{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","slug":"fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","name":"FastWan2.2-TI2V-5B-FullAttn-Diffusers","type":"model","url":"https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers","page_url":"https://unfragile.ai/fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","categories":["video-generation"],"tags":["diffusers","safetensors","text-to-video","arxiv:2505.13389","arxiv:2502.04507","license:apache-2.0","diffusers:WanDMDPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-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 diffusion model architecture (WanDMDPipeline) that iteratively denoises latent representations over multiple timesteps. The model uses a 5B parameter transformer backbone with full attention mechanisms to condition video generation on text embeddings, producing temporally coherent video sequences at inference time through the diffusers library's standardized pipeline interface.","intents":["Generate short video clips from text descriptions for content creation or prototyping","Create visual demonstrations or animations from written specifications without manual video editing","Batch-generate multiple video variations from different text prompts for A/B testing or exploration","Integrate text-to-video generation into applications via the HuggingFace diffusers API"],"best_for":["Content creators and video producers prototyping ideas before production","AI/ML engineers building video generation pipelines or multimodal applications","Researchers experimenting with diffusion-based video synthesis architectures","Teams deploying open-source video generation without commercial licensing constraints"],"limitations":["5B parameter model limits output resolution and temporal length compared to larger proprietary models (likely 480p-720p, <10 seconds)","Full attention mechanisms scale quadratically with sequence length, creating memory bottlenecks on consumer GPUs for longer videos","Inference latency likely 30-120 seconds per video on standard hardware due to iterative denoising steps across timesteps","No built-in motion control, camera movement specification, or fine-grained temporal editing after generation","Quality and coherence degrade significantly for complex multi-object scenes or specific visual styles not well-represented in training data"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ for GPU acceleration (CPU inference impractical)","diffusers library 0.25.0+","Minimum 8GB VRAM for inference (16GB+ recommended for batch processing)","HuggingFace Hub access and model weights (~10-15GB disk space)"],"input_types":["text (natural language prompt, typically 10-100 tokens)","optional: negative prompts (text describing unwanted content)","optional: guidance scale and inference step parameters (numeric)"],"output_types":["video (MP4 or raw frame tensor, typically 24-30fps, 480p-720p resolution, 4-10 second duration)","latent representations (intermediate diffusion states for analysis or further processing)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers__cap_1","uri":"capability://tool.use.integration.diffusers.compatible.pipeline.integration.for.video.synthesis","name":"diffusers-compatible pipeline integration for video synthesis","description":"Exposes video generation through the HuggingFace diffusers library's standardized WanDMDPipeline interface, enabling drop-in compatibility with existing diffusion workflows, safety checkers, and optimization techniques (e.g., attention slicing, memory-efficient attention, quantization). The pipeline abstracts away low-level denoising loop management and provides consistent APIs for prompt encoding, latent initialization, and output decoding across different hardware backends.","intents":["Integrate text-to-video generation into existing diffusers-based applications without custom pipeline code","Apply safety filters, watermarking, or post-processing through diffusers' modular safety checker architecture","Optimize inference latency using diffusers' built-in techniques (xFormers attention, quantization, compilation)","Combine text-to-video with other diffusers models (e.g., image upscaling, inpainting) in multi-stage pipelines"],"best_for":["ML engineers already invested in diffusers ecosystem (Stable Diffusion, ControlNet users)","Teams building production video generation services requiring standardized pipeline abstractions","Researchers comparing diffusion architectures using consistent evaluation harnesses"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per inference call due to Python-level orchestration","Limited customization of internal denoising schedules without forking the pipeline class","Safety checkers and post-processing hooks may not be optimized for video-specific content (designed for images)","Requires understanding of diffusers' callback and hook system for advanced use cases"],"requires":["diffusers>=0.25.0","transformers>=4.30.0 (for text encoding)","torch>=2.0.0","safetensors library for model weight loading"],"input_types":["text prompts (string)","pipeline configuration parameters (height, width, num_inference_steps, guidance_scale)"],"output_types":["PIL Image or torch.Tensor (video frames as tensor or image sequence)","optional: latent tensors for downstream processing"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers__cap_2","uri":"capability://data.processing.analysis.safetensors.based.model.weight.loading.with.integrity.verification","name":"safetensors-based model weight loading with integrity verification","description":"Loads model weights using the safetensors format, which provides memory-safe deserialization with built-in integrity checks and zero-copy tensor loading on compatible hardware. This approach prevents arbitrary code execution during model loading (vs. pickle-based PyTorch .pt files) and enables fast parallel weight loading across multiple devices, with automatic dtype conversion and device placement handled by the diffusers loader.","intents":["Load model weights safely without risk of code injection or deserialization exploits","Reduce model loading time through zero-copy tensor mapping and parallel I/O","Verify model integrity and detect corrupted weights before inference","Deploy models in restricted environments where pickle deserialization is disabled"],"best_for":["Production systems handling untrusted model sources from HuggingFace Hub","Security-conscious teams requiring artifact provenance and integrity verification","Large-scale inference services where model loading latency impacts throughput"],"limitations":["safetensors format requires explicit conversion from legacy .pt checkpoints (one-time cost)","Some older custom model architectures may not have safetensors equivalents available","Zero-copy loading only works on systems with mmap support; falls back to standard loading on others","Integrity checks catch corruption but don't verify model semantic correctness or training provenance"],"requires":["safetensors>=0.3.0","torch>=1.12.0","diffusers>=0.21.0 (for automatic safetensors support)"],"input_types":["safetensors model files (.safetensors extension)","optional: device specification (cuda, cpu, mps)"],"output_types":["loaded model state dict in memory","integrity verification status (pass/fail with checksum)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers__cap_3","uri":"capability://image.visual.full.attention.transformer.conditioning.for.temporal.video.coherence","name":"full-attention transformer conditioning for temporal video coherence","description":"Uses full (dense) attention mechanisms across all transformer layers in the text conditioning pathway, allowing every token in the text prompt to attend to every other token and every video frame to attend to every other frame in the latent space. This architectural choice prioritizes semantic coherence and temporal consistency over computational efficiency, enabling the model to maintain narrative and visual continuity across longer video sequences by explicitly modeling long-range dependencies in both text and video latent dimensions.","intents":["Generate videos with strong temporal coherence and consistent object/character identity across frames","Ensure text prompts with complex dependencies (e.g., 'the red ball bounces off the blue wall') are properly understood","Maintain visual style and lighting consistency throughout generated video sequences","Reduce temporal flickering and jitter artifacts common in sparse-attention video models"],"best_for":["Applications requiring high temporal coherence (character animation, product demos, narrative video)","Scenarios with complex multi-clause text prompts describing intricate scene dynamics","Teams with sufficient GPU memory and willing to accept longer inference times for quality"],"limitations":["Full attention scales O(n²) in memory and compute, limiting video length to ~4-10 seconds at typical resolutions","Inference latency 2-4x higher than sparse/linear attention alternatives due to quadratic complexity","Requires 16GB+ VRAM for typical batch sizes; impractical on consumer GPUs for longer videos","No adaptive attention pruning or dynamic sparsification to reduce compute on simple scenes","Attention weights become increasingly diluted with longer sequences, potentially degrading long-range coherence"],"requires":["PyTorch 2.0+ with CUDA 11.8+ for efficient attention kernels","16GB+ VRAM for inference (32GB+ for batch processing)","xFormers library optional but recommended for memory-efficient attention implementation"],"input_types":["text prompts (tokenized via CLIP or similar encoder)","video latent tensors (from VAE encoder)"],"output_types":["attended feature maps with full cross-modal dependencies","attention weight matrices (for interpretability/visualization)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers__cap_4","uri":"capability://image.visual.latent.diffusion.based.video.frame.synthesis.with.iterative.denoising","name":"latent diffusion-based video frame synthesis with iterative denoising","description":"Generates video by iteratively denoising random noise in a learned latent space over multiple timesteps (typically 20-50 steps), conditioned on text embeddings. Each denoising step applies a UNet-based noise prediction network that gradually refines the latent representation toward the target video distribution. The process operates in compressed latent space (via VAE encoder/decoder) rather than pixel space, reducing memory requirements and enabling faster inference compared to pixel-space diffusion while maintaining visual quality through learned latent representations.","intents":["Generate diverse video variations from the same text prompt through stochastic sampling","Control generation quality vs. speed trade-off by adjusting inference step count","Implement classifier-free guidance to strengthen text-video alignment and reduce unconditional artifacts","Enable iterative refinement workflows where users can regenerate specific frames or adjust prompts"],"best_for":["Applications requiring diverse video generation (multiple takes, variations for A/B testing)","Scenarios where inference latency is acceptable (batch processing, offline generation)","Teams building iterative creative tools where users refine outputs through multiple generations"],"limitations":["Inference latency scales linearly with denoising steps (20-50 steps = 30-120 seconds on typical hardware)","Stochastic sampling introduces variability; identical prompts produce different videos (no deterministic mode without seed control)","Latent space compression via VAE introduces artifacts and limits fine detail preservation","Classifier-free guidance requires training with unconditional samples, increasing training complexity","No direct control over specific frame content or temporal dynamics without prompt engineering"],"requires":["PyTorch 2.0+","Trained VAE encoder/decoder for latent space compression","Text encoder (CLIP or similar) for prompt embedding","UNet noise prediction network (5B parameters in this model)"],"input_types":["text prompts (string)","random seed (for reproducibility)","guidance scale (float, typically 7.5-15.0)","num_inference_steps (int, typically 20-50)"],"output_types":["video frames as tensor or PIL Images","intermediate latent representations (for analysis)","optional: attention maps for interpretability"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ for GPU acceleration (CPU inference impractical)","diffusers library 0.25.0+","Minimum 8GB VRAM for inference (16GB+ recommended for batch processing)","HuggingFace Hub access and model weights (~10-15GB disk space)","diffusers>=0.25.0","transformers>=4.30.0 (for text encoding)","torch>=2.0.0","safetensors library for model weight loading","safetensors>=0.3.0"],"failure_modes":["5B parameter model limits output resolution and temporal length compared to larger proprietary models (likely 480p-720p, <10 seconds)","Full attention mechanisms scale quadratically with sequence length, creating memory bottlenecks on consumer GPUs for longer videos","Inference latency likely 30-120 seconds per video on standard hardware due to iterative denoising steps across timesteps","No built-in motion control, camera movement specification, or fine-grained temporal editing after generation","Quality and coherence degrade significantly for complex multi-object scenes or specific visual styles not well-represented in training data","Pipeline abstraction adds ~50-100ms overhead per inference call due to Python-level orchestration","Limited customization of internal denoising schedules without forking the pipeline class","Safety checkers and post-processing hooks may not be optimized for video-specific content (designed for images)","Requires understanding of diffusers' callback and hook system for advanced use cases","safetensors format requires explicit conversion from legacy .pt checkpoints (one-time cost)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.48160447327646577,"quality":0.35,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:52.093Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":46362,"model_likes":63}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","compare_url":"https://unfragile.ai/compare?artifact=fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers"}},"signature":"8odE2b0WOlN8vSfszFdLSBnuCsfucMleQD8ppDbBmuIOTcMKDIDOpc/S8mZv222rS6k+W++zou/E0PujFSKPDA==","signedAt":"2026-06-20T13:22:59.231Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","artifact":"https://unfragile.ai/fastvideo--fastwan2.2-ti2v-5b-fullattn-diffusers","verify":"https://unfragile.ai/api/v1/verify?slug=fastvideo--fastwan2.2-ti2v-5b-fullattn-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"}}