{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf","slug":"quantstack--wan2.2-t2v-a14b-gguf","name":"Wan2.2-T2V-A14B-GGUF","type":"model","url":"https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF","page_url":"https://unfragile.ai/quantstack--wan2.2-t2v-a14b-gguf","categories":["video-generation"],"tags":["gguf","t2v","text-to-video","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-quantstack--wan2.2-t2v-a14b-gguf__cap_0","uri":"capability://image.visual.text.to.video.generation.with.quantized.inference","name":"text-to-video generation with quantized inference","description":"Generates short-form videos from natural language text prompts using a 14-billion parameter diffusion-based architecture optimized through GGUF quantization for CPU/GPU inference. The model uses a text encoder to embed prompts, a latent video diffusion process to iteratively denoise video frames, and a decoder to reconstruct pixel-space video. GGUF quantization reduces model size by 60-75% while maintaining quality, enabling inference on consumer hardware without cloud APIs.","intents":["Generate short videos from text descriptions for social media content without cloud API costs","Run text-to-video inference locally on edge devices or on-premise infrastructure","Integrate video generation into applications with deterministic, offline-capable pipelines","Prototype video generation workflows without rate limits or API dependencies"],"best_for":["indie developers and small teams building video generation features with cost constraints","organizations requiring on-premise or air-gapped video synthesis for compliance","researchers experimenting with diffusion-based video models without commercial licensing","builders prototyping video-augmented content pipelines for games, education, or marketing"],"limitations":["GGUF quantization introduces 2-5% quality degradation vs full-precision model due to 4-8 bit weight reduction","Inference speed on CPU is 5-15 minutes per 4-8 second video; GPU acceleration (CUDA/Metal) required for <2 minute generation","Output resolution capped at 512x512 or 768x512 due to model architecture; no upscaling included","No motion control, camera movement specification, or frame-by-frame editing — generates deterministic output from text only","Requires 8-16GB VRAM for GPU inference or 32GB+ system RAM for CPU inference","No built-in safety filtering; relies on prompt engineering or external content moderation"],"requires":["Python 3.8+","llama.cpp or compatible GGUF runtime (e.g., ollama, vLLM with GGUF support)","CUDA 11.8+ (for NVIDIA GPU) or Metal (for Apple Silicon) for acceptable inference speed","8GB+ VRAM (GPU) or 32GB+ system RAM (CPU)","PyTorch 2.0+ or compatible inference framework","~7-9GB disk space for model weights"],"input_types":["text (natural language prompts, 10-300 tokens recommended)","optional: seed integer for reproducibility","optional: guidance scale float (1.0-15.0) for prompt adherence strength"],"output_types":["video file (MP4, WebM, or raw frame sequences)","frame resolution: 512x512 or 768x512 pixels","frame rate: 8-24 fps (configurable)","duration: 4-8 seconds (model-dependent)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf__cap_1","uri":"capability://image.visual.diffusion.based.latent.video.synthesis.with.text.conditioning","name":"diffusion-based latent video synthesis with text conditioning","description":"Implements a two-stage video generation pipeline: (1) text encoder converts prompts to embeddings, (2) latent diffusion model iteratively denoises random noise into video latent codes over 20-50 timesteps, (3) VAE decoder reconstructs pixel-space video from latents. The model uses cross-attention mechanisms to inject text conditioning at each diffusion step, enabling semantic alignment between prompts and generated frames.","intents":["Generate coherent multi-frame videos where all frames align semantically with input text","Control generation quality and prompt adherence via guidance scale and sampling parameters","Understand and debug video generation by inspecting latent representations and attention maps","Fine-tune or adapt the model for domain-specific video generation (e.g., product demos, educational content)"],"best_for":["ML engineers building custom video generation pipelines with fine-tuning capabilities","researchers studying diffusion-based video synthesis and cross-modal conditioning","teams needing interpretable video generation with access to intermediate representations"],"limitations":["Diffusion sampling requires 20-50 forward passes per video, making inference inherently slow (~5-15 min per 4-8 sec video on GPU)","Cross-attention mechanism adds ~15-20% computational overhead vs image diffusion models","Temporal consistency between frames degrades for complex motion or long-duration videos (>8 sec)","No explicit control over camera movement, object trajectories, or frame-by-frame edits","Requires understanding of diffusion sampling parameters (steps, guidance scale, scheduler) for quality tuning"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA or Metal support","Diffusers library (Hugging Face) or compatible implementation","Text encoder model (CLIP or similar) for prompt embedding","VAE decoder compatible with Wan2.2 architecture"],"input_types":["text prompts (10-300 tokens)","guidance scale (1.0-15.0, controls prompt adherence)","number of diffusion steps (20-50, higher = better quality but slower)","random seed (for reproducibility)","optional: negative prompts (describe what NOT to generate)"],"output_types":["video latent codes (compressed representation, ~1/8 spatial resolution)","pixel-space video frames (512x512 or 768x512)","attention maps (for interpretability)","intermediate diffusion states (for debugging)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf__cap_2","uri":"capability://automation.workflow.gguf.quantized.model.loading.and.inference.optimization","name":"gguf quantized model loading and inference optimization","description":"Loads the Wan2.2 model from GGUF format (a binary serialization optimized for inference) using llama.cpp-compatible runtimes, automatically selecting CPU or GPU execution paths. Quantization reduces weights from 32-bit floats to 4-8 bits, enabling memory-efficient inference. The runtime handles memory mapping, batch processing, and hardware acceleration (CUDA/Metal) transparently.","intents":["Load and run the 14B parameter model on consumer GPUs (6-12GB VRAM) without out-of-memory errors","Deploy video generation to edge devices or servers with limited compute resources","Minimize inference latency by leveraging GPU acceleration when available, falling back to CPU","Integrate the model into Python applications with minimal boilerplate using standard inference libraries"],"best_for":["developers deploying models to resource-constrained environments (edge, mobile, small servers)","teams optimizing inference cost and latency for production video generation services","builders integrating open-source models without commercial licensing constraints"],"limitations":["GGUF quantization reduces model precision (4-8 bit weights vs 32-bit), causing 2-5% quality loss in generated videos","Inference speed on CPU is 5-15 minutes per video; GPU acceleration is nearly mandatory for practical use","GGUF format is less flexible than PyTorch checkpoints — no easy fine-tuning or layer inspection without conversion","Requires compatible runtime (llama.cpp, ollama, vLLM); not all inference frameworks support GGUF","Memory overhead from model loading can spike 2-3x during inference due to activation caching"],"requires":["llama.cpp, ollama, or vLLM with GGUF support","Python 3.8+ (if using Python bindings)","CUDA 11.8+ (NVIDIA) or Metal (Apple Silicon) for GPU acceleration","8GB+ VRAM (GPU) or 32GB+ system RAM (CPU)","~7-9GB disk space for model file"],"input_types":["GGUF model file (binary format)","inference parameters: temperature, top_p, guidance_scale, num_steps","text prompt and optional negative prompt"],"output_types":["video frames (raw or encoded)","inference metadata (tokens/sec, memory usage, generation time)","optional: attention weights or latent codes for debugging"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf__cap_3","uri":"capability://automation.workflow.batch.video.generation.with.reproducible.outputs","name":"batch video generation with reproducible outputs","description":"Supports generating multiple videos from a list of text prompts with deterministic outputs via seed control. The inference pipeline accepts batch parameters (seed, guidance scale, num_steps) and generates videos sequentially or in parallel, with optional caching of embeddings to reduce redundant computation. Reproducibility is achieved through fixed random seeds and deterministic sampling algorithms.","intents":["Generate 10-100 videos from a prompt list for content creation workflows without manual re-runs","Reproduce exact video outputs for testing, debugging, or A/B comparison by fixing random seeds","Optimize batch inference by caching text embeddings across multiple videos with similar prompts","Integrate video generation into data pipelines or CI/CD workflows with predictable, logged outputs"],"best_for":["content creators and marketers generating bulk video assets from prompt templates","ML engineers building reproducible video generation pipelines for research or production","teams automating video creation for e-commerce, education, or social media"],"limitations":["Batch processing is sequential by default; parallel generation requires manual GPU memory management or multi-GPU setup","Embedding caching provides only ~5-10% speedup for similar prompts due to text encoder overhead","Reproducibility is guaranteed only within the same hardware/software stack; different GPUs or quantization levels may produce slight variations","No built-in progress tracking or failure recovery — requires external orchestration for large batches","Disk I/O becomes bottleneck for batches >100 videos; requires fast storage (SSD) and streaming output"],"requires":["Python 3.8+","GGUF runtime with batch processing support","CSV or JSON file with prompt list","Optional: external orchestration framework (e.g., Ray, Airflow) for large-scale batches"],"input_types":["list of text prompts (CSV, JSON, or Python list)","batch parameters: seed (int), guidance_scale (float), num_steps (int)","optional: output directory path, video format (MP4, WebM)"],"output_types":["video files (one per prompt, 512x512 or 768x512, 4-8 sec duration)","metadata JSON (prompt, seed, generation time, file path)","optional: log file with success/failure status per prompt"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf__cap_4","uri":"capability://planning.reasoning.guidance.scale.controlled.prompt.adherence.tuning","name":"guidance-scale controlled prompt adherence tuning","description":"Implements classifier-free guidance (CFG) during diffusion sampling, allowing users to control how strictly the model adheres to text prompts via a guidance_scale parameter (typically 1.0-15.0). Higher guidance scales increase prompt fidelity but may reduce video diversity and introduce artifacts; lower scales prioritize visual quality and coherence. The mechanism works by interpolating between conditioned and unconditioned diffusion trajectories at each sampling step.","intents":["Fine-tune video generation quality by adjusting prompt adherence vs visual coherence trade-off","Generate diverse video variations from the same prompt by lowering guidance scale","Ensure specific visual elements from prompts appear in output by increasing guidance scale","Debug prompt understanding by experimenting with guidance scale to identify ambiguous or underspecified prompts"],"best_for":["content creators iterating on video generation quality without re-training","researchers studying prompt-to-video alignment and guidance mechanisms","teams tuning generation parameters for specific use cases (e.g., product demos vs artistic content)"],"limitations":["Guidance scale >12.0 often introduces visual artifacts (color banding, distortion) due to over-optimization","Guidance scale <1.5 may ignore prompt details, producing generic or off-topic videos","Optimal guidance scale varies by prompt complexity; no automatic tuning mechanism","CFG adds ~15-20% computational overhead per diffusion step due to dual forward passes (conditioned + unconditioned)","No fine-grained control over which prompt elements to emphasize (e.g., subject vs background)"],"requires":["GGUF runtime supporting guidance_scale parameter","Understanding of diffusion sampling and classifier-free guidance concepts","Iterative experimentation to find optimal guidance scale per use case"],"input_types":["guidance_scale float (1.0-15.0, default ~7.5)","text prompt","optional: negative prompt (for inverse guidance)"],"output_types":["video with varying prompt adherence based on guidance scale","metadata: guidance_scale value used, sampling trajectory (for analysis)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.2-t2v-a14b-gguf__cap_5","uri":"capability://memory.knowledge.open.source.model.distribution.and.community.fine.tuning.enablement","name":"open-source model distribution and community fine-tuning enablement","description":"Distributed via Hugging Face Model Hub as an open-source GGUF quantization of the Wan2.2 base model, enabling community access, inspection, and fine-tuning. The model card includes inference examples, quantization details, and licensing (Apache 2.0), facilitating reproducible research and derivative works. Users can download the GGUF weights directly or use Hugging Face APIs for programmatic access.","intents":["Access a state-of-the-art text-to-video model without commercial licensing or API costs","Inspect model architecture, quantization parameters, and training details for research","Fine-tune or adapt the model for domain-specific video generation (e.g., medical, industrial)","Contribute improvements or alternative quantizations back to the community"],"best_for":["academic researchers and open-source developers building on Wan2.2","organizations with open-source-first policies or compliance requirements","indie developers and startups avoiding commercial licensing fees"],"limitations":["No official support or SLA; community-driven maintenance and bug fixes","Model card may lack detailed training data, safety testing, or bias analysis documentation","Apache 2.0 license requires attribution but allows commercial use; verify compliance for your use case","Community quantizations (like this GGUF variant) may not be officially endorsed by Wan-AI","No guarantee of model stability or backward compatibility across versions"],"requires":["Hugging Face account (free) to download model","Internet connection for initial model download (~7-9GB)","Acceptance of Apache 2.0 license terms"],"input_types":["Hugging Face model identifier: QuantStack/Wan2.2-T2V-A14B-GGUF","optional: Hugging Face API token for authenticated downloads"],"output_types":["GGUF model file (binary weights)","model card (markdown with architecture, training, usage details)","optional: quantization report (compression ratio, quality metrics)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","llama.cpp or compatible GGUF runtime (e.g., ollama, vLLM with GGUF support)","CUDA 11.8+ (for NVIDIA GPU) or Metal (for Apple Silicon) for acceptable inference speed","8GB+ VRAM (GPU) or 32GB+ system RAM (CPU)","PyTorch 2.0+ or compatible inference framework","~7-9GB disk space for model weights","PyTorch 2.0+ with CUDA or Metal support","Diffusers library (Hugging Face) or compatible implementation","Text encoder model (CLIP or similar) for prompt embedding","VAE decoder compatible with Wan2.2 architecture"],"failure_modes":["GGUF quantization introduces 2-5% quality degradation vs full-precision model due to 4-8 bit weight reduction","Inference speed on CPU is 5-15 minutes per 4-8 second video; GPU acceleration (CUDA/Metal) required for <2 minute generation","Output resolution capped at 512x512 or 768x512 due to model architecture; no upscaling included","No motion control, camera movement specification, or frame-by-frame editing — generates deterministic output from text only","Requires 8-16GB VRAM for GPU inference or 32GB+ system RAM for CPU inference","No built-in safety filtering; relies on prompt engineering or external content moderation","Diffusion sampling requires 20-50 forward passes per video, making inference inherently slow (~5-15 min per 4-8 sec video on GPU)","Cross-attention mechanism adds ~15-20% computational overhead vs image diffusion models","Temporal consistency between frames degrades for complex motion or long-duration videos (>8 sec)","No explicit control over camera movement, object trajectories, or frame-by-frame edits","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5361042854721457,"quality":0.22,"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":65945,"model_likes":251}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quantstack--wan2.2-t2v-a14b-gguf","compare_url":"https://unfragile.ai/compare?artifact=quantstack--wan2.2-t2v-a14b-gguf"}},"signature":"Q8cWQTTxs0Jzehx8mbWDctFbfD/mCoR/jLMNdP/317PVFXeuSPEfRnrBSrFSXWK/yydgpDcAt8p30itDG/e8Ag==","signedAt":"2026-06-19T20:21:03.301Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quantstack--wan2.2-t2v-a14b-gguf","artifact":"https://unfragile.ai/quantstack--wan2.2-t2v-a14b-gguf","verify":"https://unfragile.ai/api/v1/verify?slug=quantstack--wan2.2-t2v-a14b-gguf","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"}}