{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf","slug":"bullerwins--wan2.2-t2v-a14b-gguf","name":"Wan2.2-T2V-A14B-GGUF","type":"model","url":"https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF","page_url":"https://unfragile.ai/bullerwins--wan2.2-t2v-a14b-gguf","categories":["video-generation"],"tags":["gguf","text-to-video","arxiv:2503.20314","arxiv:2309.14509","base_model:Wan-AI/Wan2.2-T2V-A14B","base_model:quantized:Wan-AI/Wan2.2-T2V-A14B","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__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 sequences from natural language text prompts using a diffusion model architecture (Wan2.2 base). The model processes text embeddings through a latent diffusion pipeline with temporal consistency mechanisms to produce coherent multi-frame video outputs. Quantized to GGUF format for efficient local inference without requiring cloud APIs or high-end GPUs.","intents":["Generate short video clips from text descriptions for content creation workflows","Create synthetic video data for training or prototyping without manual filming","Produce visual storyboards or animatics from script text for pre-visualization","Run text-to-video inference locally on consumer hardware without API rate limits"],"best_for":["Independent creators and small studios building video content pipelines","Researchers prototyping diffusion-based video generation without cloud costs","Developers integrating local video synthesis into privacy-sensitive applications","Teams requiring offline-capable video generation without external API dependencies"],"limitations":["GGUF quantization reduces model precision — output quality may degrade compared to full-precision Wan2.2-T2V-A14B","14B parameter model requires significant VRAM (estimated 8-16GB depending on quantization level) for real-time inference","Video length and resolution constrained by training data and memory — typically generates short clips (4-8 seconds) at lower resolutions","Temporal consistency degrades with longer sequences — multi-minute videos require frame-by-frame stitching or external post-processing","No built-in support for multi-prompt sequences or dynamic prompt interpolation across frames","Inference latency on consumer GPUs typically 30-120 seconds per video depending on hardware and output resolution"],"requires":["CUDA-compatible GPU with minimum 8GB VRAM (RTX 3060 or equivalent) or CPU with 32GB+ RAM for CPU inference","GGUF-compatible inference framework (llama.cpp, ollama, or similar)","Python 3.8+ with transformers library or equivalent GGUF loader","Approximately 15-20GB disk space for model weights","Text tokenizer compatible with Wan2.2 (typically CLIP or similar vision-language tokenizer)"],"input_types":["text (natural language prompts, 10-500 tokens typical)","optional: seed value for reproducibility","optional: guidance scale parameter for prompt adherence strength"],"output_types":["video (MP4, WebM, or raw frame sequences)","frame resolution typically 512x512 to 1024x576 depending on quantization","frame rate typically 24-30 fps","duration typically 4-8 seconds (16-240 frames)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__cap_1","uri":"capability://data.processing.analysis.gguf.model.quantization.and.optimization.for.edge.deployment","name":"gguf model quantization and optimization for edge deployment","description":"Provides pre-quantized GGUF format weights enabling inference on resource-constrained hardware without requiring the full 14B parameter model. GGUF (GUFF format) uses bit-level quantization (likely 4-bit or 8-bit) to compress model weights while maintaining functional accuracy through calibration on representative text-to-video prompts. Integrates with llama.cpp and ollama ecosystems for standardized loading and inference.","intents":["Deploy text-to-video generation on laptops or edge devices without high-end GPU requirements","Reduce model size from ~28GB (full precision) to ~8-12GB (quantized) for faster downloads and storage","Run inference offline without internet connectivity or API authentication","Integrate video generation into resource-constrained applications like mobile backends or embedded systems"],"best_for":["Developers building privacy-first applications where video generation cannot leave the device","Teams operating in bandwidth-constrained environments or regions with unreliable cloud connectivity","Researchers benchmarking quantization impact on diffusion model quality","Hobbyists and indie developers with limited hardware budgets"],"limitations":["Quantization introduces 2-8% quality degradation in video coherence and detail fidelity compared to full-precision baseline","GGUF format is primarily optimized for CPU inference — GPU acceleration requires additional framework integration (not all GGUF loaders support CUDA equally)","No dynamic quantization — fixed bit-width means trade-off between model size and quality is baked in at conversion time","Inference speed on CPU remains 5-10x slower than GPU, making real-time generation impractical on most consumer CPUs","Limited tooling for custom quantization — must use pre-quantized weights from community or perform quantization externally"],"requires":["GGUF loader compatible with diffusion models (llama.cpp with diffusion branch, or ollama with custom model config)","8GB+ RAM for CPU inference or 4GB+ VRAM for GPU-accelerated GGUF loading","Model file (~8-12GB) downloaded from HuggingFace or compatible mirror","Python 3.8+ or standalone C++ inference binary"],"input_types":["GGUF binary format model weights","text prompt (tokenized to model's vocabulary)","optional: quantization metadata (bit-width, calibration parameters)"],"output_types":["video frames (raw tensor or encoded video file)","inference timing metrics (latency per diffusion step)","optional: quantization statistics (perplexity, calibration loss)"],"categories":["data-processing-analysis","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__cap_2","uri":"capability://image.visual.temporal.aware.diffusion.sampling.for.video.coherence","name":"temporal-aware diffusion sampling for video coherence","description":"Implements multi-frame diffusion with cross-temporal attention mechanisms that enforce consistency across video frames during the denoising process. Rather than generating each frame independently, the model conditions each frame's generation on neighboring frames' latent representations, reducing flicker and ensuring objects maintain spatial continuity. Uses a scheduler that coordinates noise injection across the temporal dimension to preserve motion dynamics.","intents":["Generate videos with smooth motion and minimal flicker artifacts between frames","Maintain object identity and spatial relationships across the entire video sequence","Control motion speed and direction through prompt engineering or latent space interpolation","Produce videos that don't require post-processing stabilization or optical flow correction"],"best_for":["Content creators requiring production-quality video output without manual stabilization","Researchers studying temporal coherence in diffusion models","Applications where frame-to-frame consistency is critical (e.g., product demos, instructional videos)"],"limitations":["Temporal consistency mechanisms add 20-40% inference latency compared to frame-independent generation","Cross-frame attention requires storing intermediate representations for all frames in memory — limits maximum video length to ~8-16 seconds on consumer hardware","Motion artifacts still occur at scene transitions or with complex camera movements","Temporal consistency is learned from training data — may fail on novel motion patterns not well-represented in training set","No explicit control over motion speed or direction — must be inferred from text prompt"],"requires":["Sufficient VRAM to hold multi-frame attention tensors (8GB+ recommended)","Inference framework supporting cross-attention mechanisms (transformers library or equivalent)","Text prompts that clearly describe motion intent for optimal temporal coherence"],"input_types":["text prompt describing desired motion and scene","optional: seed for reproducible motion patterns","optional: guidance scale for prompt adherence"],"output_types":["video with temporally coherent frames","intermediate latent representations (for analysis or further processing)","attention maps showing cross-frame dependencies (optional)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__cap_3","uri":"capability://text.generation.language.prompt.to.latent.embedding.with.vision.language.alignment","name":"prompt-to-latent embedding with vision-language alignment","description":"Converts natural language text prompts into latent vector representations aligned with video content using a CLIP-like vision-language encoder. The encoder maps text into a shared embedding space with video frame representations, enabling the diffusion model to condition generation on semantic prompt content. Supports multi-token prompts with compositional semantics (e.g., 'a red ball bouncing on a blue surface' correctly grounds color and object relationships).","intents":["Translate natural language descriptions into video generation constraints without manual parameter tuning","Enable compositional prompts that combine multiple objects, actions, and attributes","Support prompt variations and interpolation for exploring the generation space","Provide semantic grounding so the model understands object relationships and spatial arrangements"],"best_for":["Non-technical users who want to describe videos in natural language","Developers building prompt-based video generation APIs","Researchers studying vision-language alignment in generative models"],"limitations":["Prompt understanding is limited to training data distribution — unusual or novel concepts may be misinterpreted","Compositional understanding degrades with complex prompts (>100 tokens) or rare attribute combinations","No explicit control over spatial layout or object positioning — must be inferred from language","Ambiguous prompts may produce inconsistent results across multiple generations","Negation and logical constraints (e.g., 'without X') are less reliable than positive descriptions"],"requires":["Text tokenizer compatible with CLIP or similar vision-language model","Pre-trained vision-language encoder weights (typically bundled with model)","Text input in English or supported language (model training language)"],"input_types":["text prompt (10-500 tokens typical)","optional: prompt weighting or emphasis markers","optional: negative prompt (what NOT to generate)"],"output_types":["latent embedding vector (typically 768-1024 dimensions)","embedding confidence scores (optional)","tokenized prompt representation"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__cap_4","uri":"capability://data.processing.analysis.latent.diffusion.sampling.with.configurable.noise.schedules","name":"latent diffusion sampling with configurable noise schedules","description":"Implements iterative denoising of video latent representations using customizable noise schedules (linear, cosine, exponential) that control the diffusion process trajectory. The sampler progressively removes noise from random initialization over 20-50 timesteps, with each step conditioned on the text embedding and previous frame latents. Supports multiple sampling algorithms (DDPM, DDIM, DPM++) with trade-offs between quality and speed.","intents":["Generate videos with tunable quality-speed trade-offs by adjusting sampling steps","Reproduce specific video outputs using fixed seeds and noise schedules","Experiment with different diffusion trajectories to understand model behavior","Optimize inference latency for real-time or batch processing scenarios"],"best_for":["Developers optimizing inference latency for production deployments","Researchers studying diffusion model sampling strategies","Applications requiring reproducible generation (e.g., testing, validation)"],"limitations":["Fewer sampling steps (10-20) produces faster but lower-quality videos with visible artifacts","More sampling steps (50+) improves quality but increases latency to 2-5 minutes on consumer GPUs","Different noise schedules produce different aesthetic results — no universal 'best' schedule","Sampling algorithm choice (DDPM vs DDIM vs DPM++) requires empirical tuning per use case","Seed reproducibility may not be guaranteed across different hardware or inference frameworks"],"requires":["Inference framework supporting diffusion sampling (transformers, diffusers, or custom implementation)","Configuration parameters: num_inference_steps (10-50), guidance_scale (7.5-15), seed (optional)","Sufficient VRAM for latent tensor storage during sampling loop"],"input_types":["text embedding (from prompt encoder)","initial noise tensor (random or seeded)","sampling configuration (steps, schedule, algorithm)","optional: previous frame latents for temporal conditioning"],"output_types":["denoised latent representation","intermediate latents at each timestep (optional, for analysis)","sampling trajectory metadata (noise levels, guidance values)"],"categories":["data-processing-analysis","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-bullerwins--wan2.2-t2v-a14b-gguf__cap_5","uri":"capability://image.visual.latent.to.video.decoding.with.frame.reconstruction","name":"latent-to-video decoding with frame reconstruction","description":"Converts denoised latent representations back into pixel-space video frames using a learned VAE decoder. The decoder upsamples compressed latent tensors (typically 8-16x compression) through transposed convolutions and attention layers, reconstructing full-resolution video frames. Includes temporal smoothing to ensure decoded frames maintain consistency across the sequence without interpolation artifacts.","intents":["Convert diffusion model outputs (latent space) into viewable video files","Reconstruct high-resolution video from compressed latent representations","Apply post-processing or color correction during decoding without re-running diffusion","Export videos in standard formats (MP4, WebM) for distribution or further editing"],"best_for":["Developers building end-to-end video generation pipelines","Applications requiring high-quality frame reconstruction","Workflows where latent-space manipulation is needed before final rendering"],"limitations":["VAE decoder quality is limited by training data — may introduce artifacts or blur fine details","Decoding adds 10-30% latency to total generation time","Output resolution is fixed by VAE architecture — typically 512x512 or 1024x576, no arbitrary upscaling","Temporal smoothing can reduce motion sharpness — trade-off between flicker reduction and motion clarity","No built-in color grading or post-processing — requires external tools for final polish"],"requires":["Pre-trained VAE decoder weights (bundled with model)","Sufficient VRAM for decoding (2-4GB typical)","Video encoding library (ffmpeg, opencv) for MP4/WebM export","Python 3.8+ with torch or equivalent tensor framework"],"input_types":["latent tensor (compressed video representation, shape [batch, channels, frames, height, width])","optional: decoding scale factor (1.0-2.0 for upscaling)","optional: temporal smoothing strength parameter"],"output_types":["video frames (uint8 RGB, shape [frames, height, width, 3])","encoded video file (MP4, WebM, or raw frame sequence)","optional: intermediate upsampling stages (for debugging)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"low","permissions":["CUDA-compatible GPU with minimum 8GB VRAM (RTX 3060 or equivalent) or CPU with 32GB+ RAM for CPU inference","GGUF-compatible inference framework (llama.cpp, ollama, or similar)","Python 3.8+ with transformers library or equivalent GGUF loader","Approximately 15-20GB disk space for model weights","Text tokenizer compatible with Wan2.2 (typically CLIP or similar vision-language tokenizer)","GGUF loader compatible with diffusion models (llama.cpp with diffusion branch, or ollama with custom model config)","8GB+ RAM for CPU inference or 4GB+ VRAM for GPU-accelerated GGUF loading","Model file (~8-12GB) downloaded from HuggingFace or compatible mirror","Python 3.8+ or standalone C++ inference binary","Sufficient VRAM to hold multi-frame attention tensors (8GB+ recommended)"],"failure_modes":["GGUF quantization reduces model precision — output quality may degrade compared to full-precision Wan2.2-T2V-A14B","14B parameter model requires significant VRAM (estimated 8-16GB depending on quantization level) for real-time inference","Video length and resolution constrained by training data and memory — typically generates short clips (4-8 seconds) at lower resolutions","Temporal consistency degrades with longer sequences — multi-minute videos require frame-by-frame stitching or external post-processing","No built-in support for multi-prompt sequences or dynamic prompt interpolation across frames","Inference latency on consumer GPUs typically 30-120 seconds per video depending on hardware and output resolution","Quantization introduces 2-8% quality degradation in video coherence and detail fidelity compared to full-precision baseline","GGUF format is primarily optimized for CPU inference — GPU acceleration requires additional framework integration (not all GGUF loaders support CUDA equally)","No dynamic quantization — fixed bit-width means trade-off between model size and quality is baked in at conversion time","Inference speed on CPU remains 5-10x slower than GPU, making real-time generation impractical on most consumer CPUs","builder identity is not verified yet","no observed match outcomes 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