{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers","slug":"wan-ai--wan2.1-t2v-14b-diffusers","name":"Wan2.1-T2V-14B-Diffusers","type":"model","url":"https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers","page_url":"https://unfragile.ai/wan-ai--wan2.1-t2v-14b-diffusers","categories":["video-generation"],"tags":["diffusers","safetensors","video generation","text-to-video","en","zh","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--wan2.1-t2v-14b-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 video frames from natural language text prompts using a 14B-parameter diffusion model architecture. The model operates through iterative denoising steps, progressively refining latent video representations conditioned on text embeddings. Implements the WanPipeline interface within the Hugging Face Diffusers framework, enabling standardized pipeline composition with scheduler control, guidance scaling, and multi-step inference.","intents":["Generate short video clips from text descriptions for content creation workflows","Create visual storyboards from narrative prompts for video production planning","Synthesize demonstration videos from technical specifications or product descriptions","Produce training data or synthetic video content for machine learning pipelines"],"best_for":["Content creators and video producers seeking rapid video prototyping from text","AI/ML engineers building video generation pipelines or multimodal systems","Teams deploying open-source video synthesis without cloud API dependencies"],"limitations":["Output video length and resolution constrained by model training data — typically generates short clips (2-8 seconds) at 480p-720p resolution","Temporal coherence degrades with complex motion or long-duration prompts; single-shot generation without frame-by-frame control","Inference latency high (~30-120 seconds per video on consumer GPUs) due to iterative denoising steps across full video tensor","Memory footprint requires 16GB+ VRAM for full model inference; quantization or model sharding needed for smaller devices","Text-to-video alignment quality depends on prompt specificity; vague descriptions produce inconsistent or low-quality outputs"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (NVIDIA RTX 3090/4090 recommended)","Hugging Face Diffusers library (>=0.21.0)","Safetensors library for model weight loading","Minimum 16GB GPU VRAM; 24GB+ recommended for batch processing","Hugging Face Hub authentication token for model download"],"input_types":["text (natural language prompts in English or Chinese)","numerical parameters (guidance_scale, num_inference_steps, seed for reproducibility)"],"output_types":["video tensor (torch.Tensor, shape: [batch, frames, channels, height, width])","MP4 or WebM video file (via post-processing with ffmpeg or torchvision)","PIL Image sequences (individual frames for frame-by-frame inspection)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_1","uri":"capability://text.generation.language.multi.language.text.conditioning.with.cross.lingual.embeddings","name":"multi-language text conditioning with cross-lingual embeddings","description":"Accepts text prompts in English and Simplified Chinese, encoding them through a shared text encoder that produces language-agnostic embeddings for video conditioning. The model uses a unified embedding space trained on bilingual caption-video pairs, allowing the diffusion backbone to generate semantically consistent videos regardless of input language. Conditioning is applied at multiple U-Net layers via cross-attention mechanisms.","intents":["Generate videos from Chinese-language prompts without separate model variants","Build multilingual video generation APIs serving global audiences","Create training datasets with mixed-language captions for downstream video understanding models"],"best_for":["Teams operating in Chinese-speaking markets or multilingual environments","Developers building international content creation platforms","Researchers studying cross-lingual video-language alignment"],"limitations":["Language support limited to English and Simplified Chinese; Traditional Chinese, Japanese, or other languages require fine-tuning","Cross-lingual performance may degrade for culture-specific concepts or idioms not well-represented in training data","Prompt translation quality affects output; ambiguous or poorly-phrased prompts in either language produce inconsistent results"],"requires":["Text encoder compatible with both English and Chinese tokenization (typically CLIP or mBERT-based)","Training data with balanced English-Chinese caption pairs (model-specific requirement)"],"input_types":["text in English (e.g., 'a cat jumping over a fence')","text in Simplified Chinese (e.g., '一只猫跳过栅栏')"],"output_types":["video tensor conditioned on language-agnostic embeddings"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_2","uri":"capability://automation.workflow.scheduler.agnostic.inference.with.configurable.denoising.schedules","name":"scheduler-agnostic inference with configurable denoising schedules","description":"Exposes scheduler selection and configuration as first-class parameters in the WanPipeline, allowing users to swap between DDIM, Euler, DPM++ Scheduler 2M, and other Diffusers-compatible schedulers without reloading the model. Scheduler choice directly controls the denoising trajectory, step count, and noise prediction strategy, enabling trade-offs between inference speed (fewer steps) and output quality (more steps with advanced schedulers).","intents":["Optimize inference latency for real-time or interactive video generation applications","Experiment with different denoising strategies to improve video quality without retraining","Implement adaptive scheduling based on hardware constraints or user-specified time budgets"],"best_for":["Developers optimizing inference performance for production deployments","Researchers experimenting with diffusion sampling strategies","Teams building interactive tools where latency is critical"],"limitations":["Scheduler selection requires manual tuning; no automatic scheduler recommendation based on prompt or hardware","Some schedulers (e.g., DPM++ 2M) require more steps for quality, increasing latency; trade-off between speed and quality is not automatically balanced","Scheduler compatibility depends on Diffusers version; older versions may not support all schedulers"],"requires":["Hugging Face Diffusers library with scheduler implementations","Understanding of diffusion sampling strategies (DDIM vs Euler vs DPM++) for effective tuning"],"input_types":["scheduler name (string: 'DDIMScheduler', 'EulerDiscreteScheduler', 'DPMSolverMultistepScheduler')","scheduler config (dict with num_inference_steps, guidance_scale, eta)"],"output_types":["video tensor generated with specified scheduler"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_3","uri":"capability://data.processing.analysis.batch.video.generation.with.deterministic.seeding","name":"batch video generation with deterministic seeding","description":"Processes multiple text prompts in a single forward pass by batching inputs through the text encoder and diffusion model, with per-sample random seeds enabling reproducible generation. Seed management ensures that identical prompts with identical seeds produce byte-identical video outputs across runs, critical for debugging and A/B testing. Batch processing amortizes model loading overhead and GPU memory allocation across multiple generations.","intents":["Generate multiple video variations from a single prompt by varying seeds","Reproduce specific video outputs for quality assurance or user feedback iteration","Efficiently generate large datasets of synthetic videos for training or evaluation"],"best_for":["Data engineers building synthetic video datasets at scale","Product teams iterating on video quality with reproducible outputs","Researchers conducting controlled experiments with video generation"],"limitations":["Batch size limited by GPU VRAM; typical batch size 1-4 on 24GB GPUs; larger batches require gradient checkpointing or model sharding","Seed reproducibility only guaranteed within same hardware/software stack (CUDA version, PyTorch version, Diffusers version); cross-platform reproducibility not guaranteed","Batch processing adds minimal latency overhead but does not reduce per-sample inference time; total time scales roughly linearly with batch size"],"requires":["Sufficient GPU VRAM for batch_size * model_size; typically 16GB+ for batch_size=2","PyTorch with deterministic mode enabled for reproducibility (torch.manual_seed, torch.cuda.manual_seed)"],"input_types":["list of text prompts (list[str])","list of seeds (list[int]) or single seed (int) for all samples","batch_size parameter (int)"],"output_types":["batched video tensor (shape: [batch_size, frames, channels, height, width])","list of video files (one per prompt)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_4","uri":"capability://data.processing.analysis.safetensors.model.weight.loading.with.integrity.verification","name":"safetensors model weight loading with integrity verification","description":"Loads model weights from safetensors format (a safer, faster alternative to pickle-based PyTorch checkpoints) with built-in integrity checks. Safetensors format includes metadata and checksums, preventing silent corruption and enabling faster deserialization compared to traditional .pt files. The WanPipeline integrates safetensors loading through Hugging Face Hub, automatically downloading and caching model weights with version control.","intents":["Load model weights safely without executing arbitrary Python code (pickle vulnerability mitigation)","Verify model integrity before inference to catch corrupted downloads","Accelerate model loading time for faster startup in production deployments"],"best_for":["Security-conscious teams deploying models in restricted environments","Production systems requiring fast model initialization and reliability","Developers building model serving infrastructure with integrity guarantees"],"limitations":["Safetensors format is read-only; model fine-tuning or weight modification requires conversion back to PyTorch format","Safetensors loading is faster but still I/O-bound; network latency dominates for remote model downloads","Integrity checks catch corruption but do not validate model correctness or output quality"],"requires":["safetensors library (>=0.3.0)","Hugging Face Hub access and authentication for model downloads","Sufficient disk space for model cache (~30GB for 14B model)"],"input_types":["model identifier (str: 'Wan-AI/Wan2.1-T2V-14B-Diffusers')","cache directory path (str, optional)"],"output_types":["loaded model weights (torch.nn.Module)","model metadata (dict with architecture, training info)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_5","uri":"capability://image.visual.guidance.scaled.conditional.generation.with.classifier.free.guidance","name":"guidance-scaled conditional generation with classifier-free guidance","description":"Implements classifier-free guidance (CFG) by training the model with unconditional (null text) examples alongside conditional examples, then interpolating between unconditional and conditional predictions during inference. The guidance_scale parameter controls the interpolation weight: higher values (7-15) increase adherence to text prompts at the cost of reduced diversity and potential artifacts; lower values (1-3) increase diversity but reduce prompt alignment. CFG is applied at each denoising step across all U-Net layers.","intents":["Increase video-text alignment by boosting guidance scale for more faithful prompt adherence","Generate diverse video variations from the same prompt by reducing guidance scale","Balance prompt fidelity and visual quality through guidance tuning"],"best_for":["Content creators seeking tight control over video-prompt alignment","Researchers studying the trade-off between diversity and fidelity in generative models","Teams building interactive tools where users can adjust guidance in real-time"],"limitations":["Guidance scale tuning is empirical; no principled method to select optimal scale for arbitrary prompts","High guidance scales (>15) often produce visual artifacts, oversaturation, or unrealistic textures","CFG requires unconditional training data; models not trained with CFG cannot use this capability","Guidance computation adds ~10-15% latency overhead per denoising step"],"requires":["Model trained with classifier-free guidance (Wan2.1 is trained this way)","Understanding of CFG trade-offs for effective tuning"],"input_types":["guidance_scale parameter (float, typically 1.0-15.0, default ~7.5)"],"output_types":["video tensor with guidance-scaled predictions"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_6","uri":"capability://image.visual.latent.space.video.diffusion.with.temporal.consistency","name":"latent-space video diffusion with temporal consistency","description":"Operates diffusion in a compressed latent space (via a pre-trained VAE encoder) rather than pixel space, reducing memory footprint and enabling longer video generation. The model learns temporal consistency constraints through a temporal attention mechanism that correlates features across video frames, preventing flicker and ensuring smooth motion. Latent diffusion is conditioned on text embeddings via cross-attention, with temporal self-attention layers enforcing frame-to-frame coherence.","intents":["Generate longer video sequences (4-8 seconds) within memory constraints","Reduce inference latency by operating on compressed representations","Ensure temporal smoothness and motion coherence across generated frames"],"best_for":["Teams deploying video generation on resource-constrained hardware (consumer GPUs, edge devices)","Applications requiring temporal coherence and smooth motion (e.g., animation, visual effects)","Researchers studying latent-space video generation and temporal modeling"],"limitations":["Latent-space bottleneck limits fine detail; output resolution capped at ~720p due to VAE decoder limitations","Temporal attention adds computational overhead; inference time scales with video length (frames) and attention window size","Temporal consistency is learned but not guaranteed; complex motion or scene changes can still produce jitter or discontinuities","VAE decoder artifacts may appear as color banding or smoothing in fine details"],"requires":["Pre-trained VAE encoder/decoder (typically included with model)","Temporal attention implementation in diffusion backbone (U-Net with temporal layers)"],"input_types":["text prompt (str)","video length in frames (int, typically 16-48 frames)"],"output_types":["latent video tensor (shape: [batch, frames, latent_channels, latent_height, latent_width])","decoded video tensor in pixel space (shape: [batch, frames, 3, height, width])"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b-diffusers__cap_7","uri":"capability://tool.use.integration.hugging.face.hub.integration.with.model.versioning.and.caching","name":"hugging face hub integration with model versioning and caching","description":"Integrates with Hugging Face Hub for model discovery, download, and caching, enabling one-line model loading via the from_pretrained() API. The integration handles model versioning (revision parameter), automatic cache management, and authentication. Models are cached locally after first download, with subsequent loads reading from cache, eliminating redundant network requests. Hub integration also provides model cards, training details, and community discussions.","intents":["Load Wan2.1 model with a single API call without manual weight download","Switch between model versions (e.g., different quantizations) via revision parameter","Share model weights and training metadata with collaborators via Hub"],"best_for":["Developers building quick prototypes or demos without infrastructure setup","Teams collaborating on model development with version control via Hub","Researchers sharing reproducible video generation pipelines"],"limitations":["First download requires internet connectivity and sufficient bandwidth (~30GB for 14B model)","Cache management is automatic but not configurable; cache directory location is fixed by Hugging Face defaults","Hub authentication required for private models; public models accessible without credentials","Network latency can be significant for users in regions with poor Hub connectivity"],"requires":["Hugging Face Hub account (free) for authentication","Internet connectivity for model download","huggingface_hub library (>=0.16.0)","Sufficient disk space for model cache (~30GB)"],"input_types":["model_id (str: 'Wan-AI/Wan2.1-T2V-14B-Diffusers')","revision (str, optional: 'main', 'fp16', 'int8')","cache_dir (str, optional)"],"output_types":["loaded WanPipeline instance","model metadata from Hub (dict)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (NVIDIA RTX 3090/4090 recommended)","Hugging Face Diffusers library (>=0.21.0)","Safetensors library for model weight loading","Minimum 16GB GPU VRAM; 24GB+ recommended for batch processing","Hugging Face Hub authentication token for model download","Text encoder compatible with both English and Chinese tokenization (typically CLIP or mBERT-based)","Training data with balanced English-Chinese caption pairs (model-specific requirement)","Hugging Face Diffusers library with scheduler implementations","Understanding of diffusion sampling strategies (DDIM vs Euler vs DPM++) for effective tuning"],"failure_modes":["Output video length and resolution constrained by model training data — typically generates short clips (2-8 seconds) at 480p-720p resolution","Temporal coherence degrades with complex motion or long-duration prompts; single-shot generation without frame-by-frame control","Inference latency high (~30-120 seconds per video on consumer GPUs) due to iterative denoising steps across full video tensor","Memory footprint requires 16GB+ VRAM for full model inference; quantization or model sharding needed for smaller devices","Text-to-video alignment quality depends on prompt specificity; vague descriptions produce inconsistent or low-quality outputs","Language support limited to English and Simplified Chinese; Traditional Chinese, Japanese, or other languages require fine-tuning","Cross-lingual performance may degrade for culture-specific concepts or idioms not well-represented in training data","Prompt translation quality affects output; ambiguous or poorly-phrased prompts in either language produce inconsistent results","Scheduler selection requires manual tuning; no automatic scheduler recommendation based on prompt or hardware","Some schedulers (e.g., DPM++ 2M) require more steps for quality, increasing latency; trade-off between speed and quality is not automatically balanced","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.47581732539776544,"quality":0.26,"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":45852,"model_likes":50}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wan-ai--wan2.1-t2v-14b-diffusers","compare_url":"https://unfragile.ai/compare?artifact=wan-ai--wan2.1-t2v-14b-diffusers"}},"signature":"uTpKm3sLBA00DODhc/UfJHWKAlvDyxwXKsAaPBATo9vc0ULsbCM9AG4g6k2wzg2SYBtNXrGRj1ZZuUZGAtJrCA==","signedAt":"2026-06-20T02:22:17.167Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wan-ai--wan2.1-t2v-14b-diffusers","artifact":"https://unfragile.ai/wan-ai--wan2.1-t2v-14b-diffusers","verify":"https://unfragile.ai/api/v1/verify?slug=wan-ai--wan2.1-t2v-14b-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"}}