{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-seedance-2-0","slug":"seedance-2-0","name":"Seedance 2.0","type":"model","url":"https://seed.bytedance.com/en/seedance2_0","page_url":"https://unfragile.ai/seedance-2-0","categories":["video-generation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-seedance-2-0__cap_0","uri":"capability://image.visual.image.to.video.generation.with.temporal.coherence","name":"image-to-video generation with temporal coherence","description":"Converts static images into dynamic videos by learning temporal motion patterns and frame interpolation across a specified duration. Uses a diffusion-based architecture that conditions on the input image and generates subsequent frames while maintaining visual consistency, spatial coherence, and realistic motion dynamics. The model infers plausible motion trajectories from the image content without explicit optical flow guidance.","intents":["I want to animate a still photograph into a short video clip with natural motion","I need to create dynamic content from product images for e-commerce without manual animation","I want to generate video previews from static design mockups or architectural renderings","I need to extend short video clips by generating additional frames with consistent motion"],"best_for":["content creators and marketers generating social media videos from static assets","e-commerce platforms automating product video generation at scale","film and animation studios exploring AI-assisted motion synthesis for storyboarding"],"limitations":["Motion generation is inferred from image content alone — complex or ambiguous motion may produce unrealistic results","Output video duration is constrained (typically 4-8 seconds based on model training)","Requires high-quality input images; low-resolution or heavily compressed images degrade output quality","No explicit control over motion direction, speed, or type — motion is fully generative","May struggle with images containing multiple independent moving objects or complex scene dynamics"],"requires":["High-quality input image (minimum 512x512 resolution recommended)","API access to Seedance 2.0 service or local model weights","GPU with sufficient VRAM for inference (typically 8GB+ for optimal performance)"],"input_types":["image (JPEG, PNG, WebP)","image metadata (optional: aspect ratio, duration parameters)"],"output_types":["video (MP4, WebM)","video metadata (frame count, duration, resolution)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_1","uri":"capability://image.visual.text.to.video.generation.with.semantic.grounding","name":"text-to-video generation with semantic grounding","description":"Generates videos from natural language descriptions by encoding text prompts into semantic embeddings and conditioning a diffusion model to synthesize frames that match the described content, motion, and style. The architecture uses a text encoder (likely CLIP-based or similar) to bridge language understanding with visual generation, enabling control over scene composition, camera movement, object interactions, and temporal progression through descriptive language.","intents":["I want to create a video from a written script or storyboard description without filming","I need to generate multiple video variations from the same text prompt to explore creative directions","I want to produce marketing videos or explainer content from product descriptions","I need to visualize narrative concepts or story ideas as video prototypes"],"best_for":["screenwriters and directors prototyping visual concepts from scripts","marketing teams generating video content from product briefs or campaign descriptions","educators creating educational videos from lesson descriptions","indie game developers and filmmakers with limited budgets exploring visual ideas"],"limitations":["Text-to-video quality is highly dependent on prompt clarity and specificity — vague descriptions produce inconsistent results","Semantic understanding is limited to concepts present in training data; novel or niche scenarios may fail","Generated videos may contain artifacts, temporal flickering, or inconsistent object persistence across frames","No fine-grained control over camera parameters, lighting, or specific visual effects — control is indirect through natural language","Output duration is fixed and typically short (4-8 seconds); longer narratives require multiple prompts and manual stitching","Computational cost scales with video length and resolution; longer outputs require significantly more inference time"],"requires":["Text prompt in English (other languages may have degraded performance)","API access to Seedance 2.0 service","GPU with 12GB+ VRAM for reasonable inference latency"],"input_types":["text (natural language prompt, 10-500 characters typical)","optional parameters (duration, aspect ratio, style guidance)"],"output_types":["video (MP4, WebM)","video metadata (duration, resolution, frame rate)"],"categories":["image-visual","text-generation-language","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_2","uri":"capability://image.visual.multi.frame.consistency.and.temporal.coherence.enforcement","name":"multi-frame consistency and temporal coherence enforcement","description":"Maintains visual consistency across generated video frames by enforcing temporal coherence constraints during the diffusion process, ensuring objects, lighting, and scene composition remain stable across time. The model uses attention mechanisms that operate across the temporal dimension, allowing frames to 'attend' to previous frames and maintain spatial relationships, preventing flickering, object teleportation, or sudden appearance/disappearance of scene elements.","intents":["I want to generate videos where objects maintain their identity and position across frames","I need to prevent temporal artifacts like flickering or jittering in generated videos","I want to ensure consistent lighting and color grading throughout the video","I need videos where camera movement is smooth and physically plausible"],"best_for":["professional content creators requiring broadcast-quality temporal stability","e-commerce platforms needing consistent product representation across video frames","VFX studios using AI-generated content as reference or base material"],"limitations":["Temporal coherence is probabilistic — occasional artifacts may still occur in edge cases","Enforcing strict coherence can reduce motion diversity and make videos appear more static","Coherence enforcement adds computational overhead, increasing inference latency by 15-30%","Complex scenes with many independent moving objects may struggle to maintain per-object consistency"],"requires":["Video generation request with temporal coherence enabled (default behavior)","Sufficient GPU memory to maintain frame-to-frame attention state (12GB+ recommended)"],"input_types":["image or text prompt","temporal coherence parameters (optional: strictness level)"],"output_types":["video with enforced temporal consistency","coherence metrics (optional: per-frame consistency scores)"],"categories":["image-visual","video-generation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_3","uri":"capability://image.visual.variable.length.video.generation.with.duration.control","name":"variable-length video generation with duration control","description":"Generates videos of different lengths by controlling the number of diffusion steps applied in the temporal dimension, allowing users to specify desired video duration (typically 4-16 seconds) and have the model synthesize appropriate motion and frame progression for that duration. The architecture uses a temporal positional encoding scheme that scales with video length, enabling the model to adapt motion speed and event pacing to fit the requested duration.","intents":["I want to generate short clips for social media (4-6 seconds) vs longer form content (10-15 seconds)","I need to control how quickly motion unfolds in the generated video","I want to generate videos that fit specific platform requirements (TikTok, YouTube Shorts, Instagram Reels)","I need to create video sequences of varying lengths from the same prompt"],"best_for":["content creators optimizing videos for different social media platforms","marketing teams creating video assets with specific duration requirements","video editors needing variable-length clips for montage or compilation work"],"limitations":["Longer videos (>12 seconds) may show degraded temporal coherence or motion quality","Motion pacing is automatically inferred from duration; no explicit control over event timing","Inference time scales linearly with duration — 16-second videos take ~4x longer than 4-second videos","Very short durations (<2 seconds) may produce abrupt or incomplete motion sequences","Memory requirements increase with duration, potentially exceeding GPU capacity for very long videos"],"requires":["Duration parameter specified in seconds (typically 4-16 second range)","GPU with VRAM scaling with duration (8GB for 4s, 16GB+ for 12-16s)"],"input_types":["image or text prompt","duration in seconds (integer or float)","optional: frame rate (24, 30, 60 fps)"],"output_types":["video with specified duration","metadata (actual duration, frame count, frame rate)"],"categories":["image-visual","video-generation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_4","uri":"capability://image.visual.style.and.aesthetic.control.through.prompt.engineering","name":"style and aesthetic control through prompt engineering","description":"Enables users to influence the visual style, cinematography, and aesthetic of generated videos through natural language descriptions in text prompts, supporting style keywords like 'cinematic', 'documentary', 'animated', 'oil painting', etc. The text encoder learns associations between style descriptors and visual features during training, allowing the diffusion model to condition generation on these aesthetic preferences without explicit style transfer or post-processing.","intents":["I want to generate videos in a specific visual style (e.g., cinematic, animated, retro)","I need to control the mood or tone of generated content through descriptive language","I want to generate videos that match a brand's visual identity or aesthetic guidelines","I need to explore different artistic interpretations of the same scene description"],"best_for":["creative directors and designers controlling visual aesthetics of AI-generated content","brand teams ensuring generated videos align with visual identity guidelines","artists exploring AI as a creative tool for style exploration and experimentation"],"limitations":["Style control is indirect and probabilistic — style keywords don't guarantee consistent aesthetic application","Uncommon or highly specific style descriptors may not be well-represented in training data","Style and content can conflict — requesting incompatible styles and content may produce unpredictable results","No explicit control over specific visual parameters (color palette, lighting temperature, contrast) — control is through natural language only","Style consistency across multiple generated videos from the same prompt is not guaranteed"],"requires":["Text prompt including style descriptors","Understanding of effective style keywords for the model (requires experimentation or documentation)"],"input_types":["text prompt with style keywords (e.g., 'cinematic sci-fi landscape')","optional: style guidance strength parameter"],"output_types":["video with applied aesthetic style","style metadata (detected style keywords, confidence scores)"],"categories":["image-visual","text-generation-language","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_5","uri":"capability://image.visual.batch.video.generation.with.parameter.variation","name":"batch video generation with parameter variation","description":"Supports generating multiple videos from a single input (image or text) with systematically varied parameters, enabling users to explore different motion interpretations, durations, or style variations in a single batch operation. The system queues multiple generation requests with different parameter sets and processes them efficiently, potentially leveraging GPU batching or parallel processing to reduce total wall-clock time compared to sequential generation.","intents":["I want to generate multiple video variations from one image to compare motion options","I need to create videos of the same scene at different durations for different platforms","I want to explore how different style keywords affect the same content","I need to generate a large volume of video content efficiently for a campaign"],"best_for":["content creators and designers exploring creative variations at scale","marketing teams generating multiple video assets for A/B testing","researchers studying model behavior across parameter variations"],"limitations":["Batch processing may be queued if API is under high load — no guaranteed latency","Total inference time scales linearly with batch size; large batches may take hours","GPU memory constraints may limit batch size or require sequential processing","No built-in deduplication — similar variations may be generated multiple times if parameters overlap","Cost scales with number of videos generated — batch operations can be expensive for large batches"],"requires":["Batch request format specifying multiple parameter sets","API support for batch operations (may require specific endpoint or SDK)","Sufficient quota/credits for multiple video generations"],"input_types":["image or text prompt","array of parameter variations (duration, style, seed, etc.)"],"output_types":["array of videos","batch metadata (generation times, parameter mappings)"],"categories":["image-visual","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_6","uri":"capability://image.visual.motion.control.through.seed.and.stochasticity.parameters","name":"motion control through seed and stochasticity parameters","description":"Provides fine-grained control over the randomness and reproducibility of generated motion by exposing seed parameters and stochasticity controls in the diffusion process. Users can set a fixed seed to reproduce identical videos, or adjust stochasticity levels to control the variance in motion generation — higher stochasticity produces more diverse and unpredictable motion, while lower stochasticity produces more deterministic and conservative motion.","intents":["I want to reproduce the exact same video generation result for consistency","I need to generate multiple variations of motion while keeping other aspects constant","I want to control how 'creative' or 'conservative' the motion generation is","I need to debug or verify specific motion behaviors by reproducing them exactly"],"best_for":["developers and researchers requiring reproducible video generation for testing and validation","content creators iterating on specific motion variations","teams needing consistent results across multiple generation runs"],"limitations":["Seed reproducibility may not be guaranteed across different hardware, software versions, or API updates","Stochasticity controls are coarse-grained — no per-object or per-region motion control","Very low stochasticity may produce overly static or repetitive motion","Seed values don't directly correspond to specific motion types — users must experiment to find desired seeds"],"requires":["Optional: seed parameter (integer)","Optional: stochasticity parameter (float, typically 0.0-1.0 range)"],"input_types":["image or text prompt","seed (integer, optional)","stochasticity level (float, optional)"],"output_types":["video with controlled motion randomness","metadata (seed used, stochasticity level)"],"categories":["image-visual","video-generation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_7","uri":"capability://image.visual.api.based.video.generation.with.asynchronous.processing","name":"api-based video generation with asynchronous processing","description":"Provides a cloud-based API interface for video generation that accepts image or text inputs and returns video files, with support for asynchronous processing where requests are queued and results are retrieved via polling or webhooks. The architecture likely uses a request queue, worker pool, and result storage system to handle concurrent requests and manage GPU resources efficiently across multiple users.","intents":["I want to integrate video generation into my application without running models locally","I need to generate videos at scale without managing GPU infrastructure","I want to submit a generation request and retrieve results asynchronously","I need to integrate video generation into a CI/CD pipeline or automated workflow"],"best_for":["application developers integrating video generation into web or mobile apps","teams without GPU infrastructure or expertise","platforms requiring scalable video generation for multiple concurrent users","automation engineers building video generation into workflows"],"limitations":["API latency depends on queue depth and server load — no guaranteed response time","Asynchronous processing adds complexity to client code (polling, webhook handling, error recovery)","API rate limits may restrict generation frequency or batch size","Network latency for uploading images and downloading videos adds overhead","Pricing may be per-video or per-minute, making large-scale generation expensive","API availability and uptime depend on service provider's infrastructure"],"requires":["API key or authentication credentials","HTTP client library (curl, requests, axios, etc.)","Network connectivity to Seedance API endpoint","Storage for input images and output videos"],"input_types":["image file (uploaded via multipart form or URL)","text prompt (JSON payload)","generation parameters (JSON)"],"output_types":["video file (MP4, WebM, or other formats)","job status (pending, processing, completed, failed)","metadata (duration, resolution, generation time)"],"categories":["image-visual","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_8","uri":"capability://image.visual.video.quality.and.resolution.scaling","name":"video quality and resolution scaling","description":"Supports generating videos at different resolutions and quality levels, allowing users to trade off between output quality, inference time, and computational cost. The model likely uses a hierarchical or progressive generation approach where lower resolutions are generated first and then upscaled, or supports multiple model variants trained at different resolutions.","intents":["I want to generate quick preview videos at low resolution before committing to high-quality generation","I need to generate videos at specific resolutions for different platforms (1080p for YouTube, 720p for social media)","I want to optimize for speed vs quality based on my use case","I need to generate high-resolution videos for professional or broadcast use"],"best_for":["content creators optimizing for different distribution channels","teams balancing quality requirements with computational budget","developers implementing progressive generation workflows (preview → final)"],"limitations":["Higher resolutions require exponentially more GPU memory and computation time","Quality improvements may plateau at certain resolutions depending on model training","Upscaling from low to high resolution may introduce artifacts or loss of fine details","Not all resolutions may be supported — only discrete options (e.g., 480p, 720p, 1080p) may be available","Very high resolutions (4K+) may not be supported due to computational constraints"],"requires":["Resolution parameter (e.g., 480p, 720p, 1080p)","GPU with sufficient VRAM for requested resolution (8GB for 720p, 16GB+ for 1080p)"],"input_types":["image or text prompt","resolution parameter (string or tuple: width x height)"],"output_types":["video at specified resolution","metadata (actual resolution, bitrate, file size)"],"categories":["image-visual","video-generation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seedance-2-0__cap_9","uri":"capability://image.visual.frame.by.frame.editing.and.refinement.interface","name":"frame-by-frame editing and refinement interface","description":"Provides tools to edit or refine specific frames within generated videos, allowing users to make targeted adjustments to individual frames without regenerating the entire video. This likely includes frame selection, masking, inpainting, or blending capabilities that enable users to fix artifacts, adjust composition, or modify specific elements while maintaining temporal consistency with adjacent frames.","intents":["I want to fix artifacts or errors in specific frames without regenerating the entire video","I need to adjust the composition or framing of specific frames","I want to modify or remove specific objects from certain frames","I need to blend or transition between different generated videos at specific frames"],"best_for":["video editors and post-production professionals refining AI-generated content","content creators making targeted fixes to generated videos","teams requiring high-quality output with minimal regeneration overhead"],"limitations":["Frame-level editing may break temporal coherence if not carefully applied","Inpainting or modification of frames requires careful masking to avoid visible seams","Editing tools may be limited compared to professional video editing software","Changes to individual frames may require re-encoding or re-processing adjacent frames to maintain consistency","No guarantee that edited frames will blend seamlessly with surrounding frames"],"requires":["Generated video file","Frame selection and editing interface (web UI or SDK)","Optional: mask or region specification for targeted edits"],"input_types":["video file","frame index or timestamp","edit specification (mask, inpainting prompt, adjustment parameters)"],"output_types":["edited video with refined frames","metadata (edited frame indices, edit history)"],"categories":["image-visual","video-generation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["High-quality input image (minimum 512x512 resolution recommended)","API access to Seedance 2.0 service or local model weights","GPU with sufficient VRAM for inference (typically 8GB+ for optimal performance)","Text prompt in English (other languages may have degraded performance)","API access to Seedance 2.0 service","GPU with 12GB+ VRAM for reasonable inference latency","Video generation request with temporal coherence enabled (default behavior)","Sufficient GPU memory to maintain frame-to-frame attention state (12GB+ recommended)","Duration parameter specified in seconds (typically 4-16 second range)","GPU with VRAM scaling with duration (8GB for 4s, 16GB+ for 12-16s)"],"failure_modes":["Motion generation is inferred from image content alone — complex or ambiguous motion may produce unrealistic results","Output video duration is constrained (typically 4-8 seconds based on model training)","Requires high-quality input images; low-resolution or heavily compressed images degrade output quality","No explicit control over motion direction, speed, or type — motion is fully generative","May struggle with images containing multiple independent moving objects or complex scene dynamics","Text-to-video quality is highly dependent on prompt clarity and specificity — vague descriptions produce inconsistent results","Semantic understanding is limited to concepts present in training data; novel or niche scenarios may fail","Generated videos may contain artifacts, temporal flickering, or inconsistent object persistence across frames","No fine-grained control over camera parameters, lighting, or specific visual effects — control is indirect through natural language","Output duration is fixed and typically short (4-8 seconds); longer narratives require multiple prompts and manual stitching","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=seedance-2-0","compare_url":"https://unfragile.ai/compare?artifact=seedance-2-0"}},"signature":"Ol+cJ50I3xNmAz/x5Yxtv7ff2otPs66vH9TQEmA4Z/eC/D1EK7gSE0nZNmLeLvWMpBW+PdJqeXfooKJiN02xCw==","signedAt":"2026-06-20T20:08:09.968Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/seedance-2-0","artifact":"https://unfragile.ai/seedance-2-0","verify":"https://unfragile.ai/api/v1/verify?slug=seedance-2-0","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}