{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers","slug":"wan-ai--wan2.2-ti2v-5b-diffusers","name":"Wan2.2-TI2V-5B-Diffusers","type":"model","url":"https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers","page_url":"https://unfragile.ai/wan-ai--wan2.2-ti2v-5b-diffusers","categories":["video-generation"],"tags":["diffusers","safetensors","text-to-video","en","zh","arxiv:2503.20314","license:apache-2.0","diffusers:WanPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_0","uri":"capability://image.visual.text.to.video.generation.with.diffusion.based.synthesis","name":"text-to-video generation with diffusion-based synthesis","description":"Generates short-form videos (typically 5-10 seconds) from natural language text prompts using a latent diffusion architecture. The model operates in a compressed latent space rather than pixel space, enabling efficient generation of multi-frame sequences. It uses a UNet-based denoising network conditioned on text embeddings (via CLIP or similar encoders) to iteratively refine noise into coherent video frames, with temporal consistency mechanisms to maintain object identity and motion continuity across frames.","intents":["Generate short promotional videos or social media clips from text descriptions without manual filming","Create visual storyboards or concept videos for creative projects based on narrative prompts","Prototype video content for games, animations, or interactive media from text specifications","Produce diverse video variations from a single text prompt for A/B testing or creative exploration"],"best_for":["Content creators and marketers needing rapid video prototyping without production equipment","Game developers and animators exploring visual concepts before committing to manual production","AI researchers and engineers building video generation pipelines or multimodal systems","Teams with limited video production budgets exploring generative alternatives"],"limitations":["Output duration typically limited to 5-10 seconds per generation; longer sequences require stitching or multiple inference passes","Temporal coherence degrades with complex motion or scene changes; objects may flicker or lose consistency across frames","Inference latency is high (30-120 seconds per video on consumer GPUs); real-time or near-real-time generation not feasible","Model struggles with precise control over object placement, camera movement, or specific spatial relationships described in text","Memory footprint of 5B parameters requires GPU with minimum 16GB VRAM; CPU inference is impractical","Generated videos may contain artifacts, unnatural physics, or hallucinated details not present in the prompt"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (RTX 3090, A100, or equivalent with 16GB+ VRAM)","Diffusers library 0.25.0+ (HuggingFace)","Transformers library for text encoding (CLIP or similar)","Safetensors library for model weight loading","Minimum 50GB free disk space for model weights and inference cache"],"input_types":["text (natural language prompt, 10-500 characters typical)","optional: negative prompts (text describing unwanted content)","optional: seed (integer for reproducibility)","optional: guidance scale (float controlling prompt adherence vs. diversity)"],"output_types":["video (MP4, WebM, or raw frame sequence)","frame tensors (torch.Tensor with shape [frames, channels, height, width])","latent representations (compressed intermediate representations for further processing)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_1","uri":"capability://text.generation.language.multilingual.prompt.understanding.with.language.agnostic.embeddings","name":"multilingual prompt understanding with language-agnostic embeddings","description":"Processes text prompts in both English and Simplified Chinese by encoding them through a shared multilingual text encoder (likely mBERT or multilingual CLIP variant) that projects prompts into a unified embedding space. This enables the diffusion model to condition video generation on semantically equivalent prompts regardless of input language, with cross-lingual transfer allowing the model to generalize concepts learned from English-dominant training data to Chinese prompts.","intents":["Generate videos from Chinese-language prompts without manual translation to English","Build multilingual video generation applications serving global audiences with native language support","Fine-tune the model on domain-specific Chinese terminology or cultural concepts for localized content"],"best_for":["Content creators and teams in Chinese-speaking markets (China, Taiwan, Singapore) needing native language support","Multilingual AI applications and platforms targeting East Asian users","Researchers studying cross-lingual transfer in generative models"],"limitations":["Quality may be asymmetric between English and Chinese prompts due to training data imbalance (likely more English examples in training)","Idiomatic or culturally-specific Chinese expressions may not translate to coherent visual concepts","No support for other languages (Japanese, Korean, etc.) despite multilingual architecture","Prompt length and complexity constraints apply equally across languages, but Chinese's higher information density per character may cause truncation issues"],"requires":["Multilingual text encoder weights (included in model)","UTF-8 text encoding support in input pipeline","No additional language model dependencies beyond base Diffusers installation"],"input_types":["text (English or Simplified Chinese, 10-500 characters)"],"output_types":["video (language-agnostic, visual output independent of input language)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_2","uri":"capability://tool.use.integration.diffusers.pipeline.abstraction.with.configurable.inference.parameters","name":"diffusers pipeline abstraction with configurable inference parameters","description":"Exposes video generation through the WanPipeline class in HuggingFace Diffusers, a standardized interface that abstracts the underlying diffusion process and allows developers to configure inference behavior via parameters like guidance_scale (controlling prompt adherence), num_inference_steps (trading quality for speed), and random seeds for reproducibility. The pipeline handles model loading, memory management, and GPU/CPU device placement automatically, while supporting both eager execution and compiled/optimized inference modes.","intents":["Integrate text-to-video generation into Python applications with minimal boilerplate code","Experiment with different inference hyperparameters to optimize quality vs. speed tradeoffs","Build reproducible video generation workflows with deterministic outputs via seed control","Deploy the model in production environments with automatic device management and memory optimization"],"best_for":["Python developers building AI applications who want standardized, well-documented APIs","ML engineers prototyping and experimenting with different inference configurations","Teams deploying generative models to production who need battle-tested abstraction layers","Researchers fine-tuning or extending the model within the Diffusers ecosystem"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per inference call due to Python-level orchestration","Limited control over low-level diffusion process (e.g., custom noise schedules, intermediate latent manipulation) without subclassing","Batch inference (multiple prompts in parallel) requires manual loop management; no native batching API","Memory optimization features (e.g., attention slicing, VAE tiling) must be explicitly enabled and may reduce quality"],"requires":["diffusers>=0.25.0","transformers>=4.30.0","torch>=2.0.0","safetensors>=0.3.0"],"input_types":["prompt (string)","negative_prompt (optional string)","num_inference_steps (optional int, default 50)","guidance_scale (optional float, default 7.5)","height, width (optional ints, default 576x1024)","num_frames (optional int, default 120)","seed (optional int for reproducibility)"],"output_types":["PIL.Image.Image list (frames as PIL images)","torch.Tensor (raw video tensor)","video file (MP4/WebM via external encoding)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_3","uri":"capability://safety.moderation.safetensors.based.model.weight.loading.with.integrity.verification","name":"safetensors-based model weight loading with integrity verification","description":"Loads model weights from Safetensors format (a memory-safe, human-readable serialization format) instead of pickle, enabling fast deserialization with built-in integrity checks via SHA256 hashing. The Safetensors format prevents arbitrary code execution during model loading and provides transparent weight inspection, making it suitable for production deployments and security-conscious environments. Loading is optimized for memory efficiency, mapping weights directly to GPU memory without intermediate CPU copies when possible.","intents":["Load model weights safely without risk of arbitrary code execution from untrusted model sources","Verify model integrity and detect corruption or tampering via cryptographic hashing","Optimize model loading speed and memory usage in production deployments","Inspect and audit model weights programmatically for transparency and compliance"],"best_for":["Production systems and enterprises requiring security and auditability in model deployment","Teams working with untrusted or community-contributed models from HuggingFace Hub","Resource-constrained environments (edge devices, serverless functions) where loading speed matters","Compliance-focused organizations needing transparent, verifiable model provenance"],"limitations":["Safetensors format is newer and less widely supported than pickle; some older tools may not recognize it","Integrity verification adds ~100-200ms per model load (negligible for one-time startup, significant for frequent reloading)","Weight inspection requires manual parsing; no built-in tools for automated anomaly detection","Safetensors format is immutable; any model modifications require re-serialization"],"requires":["safetensors>=0.3.0","torch>=1.12.0 (for memory-mapped loading)","Model weights in Safetensors format (provided by Wan-AI)"],"input_types":["model identifier (string, e.g., 'Wan-AI/Wan2.2-TI2V-5B-Diffusers')","optional: local file path to .safetensors file","optional: custom hash for verification"],"output_types":["torch.nn.Module (loaded model)","state_dict (dictionary of weight tensors)","verification status (boolean or hash comparison result)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_4","uri":"capability://image.visual.temporal.consistency.optimization.with.frame.interpolation","name":"temporal consistency optimization with frame interpolation","description":"Applies optical flow-based frame interpolation and temporal smoothing during the diffusion process to maintain visual consistency across generated video frames. The model uses intermediate optical flow estimation to detect motion patterns and applies consistency losses that penalize large frame-to-frame differences in object positions, colors, and textures. This reduces flickering, jitter, and sudden scene changes that are common artifacts in naive frame-by-frame generation, resulting in smoother, more watchable videos.","intents":["Generate videos with smooth, natural motion and minimal flickering or jitter artifacts","Maintain consistent object identity and appearance across the entire video duration","Reduce the need for post-processing stabilization or frame interpolation in downstream pipelines","Enable longer video sequences by improving temporal coherence across multiple generation steps"],"best_for":["Content creators requiring broadcast-quality or social media-ready videos without post-processing","Applications where temporal stability is critical (e.g., product demos, educational videos)","Researchers studying temporal consistency in generative models","Teams building video generation pipelines where downstream processing is expensive or unavailable"],"limitations":["Optical flow estimation adds ~20-40% to inference latency per video","Temporal consistency constraints may over-smooth motion, resulting in unnatural or sluggish movement","Complex scenes with occlusions, fast motion, or scene cuts challenge optical flow estimation, reducing effectiveness","Consistency optimization is applied globally; no per-region control over smoothing strength","Requires additional GPU memory for optical flow computation; may cause OOM on devices with <16GB VRAM"],"requires":["Optical flow estimation library (likely RAFT or similar, included in model dependencies)","Additional GPU memory (~2-4GB) beyond base model requirements","torch>=2.0.0 for efficient flow computation"],"input_types":["video frames (tensor or PIL images)","optional: flow_weight (float, controlling strength of temporal consistency, default 1.0)"],"output_types":["temporally-smoothed video frames (tensor or PIL images)","optical flow maps (optional, for visualization or debugging)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.2-ti2v-5b-diffusers__cap_5","uri":"capability://image.visual.variable.resolution.and.aspect.ratio.support.with.dynamic.padding","name":"variable resolution and aspect ratio support with dynamic padding","description":"Supports generating videos at multiple resolutions and aspect ratios (e.g., 9:16 for mobile, 16:9 for landscape, 1:1 for square) by dynamically padding or cropping input embeddings and applying aspect-ratio-aware positional encodings. The model uses learnable aspect-ratio tokens and resolution-adaptive attention mechanisms to handle variable input dimensions without retraining, enabling flexible output formats for different platforms and use cases.","intents":["Generate videos in platform-specific formats (vertical for TikTok/Instagram Reels, horizontal for YouTube, square for Twitter)","Create video content optimized for multiple distribution channels from a single model","Adapt generated videos to different screen sizes and device orientations without quality loss","Reduce the need for post-processing cropping, padding, or aspect ratio conversion"],"best_for":["Content creators and marketers distributing videos across multiple social media platforms","Applications requiring flexible output formats for different devices or use cases","Teams optimizing content delivery pipelines for diverse audience devices"],"limitations":["Extreme aspect ratios (e.g., 1:4 or 4:1) may produce lower-quality results due to limited training coverage","Dynamic padding adds ~5-10% latency overhead per inference","Memory usage scales with resolution; higher resolutions (e.g., 1080p) require proportionally more VRAM","Aspect ratio tokens are learned during training; fine-tuning on new aspect ratios requires additional training data","Quality may degrade at resolutions significantly different from training resolution (likely 576x1024 or similar)"],"requires":["height, width parameters (integers, typically 256-1024 range)","Aspect ratio tokens in model weights (included in base model)","torch>=2.0.0 for efficient dynamic shape handling"],"input_types":["height (int, e.g., 576)","width (int, e.g., 1024)","aspect_ratio (optional float, auto-computed from height/width if not provided)"],"output_types":["video (tensor or PIL images with specified height/width)","aspect_ratio_metadata (float, for downstream processing)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (RTX 3090, A100, or equivalent with 16GB+ VRAM)","Diffusers library 0.25.0+ (HuggingFace)","Transformers library for text encoding (CLIP or similar)","Safetensors library for model weight loading","Minimum 50GB free disk space for model weights and inference cache","Multilingual text encoder weights (included in model)","UTF-8 text encoding support in input pipeline","No additional language model dependencies beyond base Diffusers installation","diffusers>=0.25.0"],"failure_modes":["Output duration typically limited to 5-10 seconds per generation; longer sequences require stitching or multiple inference passes","Temporal coherence degrades with complex motion or scene changes; objects may flicker or lose consistency across frames","Inference latency is high (30-120 seconds per video on consumer GPUs); real-time or near-real-time generation not feasible","Model struggles with precise control over object placement, camera movement, or specific spatial relationships described in text","Memory footprint of 5B parameters requires GPU with minimum 16GB VRAM; CPU inference is impractical","Generated videos may contain artifacts, unnatural physics, or hallucinated details not present in the prompt","Quality may be asymmetric between English and Chinese prompts due to training data imbalance (likely more English examples in training)","Idiomatic or culturally-specific Chinese expressions may not translate to coherent visual concepts","No support for other languages (Japanese, Korean, etc.) despite multilingual architecture","Prompt length and complexity constraints apply equally across languages, but Chinese's higher information density per character may cause truncation issues","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5505304690794417,"quality":0.22,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:52.093Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":99212,"model_likes":132}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wan-ai--wan2.2-ti2v-5b-diffusers","compare_url":"https://unfragile.ai/compare?artifact=wan-ai--wan2.2-ti2v-5b-diffusers"}},"signature":"SXii8QcG62VAn92NrCbKu3sIPVa9wc5UtH59LWJrvyM/quRTFTIpGaOrMHdSYlDC58hGaHM/qinyQ7QaXLfdDg==","signedAt":"2026-06-21T05:00:01.962Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wan-ai--wan2.2-ti2v-5b-diffusers","artifact":"https://unfragile.ai/wan-ai--wan2.2-ti2v-5b-diffusers","verify":"https://unfragile.ai/api/v1/verify?slug=wan-ai--wan2.2-ti2v-5b-diffusers","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"}}