you can refer to for more details. However, we won’t be evaluating the Control-Video Alignment criterion—basically just prompt adherence—since we do not know the prompts used in the videos OpenAI inputted into Sora. ## Example 1 Sora vs Kling (prompt: monkey rollerskating on the street) Both videos do a great job with realism, with the shadows of the monkey and the trees lining up perfectly, making it hard to pick a clear winner. They also handle dynamic movements really well and are super smooth, with no jump cuts or artifacts. Overall, it’s a tie for this example, as both videos seem pretty much on the same level in terms of quality ## Example 2 Sora vs Kling (prompt: Camera zooms out from the lighthouse) Both videos appear highly realistic, with waves splashing onto the island and lighthouse in a lifelike manner. The movements are smooth and natural overall. However, in Kling's video, the waves don’t fully adhere to real-world physics. After the initial splash on the left side of the island, the waves unnaturally move upward toward the lighthouse, which seems unrealistic given the small size of the initial splash—it doesn’t justify such a large upward motion. Despite this, both videos demonstrate strong consistency, maintain continuity throughout, and are free from visual artifacts So, Sora slightly outperforms Kling for this example. (It’s worth noting that the prompt for Kling specifically instructs the camera to zoom out from the lighthouse. However, instead of following this, the camera zooms in.) ## Example 3 Sora vs Kling (prompt: A rocket launches into the sky) Sora’s video looks realistic and dynamic, showing a rocket launching into the sky. Kling’s video, on the other hand, isn’t realistic at all—it has something that’s supposed to be a rocket but looks more like a candle, popping out from behind the moon before slowly taking off, making it less dynamic than Sora's video. Sora’s video does have an issue with consistency, though, as it suddenly cuts to a blue light at the end. Meanwhile, Kling’s video, even though it’s not great quality, stays consistent throughout. Overall, Sora API outperforms Kling for this example. ## Example 4 Sora vs Kling (prompt: The camera panning from the left side of the room to the right) Both videos look realistic and have smooth, fluid movements, but Kling’s video is a bit more dynamic. The person in Kling’s video moves around more, and the camera pans further to the right compared to Sora’s. Both videos stay consistent the whole time—no artifacts, no jumpcuts, and everything flows smoothly. For this example, Kling edges Sora out by a small margin. ## Example 5 Sora vs Kling (prompt: Camels walking to the left in a desert) At first glance, both videos look realistic, but if you look closer, there’s an issue with the first camel's shadow on the far left. In both videos, the shadow suddenly moves on its own, completely disconnecting from the camel that’s supposed to be casting it. Then, the shadow is replaced by another one. Sora’s video is far more dynamic, featuring a greater number of camels that move faster and travel farther than those in Kling’s video. For this example, Sora outperforms Kling. ## Example 6 Sora vs Kling (prompt: A plant growing out of the dirt) Both videos appear quite realistic and adhere well to real-world physics, with smooth and dynamic motion. However, Sora’s video is noticeably more dynamic, showcasing the growth of a larger plant, whereas Kling’s video only features a small leaf. Both videos maintain strong temporal consistency and continuity throughout, staying free from any visual artifacts. Overall, Sora outperforms Kling for this example. ## Example 7 Sora vs Kling (prompt: The man walking towards the building) Both videos appear quite realistic, and adhere well to real-world physics, with smooth and dynamic motion. Both videos also show strong temporal consistency and continuity throughout, being free from any visual artifacts. Overall, both videos have the same quality, for this example. ## Conclusion Based on the seven examples above, it’s clear that Sora API generally outperforms Kling in several aspects. However, it’s important to note that we don’t know whether the videos from Sora API were generated using image-to-video or text-to-video prompts, nor do we know the exact prompts used. This could make the comparison less fair, but we’ve made every effort to recreate the videos as accurately as possible using Kling. We’re excited to see how OpenAI’s Sora evolves in the future and to continue exploring whether PiAPI should introduce its own Sora API. We’re also eager to see how Sora develops further and whether OpenAI can surpass other companies in advancing AI video generation models. Additionally, we look forward to seeing how Sam Altman steers OpenAI, guiding the company into the next phase of AI innovation. We hope that you found our comparison useful! And if you are interested, check out our collection of generative AI APIs from PiAPI ! ## OpenAI's Realtime API (powering ChatGPT Advanced Voice Mode) vs Moshi API A detail comparison between OpenAI's Realtime API vs Moshi API - for conversation AI applications! ## Introduction In this blog, we are going to explore and compare the latest advancements in Conversational AI APIs. But first, let's consider the importance of Conversational AI. Typing with our thumbs on a 6" screen is neither the most intuitive nor efficient way for us, compared to communicating vocally - something that we have done effortlessly since birth. Thus, it would seem logical that when it comes to AI applications, we as a species would naturally pick speaking over typing as the preferred mode of interaction. In fields such as customer support, personalized education, language training, and healthcare therapy, Conversational AI is the key to building intuitive, hands-free, emotionally rich products that the public would likely adopt. Thus, we have decided to work on this comparison blog, to examine the two most promising audio AI APIs that can become the backbone of such applications, namely, OpenAI's Realtime API, and Moshi API. ## Prior to Voice Native AI Model... If we turn the clock back to the days before Realtime API and/or Moshi API, let's examine how a typical Conversational AI application would work, and we call this workflow "transcribe-reason-text2speech":user provides an audio input; - 1. the application transcribes the input audio into text using ASR (Automatic Speech Recognition) model (for example, the Whisper model from OpenAI ); - 2. the application passes the transcribed text to text-to-text model (for example GPT-4o ) for reasoning ; - 3. the application plays the textual output using a TTS (Text-to-Speech) model (for example, the audio models from OpenAI). As we can see, this workflow will most definitely result in long latency, which is a significantly barrier to a smooth user experience in a conversational AI application, thus rendering the workflow unusable in a majority of use cases. Thus next, let's start examining the newest conversational AI APIs, and the improvements they bring. ## What is Realtime API? First, let's look at Realtime API from OpenAI. The API was first released on October 1st 2024 by OpenAI to assist developers to build smooth speech-to-speech applications. With six presets voices, and based on the GPT-4o model , developers can use this low-latency, multimodal API to build real-time voice-activated applications. ## What is ChatGPT's Advanced Voice Mode? Since a lot of users get confused about the difference between the ChatGPT's Advanced Voice Mode vs. the Realtime API, we thought it would be good to clarify. The former is a feature within ChatGPT (which is a chatbot product developed by OpenAI - one of the most popular in the world) based on the model GPT-4o's multimodal ability. This feature is rolling out to Plus and Team users first, and it also has a standard version available to all ChatGPT users through IOS and Android Apps. The standard version works by adopting the traditional aforementioned transcribe-reason-text2speech approach. On the other hand, the Realtime API is a multimodal, voice-native API that can assist developers building out their respective applications, not a feature on another chatbot product. ## Audio Input and Output in the Chat Completion API As part of the Realtime API's release, OpenAI also mentioned that they would be soon releasing audio input and output in the Chat Completion API for usecases where low-latency is not a concern. The API's input could be text or audio and API output could be text, audio or both. What is exciting for developers is that, previously one would have to stitch together multiple models to achieve the "transcribe-reason-text2speech" workflow powering the conversational experience. Now, one would just to make a single API call from Chat Completion API and the API system would take care of the rest. For the purpose of this blog, we are going to focus on comparing only Realtime API from OpenAI with the Moshi API, and deliberating leaving out the Advanced Voice Mode and the Audio Input and Output feature in the Chat Completion API, in order to have a more related and aligned analysis. ## What is Moshi API? After providing the basic context on Realtime API (and other products) from OpenAI, let's move onto Moshi API. The Moshi voice-native model was first released by the French Kyutai Team in July 2024,. With its state of the art Mimi codec processing two streams of audio (user's input and Moshi's output) simultaneously, the model significantly improves the quality of its next-token prediction. In October 2024, PiAPI is planning to soon release the Moshi API to developers to help build realtime-dialogue applications. ## Moshi API vs. Realtime API Comparison After we've provided the necessary context on Voice Native Models, let's now start the comparison between the two APIs on different aspects which would affect the development process. ## Speed & Latency Based on public forums , the Realtime API offers extremely fast token generation. Although OpenAI did not provide any official information on its speed, it seems like developers are finding the token generation process is so fast that the interruption function call are deemed redundant since all the text tokens are generated before they are finished playing as audio output. Moshi on the other hand, is known for using its Mimi codec achieving sychronized text/audio output. Their GitHub repository does not explicitly state Moshi's token generation rate, but it does mention that audio is processed at "a frame size of 80ms" and "a theoretical latency of 160ms" is achieved, with "a practical overall latency as low as 200ms on an L4 GPU". For real-time application, 200ms of latency is generally considered to be quite good, as it is below the threshold that most humans would notice as a significant delay in conversations. It is also worth to note about network latency, given Moshi is a open source model, inference providers such as PiAPI can provide servers that are geographically close to the client server and proivde custom CDN infrastructure to further reduce latency caused within the network. ## Reasoning Coherency & Accuracy In terms of model accuracy there are several aspects we need to look at. Firstly, the Realtime API uses GPT-4o as the underlying inference model and GPT-4o is a much bigger and more complex model compared to the open source Moshi model. Thus it is reasonable to say that the Realtime API will have superior overall reasoning compared to the Moshi API. However, given the open source nature of Moshi, development team is able to fine-tune the base model as per specific usecases. Moreover, if we look at the potentially popular usecases for conversational AI (ex. customer support, education, healthcare, etc.), they are all areas where domain-specific or organization-specific information are crucial to achieve satisfactory output quality. ## Flexibility & Customizations As part of comparing flexibility and customizations, we'd have to again bring up fine-tuning as a major advantage for Moshi, allowing it to be tailored for specific usecases. Futhermore, communities of fine-tuned or trained models might form and give birth to the Civitai equivalent of the Moshi ecosystem, allowing models with different emphasis to be shared across users, rapidly increasing innovation and development progress. On this note, OpenAI can also provide fine-tuning like functions for the Realtime API, and develop the infrastructure for users to share their usecase-specific models just like their GPTs endeavour. However, whether they chose to spend their development resource on that, and whether it would succeed, remain to be seen. In terms of function call and RAG (Retrieval Augmented Generation), Realtime API already supports function call and the Moshi ecosystem should support it in the future as well given the open source nature of the model. Both models are expected to have tools to build out the relavant RAG workflow. ## Rate Limits & Context Lengths In terms of rate limits for Realtime API, it is currently rate limited to approximately 100 simultaneous sessions for Tier 5 developers, with lower limits for Tiers 1-4. However OpenAI also mentioned that they will increase these limits over time to support larger deployments, and that the Realtime API will also support GPT-4o mini in upcoming versions of that model. For Moshi, rate limits would like not be a problem since clients total daily usage and peak throughput could be roughly calculated and extra inference instances could be added to accommodate each specific load profile. For context length, the Realtime API allegedly can hold upto 8k tokens. Moshi on the other hand, depending on its configurations and fine-tuning results, its context length will likely vary. ## Pricing Given the significantly larger size and complexity of the GPT-4o model, the Realtime API naturally would use more hardware resources for inference and memory compared to Moshi, and this would appear quite evident in their respective API pricing. For Realtime API, its pricings are as follows: - • Text Input tokens: $5 per 1M tokens - • Text Output token: $20 per 1M tokens - • Audio Input tokens: $100 per 1M tokens ($0.06 / min of audio) - • Audio Output tokens: $200 per 1M tokens ($0.24 / min of audio) For Moshi API, PiAPI is estimated to able to achieve the following pricing: - • Audio Input & Output tokens: $0.02 / min of audio ## Conclusion Given the comparison provided above, hopefully it was informative as to which conversational AI API might suit your needs better. We here at PiAPI are indifferent about developers' decision since most likely we will support both. However, given the open source nature of Moshi and the impressive results we've seen so far, we are very excited to see how far the open source ecosytem will go and where it will take all of us to! Also, if you are interested in other AI API that PiAPI provides , please check them out! ## How PiAPI's Kling AI API is better than the Official Kling AI API (cost, speed, and features)! In this blog we discuss on how PiAPI's Kling AI API is better than the Official Kling AI API in terms of cost, speed, and features! ## Introduction As you’ve probably heard by now, Kuaishou has launched their official Kling AI API to a number of limited developers on September 30th, 2024. However, the official Kling AI API has some major drawbacks, such as requiring developers to pay an upfront fee of $4,200 for three months, with no lesser usage options, and the correspondingly monthly units expiring at the end of each billing month. Official announcement of the release of the Kling AI API on their website At PiAPI , we're providing a superior version by developing on top of the official API, offering the same functionality plus extra features, all at a lower price for developers. Now, let's get into the detailed comparison, so you can decide for yourself which one is better. ## Comparison Here's a table we put together showing the different plans PiAPI offers and the cheapest upfront plan from the Official Kling AI API. Overview Comparison Table for PiAPI's Kling API vs. the Official Kling API Now, we'll take a closer look at the comparison table and explain it in more detail. ## Unit Pricing PiAPI's Kling AI API price per video is as low as $0.13. (note that PiAPI's pricing will be as per this blog starting October 20th, 2024) The Official Kling AI API price per unit is $0.14. For them, a unit is what video generation tasks will consume, for example: - • In standard mode , a 5-second video requires 1 unit, while a 10-second video requires 2 units. Thus, a standard mode 5-second video costs $0.14, and a standard mode 10-second video costs $0.28. - • In professional mode , a 5-second video uses 3.5 units ($0.49), and a 10-second video uses 7 units ($0.98). For PiAPI, however, we just directly use USD per video to calculate the cost. To simplify things, we've created a detailed table outlining the cost comparison. The detailed Pricing Table Comparison between PiAPI and the Official Kling API ## Upfront Fee Next up, let's look at the upfront fee. But first, what does that term mean for the services we are comparing?The upfront fee is the initial cost required to access the API platform. While some service providers require significant upfront payments, PiAPI offers a more flexible and cost-efficient approach. Here’s a comparison between PiAPI's Kling AI API and the Official Kling AI API: - • PiAPI's Kling AIAPI : PiAPI requires $0 minimum upfront fee for credit purchasing since it is a pay-as-you-go model (you use what you top up). For features, users can start on the basic free plan and if you are interested in the advanced features, you can subscribe to the corresponding Creator Plan ($8/month) or Pro Plan ($50/month). Overall, there are no large commitments required with PiAPI's offering. - • Official Kling AIAPI : The Official Kling API requires a $4,200 upfront payment for a three-month period, which includes 10,000 units per month (expiring at the end of each billing month). Kling does not offer any lower credit plans, and the $4200 is a significant investment for development teams who are experimenting with the Kling API and are uncertain about future usage growth. Another key difference is how long unused units/credits remain valid: - • For the official Kling API, the upfront $4200 payment per 3 months corresponds to 10,000 units per month, and unused units expire after each respective 30 days , forcing developers to use them quickly or lose them. - • Whereas in PiAPI's pay-as-you-go model, unused credits remain valid for 180 days from the date of the purchase, giving developers more time to use them as needed. ## Features Next, we’ll break down the features of both APIs and explain why PiAPI’s Kling AI API is the best option. Full Feature Support: PiAPI's Kling AI API offers all the features that Official Kling AI API offers, including: - • AI Text-to-Video generation - • AI Image-to-Video generation - • Support for the V1.0 Model - • Ability to handle multiple concurrent jobs - • Negative Prompts - • Watermark Removal - • Add End Frame - • Camera Movement (text-to-video) Max Concurrent Jobs Both APIs support multiple concurrent video generation tasks, but PiAPI provides significantly more concurrency than the Official API. The Official Kling AI API supports only to a maximum of 5 concurrent jobs, even after developers are forced to pay $4200 for a 3-month commitment. If the developers are expected to use up all the 10,000 credits, which is about 333 jobs per day, sometimes 5 concurrent jobs might result in long queue time. PiAPI's Kling AI API, other the hand, supports up to 20+ concurrent jobs, which is four times the concurrent job capacity than what the official is offering, making it a more useful option for users with high-volume needs. Additional API Features Besides handling more concurrent jobs and supporting all the features of the Official API, PiAPI’s API offers extra features that you won’t find in the official version, including support for: - • v1.5 Model - • Motion Brush (WIP) - • Lip Sync API (WIP) Kling released the v1.5 model recently, and the official team mentioned that it's a major improvement over the v1.0 model. Therefore, it easy to see why many developers would want the option to run the v1.5 Model through the API, but unfortunately, the Official Kling AI API doesn't offer that functionality. Motion Brush is also another tool that was released by Kling, allowing you to select an object in an image and create movement paths for precise and dynamic motion control. It is yet another powerful tool that is unavailable in the Official Kling AI API. Similarly, the Lip Sync tool is another powerful tool in Kling AI. What it allows you to do is input text, pick a voice, and generate both voiceover and video for your character. But like Motion Brush and the v1.5 Model, it is missing from the Official Kling AI API. But luckily, PiAPI’s Kling AI API gives you access to all of these features, helping you to build more powerful applications for your users. - Other AI APIs! Not only does PiAPI offer additional Kling AI API features, but with a PiAPI account, you can use the credits purchased on any of our AI APIs besides Kling. This ensures your payments are more efficiently spent, and reducing the risk of unused credits. Our family of AI APIs offering include: - • Midjourney API - • Faceswap API - • Ace-Step API - • Luma Dream Machine API - • Moshi API (coming soon!) - • Flux API Also, if developer chose the Pro Plan ($50/month) for Kling, then other premium features of our other AI APIs could also be used, such as: - • Midjourney API: 30 concurrent jobs, task Logs that last 30 days, bot_id selection, intermediate image URLs, Auto-CAPTCHA solver - • Faceswap API: 200 calls / month for free - • Ace-Step API: 30 concurrent jobs, generation, upload audio, extend music - • Luma Dream Machine API: image-to-video generation, add end frame, video extend, watermark removal - • Flux API: 10 concurrent jobs, top priority generation, LoRA (WIP), ControlNet (WIP) For more details, please refer to PiAPI's pricing page and related documentation for more information. ## Alternative Option: Host-Your-Account For developers who already have their Kling account(s), PiAPI also allows developers to use their own Kling accounts through our API. This is our Host-Your-Account (HYA) option. How it works is that you'd pay for a Kling API HYA "seat" for just $10 a month, then you can connect your own Kling account to this seat and start making API calls to your own Kling account. The number of video generations available under the HYA option will purely depend on the number of credits available in your Kling account, with no extra charges coming from PiAPI besides the $10/seat. Users can still start on the Free Subscription Plan, and if they are interested in the advanced features, they could subscribe to the $8 Creator Plan or the $50 Pro Plan anytime. The $10 paid for the seat differs from the $8 or the $50 paid for the subscription plan, where: - 1. $10/month is the monthly cost of a seat - 2. $8/month or $50/month will provide the users with different features in different subscription plans (note that concurrent job limit and whether you can remove watermarks are both determined by your own Kling account) Therefore, for the HYA option, the total upfront fee consists of the following: - 1. Personal Kling account(s) cost - 2. At least one PiAPI HYA seat ($10/month) - 3. PiAPI Subscription Plan (can start with $0/month and upgrade as needed) This option has the potential to be even cheaper than the Pay-as-you-go option offered by PiAPI. Since Kling offers major discounts for the first subscription month, and free Kling accounts can also process jobs. ## Conclusion PiAPI's Kling AI API clearly stands out as the smarter choice for indie developers or big development teams alike. Not only do we provide all the features of the Official Kling AI API, but we also offer extra features that are extremely popular with developers and end users, and all of this comes at a lower cost compared to the official API. This makes it the perfect choice for anyone looking for both a high-quality and cost-effective solution. We hope that you have found this comparison useful! And if you are interested, check out our full range of generative AI APIs at PiAPI! ## Crush it Melt it through Pika API , Kling API & Luma API In this blog we will be exploring the new Crush It Melt It trend and see if Luma API and Kling API can do as well as Pika API! ## Introduction On October 1st 2024, Pika Labs officially announced the release of Pika 1.5 , claiming their model now has more realistic movements and big screen shots. But the most exciting and popular addition is the new Pikaffects feature, which lets you create effects that "break the laws of physics" with the click of a button, no text prompt needed. Pika Lab's announcement on X about Pika 1.5 The "Crush it Melt it" trend has taken off on social media, with Pika's official video alone racking up nearly 14 million views on TikTok in the past week. Other videos following the trend have pulled in between 100,000 and 1 million views each. As the leading AI API provider, we at PiAPI are also deciding whether we should launch Pika API, since we already offer Kling API and Luma API . As part of this decision-making process, we looked at the new "Crush it Melt it" trend and tried to replicate the effect and compare the results between Pika API, Kling API, and Luma's Dream Machine API. ## Comparisons But before we start comparing the three different AI models, let us first establish our comparison method.For Pika 1.5 API, we will simply apply the corresponding Pikaffect. For Kling API and Luma API, we'll try to replicate the Pika Art API effect by testing a variety of prompts and selecting the best result for the comparison. ## Example 1: Crush it For our first example, we are going to use the Moo Deng Hippo Plushie . Seeing as this trend has been popular with plushies and figurines, we thought we'd try using them as well and see what results we get. Below is an image of the Moo Deng Hippo Plushie, which we’ll use alongside unique "Crush It" prompts or the Crush It Pikaffect as input for the three AI models we are going to compare. The image of a Moo Deng Plushie that will be used as input into Pika API, Kling API, and Luma API And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: Hydraulic press crushens and flattens Hippo Plushie) vs. Pika (Pikaffect: Crush it) vs. Kling (Prompt: Hydraulic press crushing hippo plushie) Pika Pika's output is the most impressive, with the hydraulic press smoothly crushing the Moo Deng Plushie, aligning with the Pikaffect "Crush It" prompt. The video shows fluid and dynamic movements as the hydraulic press crushes the plush, resulting in a realistic final result with no artifacts. Kling Kling's output ranks second overall, though it doesn't fully adhere to the prompt. Instead of a hydraulic press crushing the Moo Deng Plushie, it has a strange metal object crushing it. The video is fluid and dynamic, but the plushie exhibits strange unrealistic movements, with its body twitching and shaking. On the plus side, there are no visible artifacts. Luma Luma's output was by far the worst, not adhering to the prompt at all. There was no hydraulic press even though it was specified in the prompt, instead the Moo Deng Plushie fell over and morphed into something unrecognizable. The whole thing felt unrealistic, especially with the plushie’s random transformation. Also, an artifact appeared when it fell onto what looked like an iron table, even though the prompt never mentioned any kind of table. Overall, Pika's output far outshines Kling or Luma in the "Crush It" test. ## Example 2: Melt it For our second example, we are going to use a figurine of the popular YouTuber KSI . Below is an image of the KSI figurine, which we’ll use alongside unique "Melt It" prompts or the Melt It Pikaffect as input for the three AI models we are going to compare. The image of the KSI figurine that will be used as input into Pika API, Kling API, and Luma API And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: Static camera shot of the figurine melting into liquid form and becoming a puddle on the ground) vs. Pika(Pikaffect: Melt It) vs. Kling (Prompt: Static camera shot of the figurine of the man melting into liquid form and becoming a puddle) Pika Pika delivers an impressive result with the KSI figurine melting realistically, and the final touch of the shoes melting last is a nice addition. The motion is fluid and dynamic, with the entire melting process feeling realistic. Kling Kling's video is pretty well made, showing the KSI figurine's head melting first, but the issue is that it doesn't turn into a puddle as the prompt described. The way the melting process moves and flows feels natural, closely following real-world physics. Luma Luma's output is also really good—the KSI figurine's arms detach and melt into a puddle on the ground, just as described in the prompt. The motion is dynamic; the melting looks real, and the liquid physics are highly impressive. Overall, they all produce similar results for the "Melt It" prompt, and none of the videos have any artifacts. ## Example 3: Cakeify It In this third example, we’re taking a Minecraft grass block and transforming it into a cake! Below is an image of the Minecraft grass block, which we’ll use unique "Cakeify It" prompts and the Cakeify It Pikaffect as input for the three AI models we are going to compare. The image of the Minecraft grass block that will be used as input into the three APIs And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: Realistic video of a kitchen knife cutting through the minecraft grass block and cutting it open, with the insides being cake) vs. Pika (Pikaffect: Cakeify It) vs. Kling (Prompt: Same as Luma's) Pika The result from Pika is amazing, looking just like those trendy "cake or not" videos from a while ago. The hand holding the knife moves smoothly, the knife itself looks good, and the cutting action feels dynamic and lifelike, even leaving cake crumbs on the knife after slicing through the Minecraft grass block. But if you look closely, you'll notice an artifact—the tip of the knife is left in the cake after it's been used to cut it open. Kling Kling didn't do so well in this example. Near the end, they cut open the Minecraft grass block, revealing a plain white interior instead of a cake-like one. But the more glaring issue is that midway through the video, it abruptly cuts to a completely different object - a more realistic, cake-like version of the Minecraft grass block. Another problem is that the cut on the object doesn’t align with where the knife is actually cutting. Luma Luma did a decent job, although the output isn't as clean as Pika's and has random objects cluttering the video. We see the kitchen knife blade come down, slicing through the Minecraft grass block, and inside, it definitely looks like cake. But after the blade comes down, it seems to vanish, and the finished shape of the object doesn’t line up with where it was cut. There are also some very noticeable artifacts, like random objects falling onto the cake as it’s being sliced. Overall, Pika certainly takes the cake in this example. ## Example 4: Explode It In this example, we'll use an image of a Joaquin Phoenix Joker figurine. Below is an image of the Joker Figurine, which we’ll use alongside unique "Explode It" prompts or the Explode It Pikaffect as input for the three AI models we are going to compare. The image of the Joker figurine that will be used as input into the APIs And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: Close-up of a joker figurine exploding in slow motion. Camera zooms in as cracks appear and fragments fly outward in all directions. Dust particles catch the light from a nearby light, creating a dramatic, chaotic moment.) vs. Pika (Pikaffect: Explode It) vs. Kling (Prompt: static slow-motion shot of the figurine as it breaks apart and explodes into tiny pieces of itself that fly away from the explosion) Pika Pika did a great job in this example. The explosion looks amazing, especially with all the little pieces scattering from the blast. A little impressive detail is that as the Joker figurine exploded, the stand remained untouched. It also looks realistic and closely matches real-world explosion physics. Kling Kling’s output was also impressive, though the size of the explosion didn’t quite match Pika’s, with only a small burst at the base of the figurine. It didn't fully adhere to the prompt though, which asked for a static shot of the figurine, but instead, the camera rotates around it. The explosion physics are impressive, closely resembling real-world physics as the small blast knocked off the figurine’s foot and base. Luma Luma performed the worst in this example, as the "explosion" didn’t even cause the slightest damage to the figurine. Instead, we notice that there are what seem to be fragments of the figurine flying out, despite the figurine being completely undamaged. Overall, Pika also outshines both Luma and Kling in this example as well. ## Example 5: Squish It For the fifth example, we are going to use an alien plou plush . Below is an image of the alien plou plush, which we’ll use alongside unique "Squish It" prompts or the Squish It Pikaffect as input for the three AI models we are going to compare. The image of the alien plou plush that will be used as input into the APIs And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: a pair of hands coming in and squishing the plushie, the plushie morphs as if it were made out of clay when being squished, it gets squished into a ball an becomes a sphere like form.) vs. Pika (Pikaffect: Squish It) vs. Kling (Prompt: a pair of hands coming in and squishing the plushie, the plushie morphs as if it were made out of clay when being squished, it gets squished into a ball an becomes a sphere like form) Pika Pika did a pretty good job in this example, but it doesn’t quite measure up to the quality of the other videos it produced in our prior examples. The squishing effect looks great, as the alien plou plush really seems to get squished realistically. The issue isn’t with how the plush is being squished, but with the hands that does the squishing. When they first appear, it looks like two thumbs are pressing down, which is unnatural since a human’s fingers couldn’t start in that position. Another problem is the fingers pressing on the eyes seem to phase through the plush. Lastly, the pinky fingers look deformed with noticeable bulges. Kling While Kling's video gets decent results, it's noticeably more strange than the rest. The first strange thing in the Kling video is when a hand randomly puts a red object on the plush before it disappears abruptly, which is most likely an artifact. Another strange thing is that the hands appear to remove the plush's eyes, and as they detach, the eyes become extremely blurred. The video then proceeds as the hands gently squish the body of the plush. Also, there are no visible deformities present in the hands. Luma Luma delivers great results as the human hands squish the mouth of the alien plou plush realistically, with no noticeable issues with how the hands or the plush behave. It’s worth mentioning that, although it has no visible mistakes or artifacts, the motions aren’t as dynamic as Pika’s. Pika and Luma deliver the best performance overall, with Pika showing more dynamism and Luma making fewer mistakes. ## Example 6: Inflate It For our last example, we are going to use a Squid Game plush. Below is an image of the squid game plush, which we’ll use alongside unique "Inflate It" prompts or the Inflate It Pikaffect as input for the three AI models we are going to compare. The image of the squid game plush that will be used as input into the three APIs. And here are the output videos of the three different AI models, with the prompts used shown in the description. Luma (Prompt: static camera shot as the plushie bloats and inflates, flying up offscreen like a balloon) vs. Pika (Pikaffect: Crush it) vs. Kling (Prompt: the plushie inflates and floats up like a balloon) Pika Pika did a great job with its video output. We can see the Squid Game plush inflating like a balloon and floating away until it vanishes. Another cool detail is how the plush’s shadow follows it as it flies upwards. Kling Kling's video started out fine, with the Squid Game plush inflating slightly and hovering off the ground at first, but by the end, it seemed to deflate and fall to the floor, despite the prompt mentioning nothing about deflating or dropping to the ground. Luma Luma's video was a decent attempt, but while the plush inflated slightly and tried to float upwards, it didn’t get off the ground. It also didn't fully adhere to the prompt since the plush didn't fly up offscreen. Overall, Pika delivers the best performance for this example as well. ## Our Other Tests Before we get to our conclusion, let's quickly review the other tests we'veconducted. Although we didn’t include these tests in the main comparison, we hope they'd help you understand how different prompts influence the outcomes, as shown by our tests below. ## Crush It ## Luma The following are the other tests we did to recreate the "Crush It" effect using Luma API. Other "Crush It" tests we did using Luma API The following are the prompts used: L1: Hydraulic press crushing a hippo plushie L2: Hippo plushie gets squished and crushed by a hydraulic press coming from the top L3: Hippo plushie gets crushed by a hydraulic press coming from the top, flattening the hippo plushie L4: Hippo plushie gets crushed by the cylinder of the hydraulic press coming from the top, flattening the hippo plushie L5: Slow-motion close-up of the plush hippo under the hydraulic press. The soft fabric starts to compress, with subtle creases forming. Gentle lighting highlights the squishy texture. Tension builds as the press lowers. Playful, oddly satisfying. L6: Hydraulic press crushens and flattens Hippo Plushie, leaving it becoming flatand the hydraulic press crushing it from the top L7: hippo plushie gets crushed and becomes flattened L8: still hippo plushie gets crushed and becomes flattened L9: Static camera shot of the hippo plushie getting crushed by a heavy object, it's body flattening, pressure is being applied to it's soft body, by the end it should be a flatter version if itself. ## Kling The following are the other tests we did to recreate the "Crush It" effect using Kling API. Other "Crush It" tests we did using Kling API The following are the prompts used: K1: Hydraulic press crushing hippo plushie, flattening it K2: Hydraulic press cylinder crushing hippo plushie, flattening it, it is beng flattened as like how clay is flattened. Satisfying and relaxing mood. K3: Hydraulic press cylinder crushing hippo plushie, flattening it, it is beng flattened as like how clay is flattened. Satisfying mood. K4: Hydraulic press cylinder crushing hippo plushie, flattening it, it is beng flattened as like how cay is flattened. Satisfying mood. K5: Static camera shot of the hippo plushie getting crushed by a heavy object, it's body flattening, pressure is being applied to it's soft body, by the end it should be a flatter version if itself. K6: Hippo plush gets crushed by a heavy object, as it body flattens and becomes a squished version of itself ## Melt It ## Luma The following are the other tests we did to recreate the "Melt It" effect using Luma API. Other "Melt It" tests we did using Luma API The following are the prompts used: L1: A Figurine Being Melted L2: The Figurine melting into a liquid form L3: The Figurine melting into a liquid form and ends up as a liquid puddle on the floor L4: The Figurine melting into a liquid form due to extreme heat and ends up as a liquid puddle on the floor L5: Static camera shot of the figurine completely melting into liquid form and becoming a puddle on the ground L6: Static camera shot of the entire figurine melting into liquid form and becoming a puddle on the ground. The figurine is melting similar to the way ice melts and becomes a liquid L7: Static camera shot of the figurine of the man melting into a flowing liquid form like water and becoming a puddle on the ground. The figurine is melting similar to the way plastic melts when exposed to high temperatures and becomes a liquid. At the end of the video the figurine is completely turned into liquid. L8: Static camera shot of the whole body of the figurine completely melting fast into liquid form and the whole figurine becomes a puddle on the ground L9: Static camera shot of the whole body of the figurine completely melting fast into liquid form and the whole figurine becomes a puddle on the ground. The liquid is on the floor as all the liquid drops to the ground and becomes a puddle ## Kling The following are the other tests we did to recreate the "Melt It" effect using Kling API. Other "Melt It" tests we did using Kling API The following are the prompts used: K1: Static camera shot of the figurine melting into a puddle K2: Static camera shot of the figurine of the man melting into liquid form and becoming a puddle. The figurine is melting similar to the way plastic melts when exposed to high temperatures and becomes a liquid. At the end of the video, the figurine is completely turned into a liquid puddle. K3: Static camera shot of the figurine of the man melting into liquid form and becoming a puddle. The figurine is melting similar to the way plastic melts when exposed to high temperatures and becomes a liquid. At the end of the video, the figurine is completely turned into a liquid puddle. K4: Static camera shot of the figurine melting completely in its entirety into liquid form and becoming a puddle. The figurine is melting similar to the way plastic melts when exposed to high temperatures and becomes a liquid. At the end of the video, the figurine is completely turned into a liquid puddle. K5：Static camera shot of the figurine of a black man melting completely in its entirety into liquid form and becoming a puddle. The figurine is melting similar to the way plastic melts when exposed to high temperatures and becomes a liquid. At the end of the video, the figurine is completely turned into a liquid puddle. K6: Static camera shot of the whole body of the figuring completely melting into liquid from and resulting in a puddle on the ground K7: Static camera shot of the whole body of the figurine completely melting fast into liquid form and the whole figurine becomes a puddle on the ground K8: Static camera shot of the whole body of the man completely melting fast into liquid form and the whole man becomes a puddle on the ground K9: Static camera shot of the whole body of the figuring completely melting quickly into liquid from and resulting in a puddle on the ground as the final result ## Cakeify It ## Luma The following are the other tests we did to recreate the "Cakeify It" effect using Luma API. Other "Cakeify It" tests we did using Luma API The following are the prompts used: L1: Knife cutting through the minecraft grass block and revealing that it is a cake L2: realistic clean video of a kitchen knife cutting through the minecraft grass block and cutting it open, with the insides being cake L3: Knife cutting through the mincraft grass block, cutting it open, the inside of the minecraft grass block is cake ## Kling The following are the other tests we did to recreate the "Cakeify It" effect using Kling API. Other "Cakeify It" tests we did using Kling API The following are the prompts used: K1: Kitchen knife cutting open the minecraft grass block, revealing the inside being cake K2: realistic video of a kitchen knife coming down cutting through the minecraft grass block and cutting it open, with the insides revealed to be cake K3: realistic video of a kitchen knife cutting the middle of the minecraft grass block, splitting it in half, the insides of the minecraft grass block is cake ## Explode It ## Luma The following are the other tests we did to recreate the "Explode It" effect using Luma API. Other "Explode It" tests we did using Luma API The following are the prompts used: L1: The joker figurine explodes L2: the joker figurine explodes into tiny pieces all flying away from the explosion L3: the joker figurine breaks apart and explodes into tiny pieces all flying away from the explosion L4: the joker figurine breaks apart and explodes. resulting in tiny pieces of the figurine flying everywhere. The figurine is not seen anymore by the end of the video since it has exploded ## Kling The following are the other tests we did to recreate the "Explode It" effect using Kling API. Other "Explode It" tests we did using Kling API The following are the prompts used: K1: joker figurine exploding K2: the figurine explodes into small pieces K3: static shot of the figurine as it explodes into small pieces K4: static slow-motion shot of the figurine as it explodes into tiny shards that fly away from the explosion ## Squish It ## Luma The following are the other tests we did to recreate the "Squish It" effect using Luma API. Other "Squish It" tests we did using Luma API The following are the prompts used: L1: two hands come in and squish the plushie L2: First person view of a pair of hands coming in and squishing the plushie L3: a pair of hands coming in and squishing the plushie, playing with it as if it was clay L4: a pair of hands coming in and squishing the plushie, the plushie morphs as if it were made out of clay when being squished ## Kling The following are the other tests we did to recreate the "Squish It" effect using Kling API. Other "Explode It" tests we did using Kling API The following are the prompts used: K1: a pair of hands coming in and squishing the plushie K2: a pair of hands coming in and squishing the plushie, playing with it as if it was clay K3: a pair of hands coming in and squishing the plushie, playing with it and squishing it, the plushie being squished into a more sphere like object ## Inflate It ## Luma The following are the other tests we did to recreate the "Inflate It" effect using Luma API. Other "Inflate It" tests we did using Luma API The following are the prompts used: L1: the plushie inflates and floats up like a balloon L2: static camera shot as the plushie bloats and inflates, flying up like a balloon L3: static camera shot as the plushie bloats and inflates, floating up like a balloon ## Kling The following are the other tests we did to recreate the "Inflate It" effect using Kling API. Other "Inflate It" tests we did using Kling API The following are the prompts used: K1: the plushie bloats and inflates, as it flies up like a balloon K2: static camera shot as the plushie bloats and inflates, flying up offscreen like a balloon K3: static camera shot as the plushie bloats and inflate flying up like a balloon K4: static camera shot as the plushie inflates and flies up like a balloon ## Conclusion From looking at the six effect examples above, it's clear that Pika 1.5 easily outperforms both Luma and Kling. But depending on the example, Kling API or Luma API can deliver a similar level of quality, as shown in examples 2 and 5. However, we need to keep in mind that this applies only to these six specific special effects, likely because Pika Labs has optimized their AI for these particular "Pikaffects". That being said, we're eager to see where AI image-to-video technology goes next, and we’re hoping for even more types of fun and visually pleasing special effects like the ones shown above. We hope that you have found this comparison useful! And if you are interested, check out our collection of generative AI APIs from PiAPI ! ## The Motion Brush Feature through Kling API Interested about the newly released Kling Motion Brush Feature? Want to use Motion Brush through Kling API? Check out our comparison blog for more! ## Introduction Kling's Motion Brush is a newly introduced tool, launched by Kuaishou on September 19, 2024, alongside the release of Kling 1.5. Kling's announcement of Kling 1.5's and Kling Motion Brush 's release on their official website PiAPI 's Host-Your-Account users already have access to the Kling 1.5 API . However, the motion brush feature will be available to our API users by around October 8, 2024. In this blog, we’ll dive into what the motion brush tool is and compare the results with and without it to see just how much it enhances the AI's performance. ## What is the Kling Motion Brush Feature? Kling Motion Brush is a new feature in Kling’s image-to-video generative AI, currently available only in Kling 1.0 and not yet supported in Kling 1.5. This tool enables users to set movement paths for specific elements in the video. By brushing over an area manually or using auto-segmentation, you can select the object in the image you want to move, then choose a path to set its movement directions. In addition to setting movement paths, users can also apply the static brush, which stops the selected parts from moving. In theory, this level of control allows users to create more dynamic motion, bringing still images to life with more movement precision. If you are interested in learning how to use the tool more effectively, we recommend checking out Kuaishou's Official Motion Brush User Guide . With this in mind, we'll now examine the image-to-video evaluation framework which serves as the foundation for analyzing the effectiveness of the Kling Motion Brush. ## Image-to-Video Evaluation Framework For this comparison, we have taken the AIGCBench (Artificial Intelligence Generated Content Bench) , an evaluation framework designed for AI-generated Image-to-Video content. Although this is a framework designed to be used by computers, we've adjusted it for human evaluation, adapting it from an automated system into a manual process. Below are the four criteria the framework used, along with explanations for each. Control-Video Alignment We would assess how closely the output video aligns with the provided text prompt and image. This benchmark is essentially the same as the "prompt adherence" metric from our previous blog comparing Luma Dream Machine 1.5 vs 1.0 . Motion Affects We evaluate whether the motion in the video is dynamic, realistic, smooth, and consistent with real-world physics. Temporal Consistency We'll assess whether adjacent frames show high coherence, maintain continuity throughout the video, and remain free of any visible artifacts, distortions, and errors. Video Quality This metric is straightforward - we check if the video has a high resolution and check for any blurring. ## Same Prompt Comparison With the evaluation framework established, let's now outline our comparison method. We will have three comparison examples, all based on popular cultural trends. Within each example, we will compare the output of three different workflows shown below: - Workflow 1 - Kling 1.0 without Motion Brush - Workflow 2 - Kling 1.0 with Motion Brush - Workflow 3 - Kling 1.5 without Motion Brush All workflows within each example will use the same prompt and input image. We will express how we want the object to move with clear textual commands in the prompt. Workflow 1 & 3 will have to rely only on the prompt, whereas Workflow 2 will rely on the prompt and the motion path drawn. This is how we can compare how much improvement the motion brush feature brings. Note, Kuaishou's official guide ( Motion Brush User Guide ) also advises that when using the motion brush feature, the user still should specify the desired movement in the prompt for optimal performance. Our Workflow 2 adheres to this recommended practice. Now that you're familiar with our approach, let's jump right into the examples. ## Example 1: Joker Folie à Deux Because we are very excited about the upcoming release of Joker 2 , thus for this example, we've chosen to revisit the iconic stair scene from the original Joker film. Below is the image that we have found of Joaquin Phoenix's Joker on the stairs, which we'll use with a prompt to generate the videos for three workflows in this example. The image of Joaquin Phoenix's Joker on the stairs that will be used an input to Kling API The screenshot of the motion brush settings that we used for Workflow 2 (the Kling 1.0 model with Motion Brush) is shown as follows. As the green path illustrates, we intend the Joker to turn around and walk up the stairs. The motion brush settings for the image of Joaquin Phoenix's Joker, that will be used as input to Kling API (Workflow 2) And here are the output videos of the three workflows, with the prompt used shown in the description. A comparison of the three workflows of the prompt: "The camera slowly zooms out as Joker turns around, back facing the camera as he walks up the stairs." As discussed in the evaluation framework section, the following is our analysis: Control-Video Alignment All three videos have the Joker walking up the stairs with his back facing the camera. But none show the slow zoom-out specified in the prompt. Motion Effects All three versions have smooth, realistic motion, which are consistent with real-world physics, without any abrupt movements. Temporal Consistency The videos are free of flickering, abrupt transitions, or artifacts, maintaining high frame-to-frame consistency. Although the Joker seems to be turning around a bit slower than usual in the first video. Video Quality While Kling 1.5 provides 1080p, the other two are limited to 720p. None of the videos above show any signs of blurring. Overall, there doesn't seem to be much difference in this example for all four criteria. ## Example 2: NFL Wallpaper The NFL season just kicked off, and we’re having so much fun watching it that we decided to create animated NFL wallpapers for this example. Below is an image generated using Midjourney API of the Dallas Cowboy Wallpaper, which we'll use with a prompt to generate the videos for three workflows in this example. Prompt: "A Dallas Cowboys NFL player in a blue background with paint effects moving, phone wallpaper, inspiring." The screenshot below shows the motion brush settings we used for workflow 2 (the Kling 1.0 model with Motion Brush). We've used the static brush to keep the NFL player in the center stationary. The motion brush settings for the image of the Dallas Cowboys Wallpaper, that will be used as input into Kling API (Workflow 2) And here are the video outputs for the three workflows. A comparison of the three workflows of the prompt: "Cinematic loop of an NFL player in focus, stationary in vibrant blue background. Swirling effects move dynamically behind him. Camera is stationary and static." As discussed in the evaluation framework section, the following is our analysis: Control-Video Alignment Workflow 2 (Kling 1.0 w/ Motion Brush) delivers the best Control-Video Alignment, with the NFL player staying completely still and the background creating the most convincing loop. Workflow 1 (Kling 1.0 w/o Motion Brush) also performed well. But if you look closely, the NFL player has slight movements, and the background loop isn’t as smooth as in Workflow 2. Meanwhile, Workflow 3 (Kling 1.5 w/o Motion Brush) has poor alignment, as the NFL player’s visible movement prevents a seamless loop when used as a wallpaper. Motion Effects All three versions have realistic and smooth motion, with no unnatural movements visible. Temporal Consistency None of the three videos show flickering, abrupt transitions, or artifacts, maintaining high frame-to-frame coherence. Video Quality Kling 1.5 outputs in 1080p, unlike the other two, which are restricted to 720p. None of the videos display any blurring. Overall, for creating animated wallpapers, using motion brush seems like the clear choice since it lets you control which parts remain static, resulting in a more seamless loop. ## Example 3: Sonic the Hedgehog 3 As Sonic 3 races toward theaters, we picked a scene from the original movie as an example. Being longtime Sonic fans, we felt this was the perfect time to showcase him! Below is an image that we have found of Sonic, which we'll use with a prompt to generate the videos for three workflows in this example. The image of Sonic the Hedgehog that will be used as the input to Kling API The screenshot below shows the motion brush settings that we used for Workflow 2 (the Kling 1.0 model with Motion Brush). As the green path illustrates, we intend Sonic to walk offscreen to the right. The motion brush settings for the image of Sonic which be used as input into Kling API (Workflow 2) And here are the output videos for the three workflows. A comparison of the three workflows of the prompt: "Sonic walks offscreen to the right" As discussed in the evaluation framework section, the following is our analysis: Control-Video Alignment Both Workflow 1 (Kling 1.0 w/o Motion Brush) and Workflow 2 (Kling 1.0 with Motion Brush) show strong Control-Video Alignment, as Sonic walks offscreen to the right in both cases. In contrast, Workflow 3 (Kling 1.5 w/o Motion Brush) shows the lowest level of Control-Video Alignment, with Sonic disappearing offscreen instead of walking off. Motion Effects Workflow 2 (Kling 1.0 with Motion Brush) performs best in this criterion, as Sonic walks offscreen naturally. In Workflow 3 (Kling 1.5 w/o Motion Brush), Sonic’s movements are natural, but he disappears rather than walking away. However, in Workflow 1 (Kling 1.0 w/o Motion Brush), Sonic’s legs shorten, and his body morphs unnaturally before walking offscreen. Temporal Consistency Workflows 1 and 2 maintain strong temporal consistency without any abrupt disruptions. However, Workflow 3 contains a sudden transition where Sonic disappears, and a red car takes his place. Video Quality Kling 1.5 provides 1080p resolution, compared to the other two restricted to 720p, and all videos are free from blurring. In this example, Workflow 2 (Kling 1.0 with Motion Brush) has the best output, with Sonic’s movements remaining fluid and natural, and avoiding distortion or disappearance seen in other workflows. ## Conclusion Based on the three examples provided above, it's clear that using Motion Brush with Kling 1.0 offers the best control over image elements, outperforming other options in Control-Video Alignment, Motion Effects, and Temporal Consistency. However, Kling 1.5 still holds a slight advantage in video quality. But this isn't always the case, as the first example shows, the difference in output quality can sometimes be minimal. We are excited to see how Kling's Motion Brush tool will evolve in the future. Even in its initial version, this tool provides powerful, precise control over image elements for image-to-video AI generation. We can't wait to see how it will turn out once the motion brush tool is added to Kling 1.5! We hope that you found our comparison useful! And if you are interested, check out our collection of generative AI APIs from PiAPI ! ## Using Kling API & Midjourney API to Create an Animated Custom Wallpaper Using Kling API and Midjourney API to create visually stunning wallpapers! For example an anime wallpaper, a wallpaper of the character Yoshii Toragana from Shogun, and a Warhammer 40k Wallpaper. ## Introduction As generative AI technology advances, it has never been easier to create your own custom AI Wallpaper, but what if you could take it a step further and make it an animated live wallpaper? Because now you can! By pairing our Kling API and our Midjourney API , you can first generate high-quality wallpaper according to your preferences, and then bring it to life with dynamic animation. In this blog, we will explore a few examples of combining these two APIs, creating some impressive animated wallpapers. ## Example 1: Anime Wallpaper Let’s begin by creating an anime desktop wallpaper with the help of our AI APIs. In terms of workflows, we will be testing three different ones for this example, to help us better understand the steps involved and the respective output qualities. The exact same identical Kling prompt will be used across all three workflows so that we have the same reference for comparison. ## Workflow 1: Midjourney to Kling Below is the AI Anime image that we have generated using Midjourney API, with the prompt in its description (all the prompts used in this blog are edited by GPT). Prompt: "Anime wallpaper of a girl with her back to the camera, long flowing hair swaying gently in the breeze, gazing up at a vast, starry night sky filled with bright constellations and a glowing full moon, with soft clouds and a serene atmosphere" Now that we have the image generated, we will insert that image alongside a new but similar prompt into Kling API, as shown below: Prompt: "Anime girl with long flowing hair stands with her hair back to the camera, her hair gently swaying in the breeze. she gazes up at an anime-starry sky and a glowing full moon. Soft clouds float, creating a serene, calm, night. Shot from a low-angle view with ambient moonlight and soft shadows for a tranquil atmosphere." ## Workflow 2: Online image to Kling In the second workflow, we found an image online that closely matches our Midjourney prompt, an anime girl with her back to the camera, gazing at the sky. An image of an anime girl looking at the sky with her back turned to the camera to be used as an input to Kling Then, we will use this image alongside the same prompt we used in Workflow 1 to generate the video by using Kling API, with the output shown below. Prompt: Same prompt as Workflow 1 ## Workflow 3: Just using Kling For the final workflow, we will not be using any images as an input to Kling, but only the same prompt into Kling API as we did for Workflow 1, and below is the result. Prompt: same prompt as Workflow 1 ## Takeaways After reviewing the results, we believe that the output from Workflow 1 (Midjourney to Kling) delivers the clearest and most detailed live anime wallpaper, with the added benefit of the initial wallpaper being customizable according to your preferences. While Workflow 2 (Image to Kling) produces a good output, finding an image online that meets your exact preferences takes quite some time. Finally in Workflow 3 (Just Using Klng), the video resolution is lower compared to Workflow 1. This difference likely comes from Midjourney's high-quality output, which helped the Kling model to deliver better video output. Thus, if you want the highest quality results, we recommend using Kling API alongside an image from Midjourney. ## Example 2: Shogun's Yoshii Toragana For the second example, we're going to switch things up a bit by creating a phone wallpaper. This wallpaper is inspired by the character of Yoshii Toragana played by Toshiro Mifune from the critically acclaimed show Shogun. We’re eagerly anticipating the release of Shogun Season 2 to see the story of John Blackthorne, Yoshii Toragana, and Lady Mariko continue to unfold. In the meantime, we've created this wallpaper to make the wait a little more bearable. Our first step is using the Midjourney API to generate a wallpaper image of Yoshii Toragana; you can see below for the output image and the prompt. Prompt: "Toshiro Mifune as a samurai in golden armor, riding a white horse through swirling red smoke and teal mist. His hands grip the reins calmly as his iconic expression radiates strength. The dynamic colors and moody lighting create a powerful vertical phone wallpaper, highly detailed, cinematic style" Next, we entered the generated image into the Kling API using a different prompt. Below, you can see the resulting GIF from Kling alongside the prompt used. I personally like the rising, animated smoky background behind Yoshii Toragana, very fitting of the Shogun title. Prompt: "Static camera shot, the samurai and horse remain still, with only the horse’s mane and the samurai’s hair swaying gently in the wind. The red mist in the background moves slowly, creating a subtle, seamless loop for a live wallpaper. Calm yet powerful atmosphere" ## Example 3: Warhammer 40k Wallpaper In our final example, we present none other than Demetrian Titus, captain of the Ultramarines, a central figure from the Warhammer 40k universe. This wallpaper draws inspiration from the latest installment in the franchise, Warhammer Space Marine 2 , a highly anticipated game that brings to life the intense battles and rich lore within the Warhammer universe. As with the previous examples, we will need to generate a picture using Midjourney API first. Prompt: "A Warhammer 40K space marine, wearing ultramarine armor, in an action pose with his bolter raised forward. The alien desert is bathed in golden twilight with rocky spires and an ominous red moon rising in the background. Dust swirls in the wind as the marine stands his ground, illuminated by a bright flare. Cinematic lighting, vivid detail, epic composition, concept art style, hd quality, natural shading, inspired by sci-fi landscapes, dramatic contrast" Next, the generated image will be put into Kling API, along with the prompt shown below the GIF. We are particularly drawn to the gritty and sci-fi aesthetic of the video. Prompt: "Static camera shot, Space Marine in place not moving, unmoved in the desert as soft gusts of wind push his cape to the left. Dust swirls around his feet, and the distant red planet glows. A serene, yet eerie desert landscape, perfect for a live wallpaper." ## Conclusion From the examples shown above, you can probably see why combining our Kling API with our Midjourney API is the best choice for creating your very own animated custom wallpaper. The combination of Midjourney images and Kling's animation creates highly detailed results, With Midjourney providing images of high-quality output for Kling to animate. Also, prompts that create animation without drastically different starting/ending frames to achieve a more natural loop really helped with creating these animated wallpapers. We hope you've found value in our experiment and encourage you to try out some of your own ideas. If you're interested in our other AI APIs , feel free to check them out！ ## Kling 1.5 vs 1.0 - A Comparison through Kling API A detailed comparison between Kling 1.5 and it's predecessor Kling 1.0, using Kling API to generate the videos compared. The wait is finally over! On September 19th 2024, Kuaishou , the team behind one of the most advanced text/image-to-video generative AI models currently on the market, has just released its new model, Kling 1.5, making the announcement on their official website. Thus, we at PiAPI are working to bring the Kling 1.5 API to our users, and we are actively comparing the differences between version 1.0 and 1.5, sharing results with you in this blog. ## Kling 1.5 Release Official announcement of Kling 1.5's release on their official website With the new 1.5 version update, Kling promises improvements in image quality, dynamic quality, and prompt relevance. Kling has also added a new motion brush feature, where you can precisely define the movement of any element in your image, giving you unparalleled control over the motion and performance of your videos. Kling's internal tests boast a 95% performance increase, but we at PiAPI have done our own comparisons, with the evaluation framework use and the subsequent results shown in the blog. ## Evaluation Framework Regarding the evaluation framework used for this comparison, we have taken the comprehensive text-to-video evaluation framework from Labelbox , while adding "Text Adherence" into the mix since judging from our user feedback, text adherence is an important aspect of generative video for it to become more prevalent as a productivity tool than something just used for entertainment. This is the same framework we had used in our previous blog comparing Luma Dream Machines 1.0 vs 1.5 , namely: - 1. Prompt Adherence - 2. Text Adherence - 3. Video Realism - 4. Artifacts For what each of these categories mean and how they could be rated, feel free to read our previous blog for more detail. ## Kling 1.0 and Kling 1.5 Comparison And now, let's see how the previous Kling 1.0 model fares against the new Kling 1.5 model. ## Example 1: MLB Detroit Tigers Player Hitting a Baseball with a Baseball Bat Prompt: "An MLB baseball player hitting a baseball (ball) with a baseball bat, with the words "Detroit Tigers" writen on his shirt" Prompt Adherence The video generated by Kling 1.0 includes most of the elements from the prompt: the MLB player, the baseball bat, and the baseball, but it falls short of capturing the core action: the baseball player hitting the ball with his bat. Meanwhile, the video generated by Kling 1.5 contains all the elements, including the one missing from Kling 1.0, though the motion appears somewhat unnatural. Text Adherence Both outputs have poor text adherence, as neither video has the words "Detroit Tigers" or anything close to it in text. Video Realism Both videos show impressive realism, with shadows beneath the MLB players' caps following the head movements. However, the video generated by Kling 1.5 stands out more given its higher definition. Artifacts Both outputs display noticeable artifacts. In the video generated by Kling 1.0, a baseball player holds a ball at first, but it quickly morphs into a bat. While in the Kling 1.5 video, the baseball’s movement toward the player and its contact with the bat feel noticeably unnatural. The final artifact that both players are wearing New York Yankees caps whereas Detroit Tigers were specified in the prompt, is present in both videos. We believe this error is present because "the New York Yankees is the most popular Major League Baseball franchise" , therefore it is likely that it dominated the baseball-related training data that Kling used. Overall, we see only a slight improvement from the video generated by Kling 1.5. ## Example 2: AI Cat Playing a Red Electric Guitar in the Forest prompt: "A cat playing a red electric guitar in the forest" Prompt Adherence Both videos display all the elements described in the prompt: a cat playing a red electric guitar, and the forest surrounding it. Video Realism The level of realism displayed in video generated by Kling 1.5 is very high due to the cat's dynamic movements, especially the hand and head movements. The forest's reflection on its electric guitar is also of higher definition. Artifacts The video generated by Kling 1.0 contains a significant error. The left arm, intended to be the cat's paw, bears more of a resemblance to that of a human hand, with peach skin clearly visible. Meanwhile, in the video generated by Kling 1.5, the cat’s left hand has human-like fingers, but the black fur conceals this detail, making it far less noticeable than the skin-toned features seen in the other video. Overall, we see a moderate improvement from the video generated by Kling 1.5. ## Example 3: A Cartoon Monkey and Cartoon Dog Hugging Each Other Prompt: "A cartoon monkey and a cartoon dog hugging beneath a large tree" Prompt Adherence Both videos display the large tree, but only the Kling 1.5 video displays both the cartoon monkey and the cartoon dog hugging, whereas Kling 1.0 shows two animal bodies embracing but with just one head. Both bodies in the 1.0 video have monkey-like features, such as white hands with fingers, with no sign of dog-like paws. Video Realism Both versions show the core elements of the cartoon style, though the animation styles are different. This is likely caused by the lack of specificity in the prompt. But one thing of note is that the video generated by Kling 1.5 is more dynamic, as evident in the monkey's changing expressions and the dog's wagging tail. Artifacts The video from Kling 1.0 was full of artifacts: the unnatural distortion in both the characters, the absent dog, and the vanishing hands. While in the video made by Kling 1.5 showed no such flaws. For this prompt, it's evident that the video output produced by Kling 1.5 marks a significant improvement over its predecessor. ## Example 4: Earth with a Moon and a Mini Moon Prompt: "The earth, with 2 moons rotating around it" Prompt Adherence Both versions captured the prompt descriptions. Although the version generated by Kling 1.5 has a more accurate depiction of the Earth and its moons. Video Realism Both videos display an impressive amount of realism, but the version produced by Kling 1.5 provides a more convincing portrayal. It shows more dynamic camera movements, shifting shadows, and more realism, especially as the Earth's shadow slowly covers the second moon. Artifacts Kling 1.0's video output presents a few noticeable artifacts: Earth's landmasses are fully white, and the moons having mismatched colors, with one being orange and the second moon having differently colored terrain. Meanwhile, the output from Kling 1.5 has no artifacts. In fact, it even has a shockingly accurate depiction of Earth's geography - the African continent has the right amount of greenery in the middle with deserts in both the northern and southern parts. Overall, we see a general improvement in Kling 1.5's video output. ## Example 5: Messi, Wearing a Shirt with the words "Champions League", Kicking a Soccer Ball into a Goal Post Prompt: "Messi kicking a soccer ball into a goal post, with the words "Champions League" written on his shirt" Prompt Adherence Both outputs show poor prompt adherence, but Kling 1.5 performs slightly better than Kling 1.0. While both models generate an image of a soccer player, neither one resembles Lionel Messi. Kling 1.0's output doesn't have a soccer ball, a goal post, nor the visible action of Messi kicking the ball into a goal post. On the other hand, Kling 1.5's output does show a goalpost and a kicking motion, but the soccer ball itself is still missing Text Adherence Both videos display low text adherence, with neither video having the words "Champions League" or anything resembling the writing on their shirts. Video Realism Though both videos are realistic, Kling 1.5 outperforms its predecessor by a long shot. This is because Kling 1.0 merely produced a zoom-out of a static image, while Kling 1.5 renders Messi kicking a ball, with his hair swaying and his shirt subtly shifting with the motion. Artifacts The background of Kling 1.0's output is heavily blurred, especially when compared to the output of Kling 1.5. ## Conclusion Based on the various examples provided above, it is evident that both models fall short in the text adherence category within the examples provided, frequently producing incoherent and inaccurate text. However, it is clear that Kling 1.5 surpasses Kling 1.0 in terms of overall quality, prompt adherence, and video realism. With that being said, we don't see the "95% increase in performance" that Kuaishou claimed. We hope you found this comparison blog useful! If interested, please also check out our other generative AI APIs from PiAPI! ## Luma API & Midjourney API - Exploring the Marvel Multiverse Using Dream Machine API and Midjourney API we explored what famous marvel characters would look like if they were played by different actors. Such as a Tom Cruise Iron Man, a Henry Cavill Wolverine, and finally RDJ Doom! ## Introduction Ever wonder how to breathe new life into your Midjourney creations? By pairing our Midjourney API with our Luma's Dream Machine API , you can now immediately turn your AI generated images into animated videos! In this blog, we will explore some examples of combining these two AI APIs in action, creating dynamic videos as per the most popular trends! ## Example 1: Tom Cruise Iron Man In our first example, we’ll explore a fan-favorite scenario: the Iron Man character portrayed by Tom Cruise - and we will be using the AI APIs to generate a short scene of this fictional character. In terms of workflows, we will be testing three different workflows for this example, to help us better understand the steps involved, and the respective output qualities. ## Workflow 1: Midjourney to Luma For this workflow, we will first generate the image for this fictional character using Midjourney API, and then we will use that image along with another prompt to generate the video using the Luma API. Below is the image that we have generated using Midjourney API, with its prompt in its description (all the prompts used in this blog are edited by GPT). The image generated by Midjourney API will then be put into Luma API Prompt: "Ultra-realistic, high-definition portrait of Tom Cruise wearing a custom-designed Iron Man suit, with intricate metallic textures and advanced futuristic technology. The suit has a sleek, streamlined design with glowing blue energy sources. Tom Cruise is standing in a heroic pose, in a futuristic cityscape at dusk, with neon lights reflecting off the suit. The atmosphere is cinematic, with a dynamic blend of realism and sci-fi. Hyper-detailed face, showing intensity and determination, with perfect lighting and shadows to enhance realism." Now that we have the image generated by Midjourney API, we will be inserting that image alongside a new prompt into Dream Machine API, and below is the video generated. Prompt: "Tom Cruise looks away from the camera slowly, as the wind gently blows his hair. The blue energy sources on his chest and hands glow and flicker lightly. The camera movement is panning, panning from left to right, showing the futuristic city lights reflecting off his metallic suit" ## Workflow 2: Real Image to Luma For the second workflow, we have found a real image of Tom Cruise on the internet (see below), and we will use it along with a new prompt to generate the video using the Dream Machine API. An image of Tom Cruise found online to be used as an input to Luma For the prompt in this workflow, we will have to specify the "man in a red and yellow Iron Man suit" because the Tom Cruise image that we found obviously doesn't have the Iron Man suit. In contrast, Workflow 1's image includes an Iron Man suit since the image is generated by Midjourney, thus the omission in its prompt. Below is the video generated using the prompt (shown under the video GIF) and the input image found online. Prompt: "Tom Cruise in a red and yellow Iron Man suit looks away from the camera slowly, as the wind gently blows his hair. The blue energy sources on his chest and hands glow and flicker lightly. The camera movement is panning, panning from left to right, showing the futuristic city lights reflecting off his metallic suit" ## Workflow 3: Just using Luma And for the final workflow, we will not use any image as an input to Luma, but only use a relevant prompt to generate the video using the API. The main difference between the prompts for this workflow and Workflow 1 is that in Workflow 1, the image already features Tom Cruise in an Iron Man suit, so we don't need to specify the "Iron Man suit" part. Whereas this workflow has no initial image, thus needing the specification in the prompt. And below is the video generated by Luma API, using only the prompt under the GIF. Prompt: "Tom Cruise in a red and yellow iron man suit looking into the camera but then looks away from the camera slowly, as the wind gently blows his hair. The blue energy sources on his chest and hands glow and flicker lightly. The camera movement is panning, panning from left to right, showing the futuristic city lights reflecting off his metallic suit" ## Takeaways By comparing the results above, we think that the result from Workflow 1 (Midjourney to Luma) is the most realistic and detailed output. In Workflow 2 (Real image to Luma), we refined the prompt to specify Tom Cruise wearing a red and yellow Iron Man suit, as he obviously wasn't wearing one in the real image. However, despite the prompt, the final result did not include the suit. In Workflow 3 (Just using Luma), we also refined the prompt to specify Tom Cruise wearing a red and yellow Iron Man suit, as no initial image was used. The result is not as high resolution as that from Workflow 1. We think this is because the high quality image from Midjourney "primed" the Luma model to generate higher-quality video. Thus, if you want the best possible output, we recommend you to use Luma Dream Machine API in conjunction with the image from Midjourney. ## Example 2: Henry Cavill Wolverine In the second example, we’ll revisit a concept fans of the Deadpool and Wolverine movie may be familiar with, the fictional character Wolverine played by Henry Cavill. Although many may be disappointed by his brief cameo in the movie, Luma API and Midjourney API will allow us to explore an alternate reality where Henry Cavill took on the iconic role of Wolverine, instead of Hugh Jackman. First, we used Midjourney API to generate an image of Henry Cavill Wolverine; you can see below for the output image and the prompt. Prompt: "Ultra-realistic, highly detailed image of Henry Cavill as Wolverine, wearing the classic black and yellow X-Men suit with rugged textures. He has a muscular, bulky physique, thick sideburns, and sharp adamantium claws, three on each hand, extended from his knuckles. His hair is messy but styled, with intense, brooding eyes. The setting is a dark, misty forest at night, with moonlight subtly illuminating his face, adding a dramatic shadow effect. Wolverine is in a dynamic combat-ready pose, ready to strike. The atmosphere is tense and gritty, with dirt and scratches on his costume, emphasizing a recent battle." Then, we input the image generated into Luma API with a new prompt. You can see below for the output GIF from Luma and the prompt used. The output is quite dynamic as you can see. Prompt:"A muscular man in a yellow and black suit, standing in a fighting stance with sharp claws, breathes heavily, his body subtly moving up and down with each breath. The camera slowly zooms in while still focusing on the man, while mist and light flickers in the background enhance the intensity" ## Example 3 Robert Downey Jr Dr Doom In the final example, we'll explore the highly anticipated concept of the fictional character Dr. Doom, portrayed by Robert Downey Jr (RDJ). RDJ Doom takes a new direction for the Marvel universe, and by using the APIs, you can visualize this concept before Marvel releases its first glimpse of Robert Downey Jr. as Dr. Doom. As with the previous examples, we will first need to generate an image of RDJ Doom using Midjourney API with the prompt provided under the output image. Prompt: "Ultra-realistic portrait of Robert Downey Jr. as Doctor Doom from Marvel Comics, without a mask, wearing Doctor Doom’s iconic green cloak and metallic armor. His hood is up, covering part of his head but still revealing his intense and serious expression. The hood casts subtle shadows on his face, giving him a mysterious and powerful aura. The armor is intricately detailed, with visible wear and tear, suggesting battles fought. The background is dark and ominous, featuring mystical and technological elements, with faint glowing machinery. The lighting should create dramatic shadows, highlighting both his face and the folds of his cloak." The image generated will then be used in the Luma API, along with a prompt shown below the output GIF. We personally like the intense look on the character and the clockwork details as the camera pans to the left. Prompt: "A man with an intense gaze, his green hood swaying gently in the wind as the camera slowly circles around him, maintaining focus on his sharp expression and face" ## Conclusion Now it's easy to see why combining our Luma Dream Machine API with our Midjourney API is the superior choice for great animated visuals. The level of detail achieved by feeding Midjourney images into Luma is quite high, and Luma does a very good job with animating the input, resulting in relatively good-quality output videos. We hope you've enjoyed our little experiment and can try a few other ideas yourself! If you're interested in our other AI APIs , feel free to check them out！ Happy creating! ## Midjourney API's Auto-CAPTCHA-Solver An article introducing PiAPI's Auto-CAPTCHA-Solver to streamline workflow and improve efficiency ## What is Midjourney's CAPTCHA feature? Around June 2024, Midjourney introduced the CAPTCHA feature for users of their Discord bot to generate images. Being a security feature, Midjourney has not announced or released any public information regarding its implementation, thus we at PiAPI aren't sure about the exact launch date of this feature. After doing some brief research, it seems that others have posted about the CAPTCHA feature on X as early as May 2023. A user's tweet on X about Midjourney's CAPTCHA security feature back in 2023 May. So what is Midjourney's CAPTCHA feature? CAPTCHA , which stands for "Completely Automated Public Turing test to tell Computers and Humans Apart", is a type of security measure known as challenge-response authentication. Essentially, it is a small test or puzzle that websites and apps use to check if you're a real person rather than a robot. It might ask you to click on certain images, type in some letters, or solve a simple puzzle. If you have spent time online, you more than likely have done one before. Thus, why did Midjourney introduce this feature? Most likely, Midjourney introduced this feature to deter bot abuse and prevent automation attempts. However, CAPTCHAs have some problems. Firstly, it negatively impacts user experience. CAPTCHAs sometimes has unclear or distorted text for human readability, which often results in multiple failed attempts, causing frustration among users. Also, CAPTCHAs obviously impose extra manual work for users, causing workflow disruptions. Thus, it is not a surprise that 77% of IT and security leaders agree that eliminating CAPTCHA would greatly enhance the user experience . Secondly, CAPTCHAs fail at their core purpose because AI and bots now outperform humans in solving even the most challenging ones , rendering them ineffective. Naturally, developers using Midjourney API would also be negatively affected by CAPTCHAs. When developers discover a CAPTCHA has shown up, it already means that their website/app service is being disrupted, and they'd have to manually solve the CAPTCHA as well. Therefore, is there a better way to deal with this issue? PiAPI's Midjourney API has the solution! ## PiAPI's Midjourney API Auto-CAPTCHA Feature We at PiAPI have taken the initiative to develop and test our auto-CAPTCHA-solving service, and it consists of the following steps: ## Step 1 When a CAPTCHA is triggered within your own Midjourney account(s), PiAPI will automatically detect this, and suspend this account as shown below: The connected Midjourney account being suspended due to CAPTCHA popping up in PiAPI's Workspace ## Step 2 To get notified that your Midjourney account(s) is suspended: - 1. For Free Plan Users ($0/month) , you would have to manually go to PiAPI's Midjourney Workspace page and check its status; - 2. For Creator Plan users ($8/month) and Pro plan users ($50/month) , you can set up an Account Notification Webhook to automatically receive updates about your suspended account(s). The Account notifications section on PiAPI's Workspace page ## Step 3 - 1. For Free Plan users ($0/month) and Creator Plan users ($8/month) - a. If it has been within 30 minutes since PiAPI first detected that the CAPTCHA was triggered on your account. You can go to PiAPI's Midjourney Workspace page, click on "Get CAPTCHA link", solve the CAPTCHA manually, and click on "reactivate" for that particular connected account. - b. If 30 minutes have passed since PIAPI first detected that CAPTCHA has been triggered on your account. You will have to go to that particular Midjourney's account on Discord to solve the CAPTCHA manually (you need to send a new task first to trigger the CAPTCHA again - unfortunately, this might increase the risk of banning). Then, go back to PiAPI's Midjourney Workspace page and click on "Reactivate" for that particular connected account. Note: If you've solved the CAPTCHA, clicked on "reactivate", and the account still does not work - just delete the account and re-connect it again. - 2. For Pro Plan users ($50/month) , PiAPI offers our Automated CAPTCHA Solver to help users automatically solve the Midjourney CAPTCHA. All you need to do is simply turn on the switch and that is it! PiAPI's Midjourney Workspace Page showing the Automated CAPTCHA Solver ## Conclusion The difficulties for developers caused by Midjourney's CAPTCHA feature are pretty obvious and painful, from frequent disruption to inefficiencies in their operation. We at PiAPI are committed to improving your experience with our Auto-CAPTCHA-Solver feature, designed to overcome these hurdles effortlessly. We hope you'll like this feature that we worked on! Please check out our other AI APIs if you are interested! Happy building! ## Luma's Dream Machine 1.6 - Testing Camera Motion Feature through Luma API! Introducing the new Camera Motion Feature from Luma's Dream Machine 1.6 ! On September 4th 2024, Luma officially announced the release of Dream Machine 1.6 and launching the highly anticipated feature Camera Motion . This new feature could elevate the quality of generated videos, allowing users to control camera movements through simple text prompts. In this blog, let's try testing this new feature through PiAPI's Luma API (and yes - our API already supports this feature!) and see how it actually performs using different prompts! Luma's announcement on X about Dream Machine 1.6 ## What’s New in Dream Machine 1.6 From the introduction of its first version in June 2024, Luma has continued to enhance the Dream Machine mdoel, rolling out version 1.5 in August with features like keyframe creation and improved text-to-video quality. With Dream Machine 1.6, Luma has added Camera Motion , giving users control over various camera movements simply by typing commands into the prompt box. This feature includes 12 camera movements such as panning left or right, pushing in or pulling back, orbiting, and even vertical movements like ascents and descents. By typing "Camera" in your prompt box, you can trigger and choose from these preset camera motions, making your videos feel more polished and cinematic. Typing "camera" into prompt and triggering the available camera movements ## Testing the New Camera Motion Feature through API And to test this new feature, we will generate three videos using the different styled prompts through our API! ## Example 1 Prompt: "A leopard strolls leisurely through a gently falling snowy landscape, its movements slow and graceful. The animal occasionally lifts its paw or playfully snaps at the snowflakes drifting down around it. Soft, warm light bathes the scene." The video without camera motion looks lacking depth, as the camera remained static throughout the scene. While the visuals were pretty good, the shot felt somewhat flat and passive. On the other hand, the video with camera motion is more immersive. The camera gradually zoomed in on the leopard, creating a sense of motion and drawing the viewer into the scene. As the camera pushed closer, the details of the leopard’s movements became more pronounced, adding more engagement. With that being said, we are sure there might be some users who would prefer the without camera motion version. ## Example 2 Prompt: "A woman holds a bright red umbrella in the heavy rain, her clothes soaking wet and her hair plastered to her back as she walks along a dimly lit narrow street." The video generated with the prompt without camera movement captures the stormy mood well, but there is some limitations on the overall engagement. While the scene is visually appealing, the absence of camera movement makes the sequence kind of dull. When we add camera motion (in this case we used "move up"), the output video has a a bit more dynamic feel, but one could argue however the upward pan is not very noticeable. ## Example 3 Prompt: "The woman in a peaked cap is smiling and talking, natural movements, charming eyes, and slow movements." The video generated by the aforementioned prompt without camera movement captures the charming women well. Despite without any movement, the overall shot retains relatively high quality. For the video with camera movement, the output already puts the women on the right side of the shot at the beginning of the sequence. Thus with the camera moving left command that we used, the woman quickly disappears from the shot and it instead shows a blurry man in the background, which is not what our prompt is asking. Thus this is a relatively important lesson: with the addition of the camera movement feature, the user should really think about the original position of the main objects in the scene and the underlying background of the scene so that undesirable details do not appear in the moving footage. ## Conclusion As we can see, the Camera Motion feature in Dream Machine 1.6 could be game-changer for video creators, as it can enhance the visual impact of their project. However, we can also see that adding camera movement does not always improve the quality of the video; the moving camera would inevitably add an additional layer of complexity to the scene that the users need to really think about before giving instructions to the model. With that said, we remain excited to see what’s next for Dream Machine as Luma continues to push the boundaries. If you are interested in our AI APIs , feel free to check them out! Happy creating! ## Luma Dream Machine vs Kling - A brief comparsion using APIs Comparing the same-prompt-outputs of Luma's Dream Machine and Kuaishou's Kling, results are generated using their respective APIs ## Introduction As OpenAI's text-to-video model Sora rapidly gained popularity after the release of its several high-definition previews on Feb 15th 2024, the AI ecosystem was eagerly waiting for months for its launch to experiment with this newest text-to-video technology but they were of no avail. However, on June 12th, Luma's Dream Machine was launched for public use. And two days prior, on June 10th, the Chinese short video platform Kuaishou released their own text-to-video model Kling . Both product launches were able to make significant dents in the text-to-video generation space, with developers, tech enthusiast and content creator quickly utilizing the tools as part of their workflow. Thus, PiAPI as the API provider for both Luma's Dream Machine and Kling , would like to briefly compare the two models and share as a reference for our users. ## Evaluation Framework Regarding the evaluation framework used for this comparison, we'd like to adopt the same framework as we had used in our previous blog comparing Luma Dream Machines 1.0 vs 1.5 , namely: - Prompt Adherence - Text Adherence - Video Realism - Artifacts For what each of these aspects mean and how they could be rated, feel free to read our previous blog for more detail. ## Same Prompt Comparison To effectively compare the outputs of these two models, we have decided to use the same prompt for both models. The prompts are submitted to Dream Machine and Kling using our Dream Machine API (or Luma API) , and our Kling API and the returned results are provided along with their analysis, as shown below. Prompt: On a warm summer afternoon with sunlight shining on the beach, the sea meets the sky in the far distance, gentle waves lap against the shore, and a beautiful little girl, about five or six years old, is playing on the beach. Her golden curls glisten, and her face beams with smile. She wears a pink dress, its hem gently fluttering in the breeze, and small beach sandals, her toes occasionally burying into the soft sand. With a tiny plastic shovel in her hand, she is trying to build a sandcastle. There are seagulls circle in the sky, and a few white sailboats in the distance. Prompt Adherence Both models shows important elements described in the prompt: an afternoon, sunlight, sandy beach, ocean, the litter girl with blonde hair and pink dress, and the boats in the far distance. The Luma model was able to get the seagull and sandal details well; whereas the Kling model was able to get the "toe buried in the sand" part well. The Luma model wasn't able to get shovel and the sand castle details where Kling depicted them well. Video Realism The Luma Model offered a slightly different visual effect, perhaps a bit too ideal and too unblemished. Kling's output on the other hand resembles the real-world more closely, with natural lighting and proportions. Video Resolution The Luma Model's resolution is high, with details like the texture of the hair, clothing, and the sparkle of the sea clearly visible, but the overall effect appears somewhat smooth due to the artistic processing. Kling's output also has high resolution, with details such as the sand, waves, and the girl’s hair strands appearing clear and natural, consistent with real-world resolution. Artifacts The depiction of the little girl's knees and legs are quite off in Luma's output, perhaps it is due to there is very little training data on that particular w-sitting position. And since Kling's output has the little girl standing up, one could make the assumption that Kling would have a similarly difficult time portraying the sitting posture accurately and realistically. Prompts: On a night in a modern metropolis, the camera overlooks from high above, outlining the city skyline with blinking lights. Then, the camera slowly descends, focusing on bustling streets. The neon lights shine brilliantly in colors of red, blue, green, and purple, illuminating the entire block. Pedestrians hustle along the sidewalks, some pausing to gaze at shop windows or check their phones. Vehicles passes by on the streets, their headlights flickering in the night. Prompt Adherence Both models captured most of the prompt descriptions: the night city, the neon lights, the overviewing camera angle, busy streets with people and cars. However, both models missed the slowly descending camera view part, perhaps it was due to the usage of the word "slowly". Video Realism Both videos showed a decent level of realism with the night lighting. The city skyline portrayed in the Luma video seems a bit whimiscal. Video Resolution Both videos had a relatively good quality of resolution. Artifacts The video from Luma shows some unnatural transitions between the lights in the background and those on the streets, particularly around the edges of buildings, where there is slight distortion and blurring. For Kling's video, the way that the vehicles shrinks in size and rapidly disappears as they approach the end of the street look very unnatural. ## Conclusion So above are the two examples of both text-to-video generation model processing complex, dynamic scenes with multiple details. It is up to readers like you to decide which model fared better, our team at PiAPI simply proposes a framework to evaluate the outputs and provide real test outputs for you :) We hope you found this comparison blog useful! If you are interested, please also check out PiAPI for our other generative AI APIs! ## Luma Dream Machine 1.5 vs 1.0 - Comparison through Luma API A detailed comparison between Luma's Dream Machine version 1.5 compared to previous version, using the Luma API to generate videos compared Hi everyone! On August 20th 2024, Luma - the team behind one of the best text/image-to-video generative AI model currently on the market, announced on X that their new 1.5 version is now available for the public to try! ## Luma Dream Machine 1.5 Release Luma's announcement of Dream Machine 1.5's release on X.com With the new 1.5 version update, Luma promises better overall quality, better prompt adherence, and more accurate custom text rendering in the generated videos. Unfortunately, Luma did not provide a choice to select previous model version if one were to currently use their platform to generate videos. Thus, the difference between the former model and the new v1.5 model would be hard to evaluate. However, given PiAPI's position as the market leading generative AI API provider, which includes Luma API (or Dream Machine API) as well, we are able to perform this comparison as we have to test our own products extensively before releasing them to the market (and continuously after the initial release). ## Text-to-Video Evaluation Framework For this comparison, we have the taken the comprehensive text-to-video evaluation framework from Labelbox , while adding "Text Adherence" into the mix since judging from our user feedback, text adherence is an important aspect of generative video model in order for it to become more prevalent as a productivity tool rather than just a fleeing entertaining toy. Thus, below are the various aspects (and their respective explaination) we will adopt to compare the output videos of different model versions. ## Prompt Adherence We'd assessed how well the output video matched the given text prompt. For example, in the given prompt: “A peaceful Zen garden with carefully raked sand, bonsai trees, and a small koi pond.” We'd looked to see if there was prompt adherence by looking at the presence of key concepts of the prompt: - Is there a garden? - Does it look peaceful? - Is the sand present, and is it raked? - Are there bonsai trees? - Is the a small koi pond? Scoring - High: If all or most of the key concepts are present. - Medium: If half the key concepts are present. - Low: If less than half of key concepts are present. ## Text Adherence We'd assess the level of accuracy of the text reproduced in the video as per the given prompts, if there are any text requirements in the prompts. - High: texts are reproduced accurately in the generated video - Medium: texts are reproduced with minor mistakes in the generated video - Low: texts are not reproduced or reproduced with major mistakes in the generated video ## Video Realism We'd assess how closely the generated video resembles reality, although reality is not always an appropriate benchmark depending on the context of the prompt. - High: Realistic lighting, textures, and proportions. - Medium: Somewhat realistic but with slight issues in shadows or textures. - Low: Animated or artificial appearance. ## Artifacts We'd scan for any visible artifacts, distortions, or errors in the video, such as: - Unnatural distortions in objects or backgrounds - Misplaced or floating elements - Inconsistent lighting or shadows - Unnatural repeating patterns - Unnatural movements - Blurred or pixelated areas Scoring - High: If all or 5 of the errors are present. - Medium: If 2 or 3 errors are present. - Low: If 1 or 0 errors are present. ## v1.0 and v1.5 Video Comparison And now, let's check out the same-prompt comparison between Dream Machine 1.0 versus the new Dream Machine 1.5 version model. Prompt: "old man walking in a park, anime style." Prompt Adherence: Both videos display an old man walking and a park, although the 1.5 version has a prolonged shot where no old man is present, but this can be due to lack of specificity in the prompt. Video Realism : both versions show the basic elements of the anime style, and the 1.5 version show the shadow details from the trees in the park. Artifacts: The 1.0 version shows a significant error of the elderly man facing away from the shot but walking towards it. And the 1.5 version shows a prolonged shot with no human figure present. Overall, we see a slight improvement from the v1.5 video. Prompt: "create a tornado with the word 'BYLD Network' on the outside of it" Prompt Adherence: Both videos display a tornado with the v1.5 much more realistic than the v1.0 Video Realism : The proportion in the v1.0 is quite off as the tornado portrayed is also invisible. In the v1.5 video we can see elements flying slowing circling the eye of the tornado. Text Adherence : The v1.0 video spelled out "BYLD Network quite accurately with the Y and L overlapping a bit together. For the v1.5 video it spelled the word with a double B. Artifacts: The 1.0 version portrayed a much less accurate representation of the tornado compared to the v1.5 video. Overall, we see a general improvement from the v1.5 video, with the except on Text Adherence aspect. Prompt: "a teddy bear in sunglasses playing electric guitar, dancing and headbanging in the jungle in front of a large beautiful waterfall" Prompt Adherence: Both videos display a teddy bear doning a pair of sunglasses, although if you google "teddy bear" most will bear more resemblence with the one from v1.5. Both video show a running fall amidst a jungle. However, v1.0 shows much better "headbanging" and playing motion compared to the relatively still v1.5 video. Video Realism : The level of realism displayed in the v1.0 video is very high given the teddy's dynamic movements, the following shadows, and the realistic human-like motions. The v1.5 videos on the other hand is very underwhelming given the static look of the character. Artifacts: The 1.5 showed a more static version of the teddy whereas the prompt specifically asked for "dancing and headbanging". For this prompt, it is quite clear that the v1.0 video is of a higher quality version. Prompt: "a black Dodge Challenger on asphalt drifts around a red 'Kassir 34' sign, viewed from above." Prompt Adherence: Both video displayed a black Dodge Challenger on asphalt, we see a red sign, the view is from the above, but none of the cars are drifting, which could be due to the lack of movement-specific training dataset. Video Realism : The level of realism displayed in the v1.5 is quite high - the lighting on the car, the texture of the car itself, and the shadows of the surrondings are all comparatively better than the version demonstrated in the v1.0. Text Adherence : The texts reproduced in the v1.5 video is higher compared to the v1.0 version; the latter texts spelled "Kassar" whereas the former spelled "Kassi 34", which is just one letter short of the text specified in the prompt. Artifacts: Neither videos showed the drifting motion as specified in the prompt. Other than that there does not seem any major artifiacts in the video. For this prompt, the v1.0 video is of a higher quality compared to the v1.0 version. ## Conclusion Based on the various examples provided above, we can see that Dream Machine 1.5 from Luma is indeed better than the previous 1.0 version, in overall quality, text adherence, and video realism. It should be noted that the better quality does not always happen as can be observed from the teddy bear example. However, given the probabilistic nature of generative AI model, this is to be expected. We hope that you found our comparison useful! And if you interested, check out the generative AI APIs from PiAPI ! ## Midjourey V6.1 through Midjourney API An intro to the new Midjourney V6.1 model, acccessing it through Midjourney API, and the comparison results against previous models. Hi developers! As you probably already know, the founder and CEO of Midjourney DavidH posted on July 30th 2024 in Midjourney's Discord server that Midjourney V6.1 is here, perfect for all y'all developers to try out! :D Midjourney V6.1 Announcement ## Midjourney V6.1 Update! Alright, so let's check out the new improvements with the V6.1 model of Midjourney! - 25% faster - all you developers who are complaining about fast tasks are too expensive but relax jobs are too slow, you might be excited about this one! - Better quality! Better images for arms, legs, body parts, plants, animals, etc! - Picture enhancement - eyes, faces, hands all getting improvement in detail - Textual Accuracy - when you put texts in quotation marks in prompts, the new model will generate pictures with these texts with higher accuracy! - Lastly, users don't have to add --v 6.1 to try the new model, the new V6.1 model will be selected as a default and users can use the --v parameter to choose the previous model versions as they desire. ## Accessing the Midjourney V6.1 model through API! As you might have already guessed, since Midjourney has decided that users will switch to the V6.1 model by default; therefore for developer's using our Midjourney API , no changes to the existing code is needed to try out the V6.1 model. And if you want to try previous model versions, you can just add the --v parameter as per Midjourney documentation . Below are two sample cURL code for your reference. Midjourney API Imagine Endpoint call using the V6.1 model curl --location 'https://api.piapi.ai/mj/v2/imagine' \\ --header 'X-API-Key: your_api_key' \\ --header 'Content-Type: application/json' \\ --data '{ "prompt": "a boy running in the park, arms in swaying back and forth, wearing a t-shirt saying '\\''Love Midjourney'\\'' ", "process_mode": "fast", "aspect_ratio": "", "webhook_endpoint": "", "webhook_secret": "" }' Midjourney API Imagine Endpoint call using the V6 model curl --location 'https://api.piapi.ai/mj/v2/imagine' \\ --header 'X-API-Key: your_api_key' \\ --header 'Content-Type: application/json' \\ --data '{ "prompt": "a picture of pianist playing piano, wearing a golden ring on the index finger of the right hand, face focused with concentration --v 6 ", "process_mode": "fast", "aspect_ratio": "", "webhook_endpoint": "", "webhook_secret": "" }' ## V6.1 vs V6 Model Comparison And you know we can't wrap this blog up without some actual test comparison on these two models. Since Midjourney mentioned what V6.1 will excel at, we have tried the following prompts to illustrate actual test results on quality of detail, quality of body parts, text accuracy, generation speed, etc. And of course, all the tests below are ran using Midjourney API from PiAPI! V6.1 | "a boy running in the park, arms in swaying back and forth, wearing a t-shirt saying 'Love Midjourney'" V6 | "a boy running in the park, arms in swaying back and forth, wearing a t-shirt saying 'Love Midjourney'" As you can see above, although there aren't too much difference in terms of the accuracy of the arms, the letter "Love Midjourney" is much better traced in V6.1! V6.1 | "a picture of pianist playing piano, wearing a golden ring on the index finger of the right hand, face focused with concentration" V6 | "a picture of pianist playing piano, wearing a golden ring on the index finger of the right hand, face focused with concentration" As shown above, there seem to be a slight improvement of the hand illustration in V6.1. V6.1 | "a basketball star dunking midair, 'Best Dad in Town' shown on his jersey, tongues out, face with concentration" V6 | "a basketball star dunking midair, 'Best Dad in Town' shown on his jersey, tongues out, face with concentration" As you can see, again text adherence is definitely better for V6.1 The motion of the arms, legs and hands are a bit more natural in V6.1. The part with how the tongue is interacting with the face is however quite off for both models, presumably due to the abnormally large tongue. Although this is probably understandable given the lack of realistic training data. Note the two pictures are processed in relax mode, and since our Midjourney API tracks the numbers of seconds that tasks used to generation, below are the times for the two jobs V6.1: 80 seconds V6: 88 seconds ## Conclusion And that is it! As you can see, the V6.1 is definitely a bit faster than the V6 model, has significant better text adherence (although not perfect), and we can see a bit of improvement on its illustration on arms, legs, hands, etc. Like true fans, we are indeed looking forward to the future improvements from Midjourney, we hope you will have fun playing around with the V6.1 model as well! ## How to integrate Midjourney API into Zapier Want to add Midjourney's image generation capability into your automated Zapier workflow? Check out this tutorial on how to do so using the Midjourney API! Hi everyone! I'm sure many of you all have heard of Zapier , it is an online automation tool that allows you to streamline your workflows and connect different web applications seamlessly. In Zapier's visually simple workspace, you can drag and drop various built-in tools such as webhooks and tables, as well as connected apps like Gmail and Slack into the workflow. Today, we will dive in and see how we can connect and integrate PiAPI's Midjourney API into Zapier. Let's go! ## Sign up First and foremost, we will need to jump into Zapier's website and sign up for an account or directly log in with your Google account. Once you've signed up and logged in, you may choose the connected apps you want to use and click the 'Finish Setup' button. After that, you will be redirected to the dashboard page, which will look similar to the image below. An image of Zapier's dashboard. Below are some common terms used in Zapier: Zap : Zap refers to an automated workflow that comprises of a trigger and one or multiple actions executed when the trigger event initiates; zap allows you to automate tasks based on specific events. Trigger: A trigger is an event that initiates an automation workflow. The trigger event can be set up using either Built-in tools (ex. webhook) or can be set in some connected apps (ex. Gmail, Google Docs, Google Drive, etc.) Action: An action is an event that an zap is performed after an triggered event. The action event can set up using either Built-in tools (ex. Tables) or can be set in some connected apps . ## Creating a zap Now, we are going to create a Zap, by clicking the create and then the Zap button on the top-left corner of the dashboard. Once you are inside the editor, you may click the 'Edit' icon on the top-left corner and change the 'Untitled Zap' input into your preferred name. Creating and changing the name of a Zap ## Set up a Trigger Next, let's move on to set up the 'Trigger'. There are two main Trigger types: - Polling Trigger - A trigger that asks the API for all recent data. If there is no new data, it waits for a period of time before asking again. - Instant Trigger - A trigger that sends new data to Zapier from an application automatically without asking the API if there is any new data. For today's tutorial, we will be using Instant Triggers. The first step is to click on the Trigger box and click on Webhook . Note: Zapier's webhook tool is a premium app. When you create a new Zapier account, you will have 14 days of free trial to try Zapier's paid features. These paid features include Premium apps , such as PayPal and webhook (which we will be using for this tutorial). You may refer to this link here: help.zapier.com Selecting Webhook tool in the Trigger box Our next step will be to search for Catch Hook and select it as an event, leave the trigger section empty and click continue . Setting up the webhook ## Testing the Trigger Copy the webhook URL provided by Zapier and insert it in your Midjourney's API imagine prompt and send the request. Once the task is completed, Zapier will be notified and the response will be shown in their test section under the Trigger box. Once Zapier receives the response from the API, it records the request details and generates the data fields as per the API response structure. Then, you can proceed to the next step. Inserting the webhook's URL into the API with a record of the API's response output ## Configure an action Once a trigger is activated, actions are carried out automatically to complete the desired task. The Actions events can include: - Sending messages or notifications - Perform any other functions supported by the connected apps in Zapier So, click the Action box below the Trigger box. Here, there are multiple connected apps you can choose from. For this tutorial we will stick to the Gmail event. So, click on the Gmail button. Selecting Gmail as an action In the settings, select the Send Email event in the list of events and link your Gmail account in the next step (the Account step), then click continue . The Action step is where you can enter the details of the email content. For now, I am going to send the email to myself, so I will enter the subject line and select the data fields I want from the the imagine task response (such as the task status, the result image URL, and the meta task request prompt) into the body section, and then click on continue . Setting up an Action with Gmail ## Testing the action Upon reaching the test page, you will see a confirmation notice that shows the email has been sent to the Gmail, and you can see what the email looks like in your inbox. As you can see from the images below, it works perfectly! An email with the data field contents sent from Zapier to Gmail Now that the setup is done. Your Zap is now live! It will run automatically whenever the specified trigger event occurs. You can also return to the editor to make changes or turn it off if required. ## Conclusion With this tutorial, you are now able to integrate PiAPI's Midjourney API into Zapier's automation workflow platform. Thank you for reading through this tutorial - if you find this content enjoyable and would like to try out PiAPI's Midjourney API, don't hesitate to sign up for our workspace! Also, if you are interested in Guide and Library for Midjourney Style Reference, check out MidjourneySref ! ## How to use Faceswap API from PiAPI with Postman Interested in using Faceswap API but don't know how to do it? Check out this tutorial on how to use Faceswap API with Postman and create your favourite face swapped images! Hi developers! We are very delighted to make this tutorial to show how you can use PiAPI's Faceswap API with Postman ! Before we dive into this tutorial, do sign up for our Workspace, obtain your API Key, and check out the documentation for our Faceswap API endpoints. Now, let's get started! ## Tutorial Firstly, you will need to open up or download Postman. Click the "New" button on the top left of your screen and create a new HTTP request, then change the request type to "POST". Setting up the Faceswap API in Postman Enter the Faceswap API's endpoint address from our docs into the endpoint input field, click on "Headers" and type "x-api-key" in the empty space under the "Key" text and enter your API Key under the "Value" text as shown in the example below. Entering PiAPI's Faceswap API endpoint address, the API Key entry, and associated value in Postman Next, click on "Body" located below the endpoint address, then click on "raw", copy the code from our docs and insert it into the body section. Press "Send", then a task id will be returned to you in the response section, and you will need to keep it for later use. Once you're done, proceed to the next step. The request body for generating a Faceswap task Now, we are going to fetch for the task that has been submitted. You will need to create a new HTTP request, change the request type to "POST", then copy the fetch endpoint URL from our docs into the endpoint input field, similar to the above steps. Entering the fetch endpoint url, the API Key entry, and associated value in Postman Then, enter the saved task id response into the body of the fetch endpoint, and press "Send". Once the result status shows "finished", you may copy the returned URL (as per arrow below) into a web browser and see your face-swapped image! The Request HTTP call for fetching the faceswapped image Note: As you are sending the fetch request, if the returned "status" is either "pending" or "processing", then you should wait for a moment before you re-send the request again. ## Conclusion If you are wondering about pricing, please check out our Faceswap page 's pricing section for more information. We also provide custom dedicated deployment service to users who need solutions for specific requirements (ex. low latency, high concurrency, shorter queuing time). And this is how you can get started using PiAPI's Faceswap API with Postman! Thank you for reading through this tutorial. 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