{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control","slug":"alibaba-pai--wan2.1-fun-14b-control","name":"Wan2.1-Fun-14B-Control","type":"model","url":"https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control","page_url":"https://unfragile.ai/alibaba-pai--wan2.1-fun-14b-control","categories":["video-generation"],"tags":["videox_fun","diffusers","safetensors","i2v","video","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-alibaba-pai--wan2.1-fun-14b-control__cap_0","uri":"capability://image.visual.text.to.video.generation.with.motion.control","name":"text-to-video generation with motion control","description":"Generates short-form videos from natural language text prompts using a diffusion-based architecture with explicit motion control mechanisms. The model uses a latent diffusion framework operating in compressed video space, enabling efficient generation of temporally coherent video sequences. Motion control is achieved through conditioning mechanisms that allow fine-grained specification of camera movement, object trajectories, and scene dynamics during the generation process.","intents":["Generate short videos from text descriptions for content creation workflows","Create videos with specific motion patterns and camera movements programmatically","Produce consistent video outputs with controllable temporal dynamics","Build video generation pipelines that respect motion constraints and scene composition"],"best_for":["Content creators building automated video production pipelines","AI researchers experimenting with controllable video synthesis","Teams developing video-first applications requiring motion-aware generation","Developers prototyping video generation features without cloud API dependencies"],"limitations":["Output video length and resolution constrained by model training data and VRAM requirements (typical outputs 4-8 seconds at 480p-720p)","Motion control precision depends on prompt engineering and conditioning signal quality; complex multi-object interactions may produce artifacts","Generation latency typically 30-120 seconds per video on consumer GPUs, requiring batch processing optimization for production use","No built-in support for frame-by-frame editing or post-generation refinement; requires external video processing for modifications","Bilingual training (English/Chinese) may introduce language-specific biases in motion interpretation for non-native prompts"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU with minimum 16GB VRAM","Hugging Face Transformers library (4.30+)","Diffusers library (0.21.0+) with safetensors support","14GB+ disk space for model weights (safetensors format)","Optional: FFmpeg for video post-processing and format conversion"],"input_types":["text (natural language prompts in English or Chinese)","motion control specifications (optional: trajectory maps, camera movement vectors, optical flow hints)","seed values for reproducibility","generation parameters (num_inference_steps, guidance_scale, motion_scale)"],"output_types":["video files (MP4, WebM formats via diffusers pipeline)","raw tensor outputs (latent space representations)","frame sequences (optional: individual frame extraction)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_1","uri":"capability://image.visual.image.to.video.temporal.extension","name":"image-to-video temporal extension","description":"Extends static images into coherent video sequences by predicting plausible temporal continuations using the diffusion model's learned motion priors. The model conditions on the input image as the first frame and iteratively generates subsequent frames while maintaining visual consistency and respecting motion control parameters. This leverages the model's understanding of natural motion patterns learned during training on video datasets.","intents":["Animate static images with realistic motion for social media content","Create video previews from product photography or artwork","Generate temporal context for image-based storytelling applications","Extend short clips or keyframes into longer video sequences"],"best_for":["E-commerce platforms converting product images to demo videos","Social media content creators automating video production from image libraries","Game developers generating in-engine cinematics from concept art","Researchers studying temporal coherence in generative models"],"limitations":["Motion prediction quality degrades for images with ambiguous or complex scenes; model may hallucinate unrealistic motion patterns","Generated motion is constrained to patterns seen in training data; novel or unusual motion types produce artifacts","Requires high-quality input images; low-resolution or heavily compressed inputs result in degraded video quality","No explicit control over motion direction or intensity without additional conditioning signals; relies on implicit prompt guidance"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+","Diffusers library (0.21.0+) with image loading support","Input image in standard formats (PNG, JPEG, WebP)","16GB+ VRAM for efficient processing"],"input_types":["image files (PNG, JPEG, WebP at 512x512 to 768x768 resolution)","text prompts describing desired motion (optional)","motion control parameters (optional: direction vectors, speed scaling)"],"output_types":["video files with input image as first frame","frame sequences as tensor arrays","optical flow maps (optional: for motion analysis)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_2","uri":"capability://text.generation.language.multilingual.prompt.understanding.and.motion.interpretation","name":"multilingual prompt understanding and motion interpretation","description":"Processes text prompts in English and Chinese to extract semantic intent and motion specifications, using a shared embedding space learned during bilingual training. The model maps natural language descriptions of motion (e.g., 'camera pans left', 'object rotates clockwise') to structured motion control signals that guide the diffusion process. This enables non-English speakers to specify complex motion behaviors without translation overhead.","intents":["Generate videos from Chinese-language prompts without translation preprocessing","Build multilingual video generation APIs serving global audiences","Extract motion intent from prompts in either language for consistent behavior","Create region-specific content generation pipelines with native language support"],"best_for":["International teams building video generation products for Asian markets","Content creators working in Chinese-speaking regions","Multilingual AI applications requiring video synthesis capabilities","Researchers studying cross-lingual motion semantics in generative models"],"limitations":["Bilingual training may introduce language-specific biases; motion interpretation varies between English and Chinese prompts for identical concepts","Limited to English and Chinese; other languages fall back to English-only behavior with degraded performance","Prompt ambiguity in either language can result in unpredictable motion; requires careful prompt engineering for consistent results","No explicit language detection; mixed-language prompts may produce undefined behavior"],"requires":["Python 3.8+","Text input in UTF-8 encoding for Chinese character support","Tokenizer compatible with both English and Chinese (included in model)","No additional language model dependencies; motion interpretation is end-to-end"],"input_types":["text prompts in English or Chinese","mixed-language prompts (behavior undefined)","structured motion descriptions in either language"],"output_types":["motion control tensors","semantic embeddings","video outputs reflecting language-specific motion interpretation"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_3","uri":"capability://image.visual.latent.space.diffusion.with.efficient.vram.utilization","name":"latent-space diffusion with efficient vram utilization","description":"Operates diffusion process in compressed latent space rather than pixel space, reducing memory footprint and computation time by 4-8x compared to pixel-space diffusion. The model uses a pre-trained VAE encoder to compress video frames into low-dimensional latent representations, performs iterative denoising in this compressed space, and decodes the final latent sequence back to video frames. This architectural choice enables generation on consumer-grade GPUs while maintaining visual quality.","intents":["Generate videos on consumer GPUs with 16GB VRAM without cloud infrastructure","Reduce generation latency for interactive or batch video production workflows","Deploy video generation models on edge devices or resource-constrained environments","Optimize inference cost by reducing computational requirements per video"],"best_for":["Individual developers and small teams without access to enterprise GPU clusters","Startups building video generation features with cost constraints","On-device or edge deployment scenarios requiring local inference","Research teams studying efficient diffusion architectures"],"limitations":["Latent space compression introduces quantization artifacts; fine details may be lost compared to pixel-space diffusion","VAE decoder quality bottleneck; artifacts in VAE can propagate to final video output","Latent space interpretation is opaque; debugging generation failures requires understanding VAE behavior","Batch size is still constrained by VRAM; typical batch size 1-2 on 16GB GPUs even with latent-space optimization"],"requires":["GPU with minimum 16GB VRAM (RTX 3060 Ti, RTX 4060 Ti, or equivalent)","PyTorch 2.0+ with CUDA support","Diffusers library with VAE integration","Optional: xFormers library for attention optimization (reduces VRAM by additional 20-30%)"],"input_types":["text prompts","image inputs (converted to latent space internally)","motion control parameters"],"output_types":["video files (decoded from latent space)","latent tensors (optional: for analysis or fine-tuning)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_4","uri":"capability://image.visual.reproducible.video.generation.with.seed.control","name":"reproducible video generation with seed control","description":"Provides deterministic video generation through explicit seed parameter control, enabling reproducible outputs for testing, debugging, and content iteration. The model's random number generation is seeded at initialization, allowing developers to regenerate identical videos given the same prompt, seed, and generation parameters. This is critical for production workflows requiring consistency and version control.","intents":["Regenerate specific videos for quality assurance and debugging","Build deterministic video generation pipelines for content workflows","Version control video outputs by associating them with seed values","Enable A/B testing of prompts with controlled randomness"],"best_for":["Production video generation systems requiring reproducibility","QA teams testing video generation quality across model versions","Content creators iterating on prompts with consistent baselines","Researchers studying model behavior across seed variations"],"limitations":["Seed reproducibility is only guaranteed within the same PyTorch version and CUDA version; cross-version reproducibility not guaranteed","GPU-specific randomness variations may occur with different hardware; same seed on different GPUs may produce slightly different outputs","Seed control does not guarantee identical outputs across different diffusers library versions","Floating-point precision differences can accumulate, causing minor variations even with identical seeds"],"requires":["Python 3.8+","PyTorch 2.0+ (specific version for reproducibility)","CUDA 11.8+ (specific version for reproducibility)","Diffusers library (specific version for reproducibility)"],"input_types":["seed value (integer, typically 0-2^32)","text prompts","generation parameters"],"output_types":["deterministic video outputs","metadata including seed value for tracking"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_5","uri":"capability://automation.workflow.batch.video.generation.with.pipeline.optimization","name":"batch video generation with pipeline optimization","description":"Processes multiple video generation requests sequentially or in optimized batches through the diffusion pipeline, with support for parameter variation and efficient memory management. The implementation uses diffusers' pipeline abstraction to handle batching, caching, and attention optimization, allowing developers to generate multiple videos with different prompts or parameters without reloading model weights. Supports both synchronous and asynchronous generation patterns.","intents":["Generate multiple videos in production workflows without reloading models between requests","Optimize GPU utilization by batching similar generation requests","Build scalable video generation services handling concurrent requests","Implement efficient content production pipelines with parameter sweeps"],"best_for":["Content production platforms generating videos at scale","Batch processing systems for video dataset creation","API services handling multiple concurrent generation requests","Research teams conducting parameter sweeps and ablation studies"],"limitations":["Batch size is constrained by VRAM; typical batch size 1-2 on 16GB GPUs even with latent-space optimization","Sequential batching introduces latency; parallel processing requires multiple GPU instances","Memory fragmentation can occur with long-running batch jobs; periodic model reloading may be necessary","No built-in request queuing or load balancing; requires external orchestration for production deployment"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Diffusers library (0.21.0+) with pipeline support","Optional: Ray or similar distributed computing framework for multi-GPU batching"],"input_types":["list of text prompts","list of generation parameters (seeds, guidance scales, motion parameters)","batch configuration (batch size, processing order)"],"output_types":["list of video files","metadata including generation parameters and timing information","optional: structured results with prompt-to-video mapping"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-alibaba-pai--wan2.1-fun-14b-control__cap_6","uri":"capability://data.processing.analysis.safetensors.model.format.support.with.fast.loading","name":"safetensors model format support with fast loading","description":"Uses safetensors format for model weight storage instead of PyTorch's default pickle format, enabling faster model loading, improved security, and better compatibility across frameworks. Safetensors is a binary format optimized for efficient tensor serialization, reducing model loading time from 30-60 seconds to 5-10 seconds on typical hardware. This format also prevents arbitrary code execution during model loading, improving security for untrusted model sources.","intents":["Reduce model initialization overhead in production deployments","Load models securely without risk of code injection from untrusted sources","Enable faster iteration during development and testing","Improve compatibility with non-PyTorch frameworks (JAX, TensorFlow)"],"best_for":["Production systems requiring fast model initialization","Security-conscious deployments handling untrusted model sources","Development workflows with frequent model reloading","Multi-framework environments requiring format compatibility"],"limitations":["Safetensors format is newer; some older tools and frameworks may not support it natively","Model size on disk is slightly larger than optimized pickle formats (typically 2-5% overhead)","Conversion from pickle to safetensors requires one-time processing; existing pickle checkpoints must be converted","No support for custom Python objects in safetensors; model architecture must be defined separately"],"requires":["safetensors library (0.3.0+)","PyTorch 1.13+ (for native safetensors support in newer versions)","Diffusers library (0.21.0+) with safetensors integration"],"input_types":["safetensors model files (.safetensors extension)","model configuration files (JSON format)"],"output_types":["loaded model weights in PyTorch tensor format","model architecture and configuration"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU with minimum 16GB VRAM","Hugging Face Transformers library (4.30+)","Diffusers library (0.21.0+) with safetensors support","14GB+ disk space for model weights (safetensors format)","Optional: FFmpeg for video post-processing and format conversion","PyTorch 2.0+ with CUDA 11.8+","Diffusers library (0.21.0+) with image loading support","Input image in standard formats (PNG, JPEG, WebP)","16GB+ VRAM for efficient processing"],"failure_modes":["Output video length and resolution constrained by model training data and VRAM requirements (typical outputs 4-8 seconds at 480p-720p)","Motion control precision depends on prompt engineering and conditioning signal quality; complex multi-object interactions may produce artifacts","Generation latency typically 30-120 seconds per video on consumer GPUs, requiring batch processing optimization for production use","No built-in support for frame-by-frame editing or post-generation refinement; requires external video processing for modifications","Bilingual training (English/Chinese) may introduce language-specific biases in motion interpretation for non-native prompts","Motion prediction quality degrades for images with ambiguous or complex scenes; model may hallucinate unrealistic motion patterns","Generated motion is constrained to patterns seen in training data; novel or unusual motion types produce artifacts","Requires high-quality input images; low-resolution or heavily compressed inputs result in degraded video quality","No explicit control over motion direction or intensity without additional conditioning signals; relies on implicit prompt guidance","Bilingual training may introduce language-specific biases; motion interpretation varies between English and Chinese prompts for identical concepts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3836728733363136,"quality":0.24,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-04-22T08:08:18.365Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":11751,"model_likes":56}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alibaba-pai--wan2.1-fun-14b-control","compare_url":"https://unfragile.ai/compare?artifact=alibaba-pai--wan2.1-fun-14b-control"}},"signature":"1iBW1EjjZntA88iwBgSdau39Z2Vq5MHgPXlezVhGgnMsJBHVYEokzdBb/aXxl3zF8SUSqwHsWu3eOcgAvdunCg==","signedAt":"2026-06-21T21:30:02.562Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alibaba-pai--wan2.1-fun-14b-control","artifact":"https://unfragile.ai/alibaba-pai--wan2.1-fun-14b-control","verify":"https://unfragile.ai/api/v1/verify?slug=alibaba-pai--wan2.1-fun-14b-control","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"}}