{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-wan-ai--wan2.1-t2v-14b","slug":"wan-ai--wan2.1-t2v-14b","name":"Wan2.1-T2V-14B","type":"model","url":"https://huggingface.co/Wan-AI/Wan2.1-T2V-14B","page_url":"https://unfragile.ai/wan-ai--wan2.1-t2v-14b","categories":["video-generation"],"tags":["diffusers","safetensors","t2v","video generation","text-to-video","en","zh","license:apache-2.0","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__cap_0","uri":"capability://image.visual.text.conditioned.video.generation.with.diffusion.based.synthesis","name":"text-conditioned video generation with diffusion-based synthesis","description":"Generates short-form videos (typically 4-8 seconds at 24fps) from natural language text prompts using a latent diffusion architecture. The model operates in a compressed video latent space rather than pixel space, enabling efficient generation through iterative denoising steps guided by CLIP-based text embeddings. Supports both English and Chinese prompts with cross-lingual semantic understanding through shared embedding space.","intents":["Generate short promotional videos or social media clips from text descriptions without manual filming","Create visual storyboards or concept videos for creative brainstorming and prototyping","Batch-generate diverse video variations from a single text prompt for A/B testing content","Produce placeholder or reference videos for video editing workflows before final production"],"best_for":["Content creators and marketers generating social media assets at scale","AI/ML researchers prototyping video generation pipelines and fine-tuning approaches","Indie game developers and VFX artists creating placeholder animations","Teams building video synthesis APIs or multimodal applications"],"limitations":["Output resolution capped at 720p with 4-8 second duration; longer or higher-res videos require external upscaling or stitching","Temporal consistency degrades with complex motion or scene changes; simple, coherent scenes perform best","Inference latency ~30-60 seconds per video on consumer GPUs (A100: ~15-20s); requires GPU with 24GB+ VRAM for batch generation","No fine-grained control over camera movement, object trajectories, or frame-by-frame editing; text prompts map to holistic scene generation","Multilingual support limited to English and Simplified Chinese; other languages fall back to English understanding with degraded quality"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (minimum 24GB VRAM recommended; 40GB+ for batch inference)","Diffusers library 0.21.0+","HuggingFace transformers 4.30.0+","Model weights (~14B parameters, ~28GB disk space in safetensors format)","Optional: ffmpeg for video encoding/decoding and frame extraction"],"input_types":["text (natural language prompts, 10-150 tokens optimal)","optional: seed (integer for reproducibility)","optional: guidance_scale (float 7.5-15.0 for prompt adherence strength)","optional: num_inference_steps (integer 20-50 for quality/speed tradeoff)"],"output_types":["video (MP4 H.264 codec, 24fps, 720p or configurable resolution)","latent tensors (intermediate diffusion outputs for inspection or further processing)","frame sequences (optional PIL Image list for frame-by-frame analysis)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_1","uri":"capability://image.visual.prompt.guided.iterative.denoising.with.classifier.free.guidance","name":"prompt-guided iterative denoising with classifier-free guidance","description":"Implements classifier-free guidance (CFG) mechanism where the diffusion model is conditioned on text embeddings during the reverse diffusion process, allowing dynamic control over prompt adherence strength via a guidance scale parameter. The model performs iterative denoising steps (typically 20-50) in latent space, progressively refining noise into coherent video frames while maintaining semantic alignment with the input text prompt.","intents":["Fine-tune the balance between prompt fidelity and creative variation by adjusting guidance scale","Generate multiple video variations with different guidance strengths to explore prompt interpretation","Reproduce exact video outputs by fixing random seeds and guidance parameters","Debug prompt understanding by comparing outputs across guidance scale ranges"],"best_for":["Researchers studying prompt-to-video alignment and guidance mechanisms","Developers building interactive video generation UIs with real-time parameter tuning","Content creators iterating on prompts to achieve specific visual styles"],"limitations":["Higher guidance scales (>15) increase artifacts and temporal flickering; optimal range 7.5-12.0","Guidance scale does not enable semantic negation (e.g., 'no red objects'); negative prompts not supported","Inference time scales linearly with num_inference_steps; doubling steps ~doubles latency","Deterministic reproduction requires identical hardware, PyTorch version, and CUDA settings due to floating-point non-determinism"],"requires":["Understanding of diffusion model mechanics and guidance scale tuning","GPU with sufficient VRAM to hold model weights + intermediate activations (~28GB+ for 14B model)","Diffusers library with CFG implementation (0.21.0+)"],"input_types":["text prompt (string)","guidance_scale (float, typical range 7.5-15.0)","num_inference_steps (integer, typical range 20-50)","seed (integer, optional for reproducibility)"],"output_types":["video tensor (latent space, shape [T, C, H, W])","decoded video (pixel space, MP4 or frame sequence)","intermediate noise predictions (optional for analysis)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_2","uri":"capability://text.generation.language.multilingual.text.embedding.and.cross.lingual.prompt.understanding","name":"multilingual text embedding and cross-lingual prompt understanding","description":"Encodes text prompts in English and Simplified Chinese into a shared semantic embedding space using a CLIP-based text encoder, enabling the diffusion model to understand prompts across both languages without language-specific branches. The encoder maps text to a fixed-dimensional vector that conditions the video generation process, with semantic similarity preserved across languages through joint training on aligned multilingual corpora.","intents":["Generate videos from Chinese-language prompts with equivalent quality to English prompts","Build applications serving global audiences without separate model deployments per language","Mix English and Chinese tokens in prompts for hybrid-language creative direction"],"best_for":["Teams building video generation products for Chinese-speaking markets","Multilingual content platforms requiring single-model deployment","Researchers studying cross-lingual semantic alignment in generative models"],"limitations":["Only English and Simplified Chinese supported; Traditional Chinese, Japanese, Korean, and other languages fall back to English understanding with degraded quality","Cross-lingual prompt mixing (e.g., 'a 红色 car') may produce unpredictable results due to tokenizer boundary effects","Semantic alignment quality varies by domain; technical or domain-specific terms may not transfer equally across languages","No explicit language detection; model assumes prompt is primarily one language"],"requires":["CLIP text encoder compatible with multilingual tokenization (typically supports 77-token context window)","Tokenizer supporting both English (BPE) and Chinese (character-level or subword) vocabularies","Training data with aligned English-Chinese video-text pairs (proprietary to Wan-AI)"],"input_types":["text prompt in English (string, UTF-8)","text prompt in Simplified Chinese (string, UTF-8)","mixed-language prompts (not officially supported but may work)"],"output_types":["text embedding (vector, typically 768-1024 dimensions)","video conditioned on embedding"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_3","uri":"capability://image.visual.latent.space.video.vae.encoding.and.decoding","name":"latent-space video vae encoding and decoding","description":"Compresses video frames into a learned latent representation using a video VAE (Variational Autoencoder), reducing spatial and temporal dimensions by factors of 4-8x. The diffusion process operates in this compressed latent space rather than pixel space, enabling efficient generation. After diffusion, a VAE decoder reconstructs pixel-space video from latent tensors, with learned perceptual loss ensuring visual quality despite compression.","intents":["Reduce inference latency and VRAM requirements by operating in compressed latent space","Enable batch video generation on consumer GPUs by reducing memory footprint","Preserve temporal coherence through VAE's learned temporal compression"],"best_for":["Developers deploying video generation on resource-constrained hardware (consumer GPUs, edge devices)","Teams requiring low-latency inference for real-time or interactive applications","Researchers studying latent-space generative models and VAE design"],"limitations":["VAE compression introduces perceptual artifacts (blurriness, color shifts) especially in high-frequency details; output quality lower than pixel-space diffusion","Latent space dimensionality fixed during training; cannot adjust compression ratio post-hoc","VAE decoder has fixed upsampling schedule; cannot generate arbitrary resolutions (e.g., 1080p requires retraining)","Temporal compression may lose fine-grained motion details; fast motion or flicker artifacts more pronounced than pixel-space approaches"],"requires":["Pre-trained video VAE encoder/decoder (included in model weights)","Understanding of latent-space diffusion mechanics","GPU with sufficient VRAM for latent tensors (~4-6GB for 720p video vs 24GB+ for pixel-space)"],"input_types":["text prompt (string)","optional: latent seed (for deterministic latent initialization)"],"output_types":["latent tensor (compressed video representation, shape [T, C_latent, H_latent, W_latent])","pixel-space video (decoded, shape [T, 3, 720, 1280] or configurable)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_4","uri":"capability://image.visual.batch.video.generation.with.seed.based.reproducibility","name":"batch video generation with seed-based reproducibility","description":"Generates multiple videos in parallel from a single prompt or prompt batch, with deterministic output reproducibility via fixed random seeds. The model accepts batch-size parameters and seed arrays, enabling efficient GPU utilization for generating video variations or A/B test sets. Seed-based reproducibility allows exact recreation of outputs across runs and hardware (with caveats for floating-point non-determinism).","intents":["Generate multiple video variations from one prompt for content selection and A/B testing","Reproduce exact video outputs for debugging or quality assurance","Maximize GPU utilization by batching multiple generation requests","Create deterministic video datasets for model evaluation or benchmarking"],"best_for":["Content creators generating multiple takes of the same concept","Teams building video generation APIs with batch processing endpoints","Researchers creating reproducible evaluation datasets"],"limitations":["Batch size limited by GPU VRAM; typical max batch size 2-4 on 24GB GPUs, 8-16 on 40GB+ GPUs","Reproducibility not guaranteed across different PyTorch versions, CUDA versions, or hardware architectures due to floating-point non-determinism","Seed-based variation limited to noise initialization; prompt semantics remain identical across seeds","No built-in seed scheduling or curriculum learning; all seeds treated equally"],"requires":["GPU with sufficient VRAM for batch inference (24GB+ for batch_size=2, 40GB+ for batch_size=4+)","PyTorch with deterministic algorithms enabled (torch.use_deterministic_algorithms(True))","Diffusers library with batch generation support (0.21.0+)"],"input_types":["prompt (string or list of strings for batch)","batch_size (integer, 1-16 depending on GPU)","seeds (integer or list of integers)","guidance_scale (float or list of floats for per-sample guidance)"],"output_types":["video batch (list of MP4 files or tensor batch)","metadata (seeds, prompts, generation parameters per video)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_5","uri":"capability://image.visual.inference.optimization.with.mixed.precision.and.memory.efficient.attention","name":"inference optimization with mixed-precision and memory-efficient attention","description":"Optimizes inference through mixed-precision computation (FP16/BF16 for activations, FP32 for stability-critical operations) and memory-efficient attention mechanisms (e.g., flash attention or grouped query attention). These techniques reduce VRAM footprint and latency while maintaining output quality, enabling deployment on consumer-grade GPUs and faster generation on high-end hardware.","intents":["Run video generation on GPUs with 24GB VRAM (e.g., RTX 4090, A5000) instead of requiring 40GB+","Reduce inference latency from 60s to 20-30s per video on consumer hardware","Deploy on edge devices or cloud instances with cost-effective GPU options"],"best_for":["Developers deploying video generation on cost-constrained infrastructure","Teams requiring sub-30s inference latency for interactive applications","Researchers optimizing diffusion model inference efficiency"],"limitations":["Mixed-precision may introduce subtle numerical instabilities in edge cases; requires validation per use case","Memory-efficient attention (e.g., flash attention) requires specific GPU architectures (Ampere+); older GPUs fall back to standard attention with higher latency","Optimization trades off some output quality for speed; imperceptible to humans but measurable in metrics (LPIPS, FID)","Quantization to INT8 not supported; FP16/BF16 is minimum precision"],"requires":["GPU with mixed-precision support (NVIDIA Ampere/Ada or AMD RDNA2+)","PyTorch 2.0+ with torch.cuda.amp or torch.autocast support","Optional: flash-attention library for further optimization (pip install flash-attn)","CUDA 11.8+ or compatible"],"input_types":["enable_attention_slicing (boolean, trades memory for speed)","enable_memory_efficient_attention (boolean, requires compatible GPU)","dtype (torch.float16, torch.bfloat16, or torch.float32)"],"output_types":["video (same quality as full-precision, ~5-10% faster, ~20-30% lower VRAM)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_6","uri":"capability://tool.use.integration.safetensors.model.format.loading.with.integrity.verification","name":"safetensors model format loading with integrity verification","description":"Loads model weights from safetensors format (a secure, efficient serialization format) instead of pickle, enabling fast loading with built-in integrity checks via SHA256 hashing. Safetensors format prevents arbitrary code execution during deserialization and provides faster I/O compared to PyTorch's default .pt format, especially on network storage or cloud object stores.","intents":["Load model weights safely without risk of arbitrary code execution from untrusted sources","Verify model integrity via SHA256 hashes before inference","Reduce model loading time from 30-60s to 5-10s on network storage"],"best_for":["Teams deploying models in security-sensitive environments (healthcare, finance)","Developers using untrusted model sources and requiring integrity verification","Infrastructure teams optimizing model loading latency on cloud storage"],"limitations":["Safetensors format not compatible with older PyTorch versions (<1.13); requires modern PyTorch","SHA256 verification adds ~1-2s overhead per model load; can be disabled if speed is critical","Safetensors format larger than compressed .pt files (~5-10% overhead); requires more disk space","No built-in support for partial model loading (e.g., loading only encoder weights); requires custom code"],"requires":["safetensors library (pip install safetensors)","PyTorch 1.13+","Model weights in safetensors format (included in HuggingFace model repo)"],"input_types":["model_id (HuggingFace model identifier, e.g., 'Wan-AI/Wan2.1-T2V-14B')","cache_dir (optional, local directory for cached weights)","verify_hash (boolean, enable/disable SHA256 verification)"],"output_types":["loaded model (PyTorch nn.Module with weights initialized)","verification status (pass/fail for integrity check)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-wan-ai--wan2.1-t2v-14b__cap_7","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.caching.and.auto.download","name":"huggingface hub integration with model caching and auto-download","description":"Integrates with HuggingFace Hub for seamless model discovery, downloading, and caching. The model can be loaded with a single line of code (e.g., `from_pretrained('Wan-AI/Wan2.1-T2V-14B')`) which automatically downloads weights to a local cache directory, manages version control, and handles authentication for private models. Caching prevents redundant downloads across multiple runs.","intents":["Load the model with minimal setup code without manual weight downloading","Share model weights via HuggingFace Hub for easy community access","Manage model versions and updates through HuggingFace's version control","Cache model weights locally to avoid repeated downloads"],"best_for":["Developers building quick prototypes or demos without infrastructure setup","Teams sharing models within organizations via HuggingFace Hub","Researchers distributing models to the community"],"limitations":["Initial download requires 28GB+ disk space and 10-30 minutes on typical internet connections","Cache directory grows unbounded; requires manual cleanup or disk space monitoring","HuggingFace Hub downtime blocks model loading (no offline fallback)","Authentication required for private models; token management adds complexity"],"requires":["huggingface-hub library (pip install huggingface-hub)","Internet connection for initial model download","~30GB free disk space for model cache","Optional: HuggingFace API token for private models (huggingface-cli login)"],"input_types":["model_id (string, e.g., 'Wan-AI/Wan2.1-T2V-14B')","cache_dir (optional, custom cache directory)","token (optional, HuggingFace API token for private models)"],"output_types":["model (loaded diffusers.DiffusionPipeline or similar)","model_info (metadata from HuggingFace Hub: description, tags, downloads)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (minimum 24GB VRAM recommended; 40GB+ for batch inference)","Diffusers library 0.21.0+","HuggingFace transformers 4.30.0+","Model weights (~14B parameters, ~28GB disk space in safetensors format)","Optional: ffmpeg for video encoding/decoding and frame extraction","Understanding of diffusion model mechanics and guidance scale tuning","GPU with sufficient VRAM to hold model weights + intermediate activations (~28GB+ for 14B model)","Diffusers library with CFG implementation (0.21.0+)","CLIP text encoder compatible with multilingual tokenization (typically supports 77-token context window)"],"failure_modes":["Output resolution capped at 720p with 4-8 second duration; longer or higher-res videos require external upscaling or stitching","Temporal consistency degrades with complex motion or scene changes; simple, coherent scenes perform best","Inference latency ~30-60 seconds per video on consumer GPUs (A100: ~15-20s); requires GPU with 24GB+ VRAM for batch generation","No fine-grained control over camera movement, object trajectories, or frame-by-frame editing; text prompts map to holistic scene generation","Multilingual support limited to English and Simplified Chinese; other languages fall back to English understanding with degraded quality","Higher guidance scales (>15) increase artifacts and temporal flickering; optimal range 7.5-12.0","Guidance scale does not enable semantic negation (e.g., 'no red objects'); negative prompts not supported","Inference time scales linearly with num_inference_steps; doubling steps ~doubles latency","Deterministic reproduction requires identical hardware, PyTorch version, and CUDA settings due to floating-point non-determinism","Only English and Simplified Chinese supported; Traditional Chinese, Japanese, Korean, and other languages fall back to English understanding with degraded quality","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5581317117369171,"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":51863,"model_likes":1493}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wan-ai--wan2.1-t2v-14b","compare_url":"https://unfragile.ai/compare?artifact=wan-ai--wan2.1-t2v-14b"}},"signature":"NmkJ4rfg15VDwEhFQaywf6q6BcdpYpnPRemgzRN896ci7k3XyFen+nnsyRVXq5xN537mkkQu8uSerZRatzpcCA==","signedAt":"2026-06-19T21:53:10.974Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wan-ai--wan2.1-t2v-14b","artifact":"https://unfragile.ai/wan-ai--wan2.1-t2v-14b","verify":"https://unfragile.ai/api/v1/verify?slug=wan-ai--wan2.1-t2v-14b","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"}}