{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf","slug":"quantstack--wan2.1_14b_vace-gguf","name":"Wan2.1_14B_VACE-GGUF","type":"model","url":"https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF","page_url":"https://unfragile.ai/quantstack--wan2.1_14b_vace-gguf","categories":["video-generation"],"tags":["gguf","video","video-generation","text-to-video","base_model:Wan-AI/Wan2.1-VACE-14B","base_model:quantized:Wan-AI/Wan2.1-VACE-14B","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf__cap_0","uri":"capability://image.visual.text.prompt.to.video.generation.with.quantized.inference","name":"text-prompt-to-video-generation-with-quantized-inference","description":"Generates short-form videos from natural language text prompts using a 14B parameter diffusion-based architecture quantized to GGUF format for CPU/GPU inference. The model uses a text encoder to embed prompts, a latent diffusion process to iteratively denoise video frames in compressed latent space, and a decoder to reconstruct full-resolution video output. GGUF quantization reduces model size from ~28GB to ~8-10GB while maintaining generation quality through post-training quantization, enabling local inference without cloud APIs.","intents":["Generate short videos from text descriptions without cloud dependencies or API costs","Run video generation locally on consumer hardware (8GB+ VRAM or CPU with offloading)","Integrate text-to-video into applications with deterministic, reproducible outputs","Prototype video content generation workflows with custom prompts and parameters"],"best_for":["Independent developers and researchers building local video generation pipelines","Teams requiring on-premise video synthesis without external API dependencies","Content creators prototyping video ideas with custom prompts and iterative refinement","Organizations with data privacy requirements prohibiting cloud-based video generation"],"limitations":["GGUF quantization reduces generation quality compared to full-precision FP32 baseline — expect 5-15% perceptual degradation in fine details and motion smoothness","Inference speed on CPU is prohibitively slow (5-30 minutes per video); requires NVIDIA GPU with 8GB+ VRAM for practical use (2-5 minutes per 4-8 second video)","Output videos limited to 4-8 seconds at typical resolutions (512x512 or 768x512) due to memory constraints during diffusion sampling","No built-in support for multi-prompt composition, video editing, or frame interpolation — single prompt generates single video","Requires manual prompt engineering; lacks semantic understanding of complex scene descriptions or temporal coherence across long sequences"],"requires":["Python 3.8+","NVIDIA CUDA 11.8+ or compatible GPU (RTX 3060 12GB minimum; RTX 4090 recommended for <3min inference)","llama.cpp or compatible GGUF inference runtime (e.g., ollama, gpt4all, or custom C++ bindings)","8-12GB VRAM for GPU inference; 32GB+ system RAM for CPU offloading","HuggingFace transformers library 4.30+ for tokenizer and text encoding","~10GB disk space for model weights"],"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)"],"output_types":["video file (MP4, WebM, or raw frame sequence)","resolution: 512x512, 576x1024, 768x512 (model-dependent)","duration: 4-8 seconds at 24-30 fps","latent embeddings (intermediate diffusion states for analysis)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf__cap_1","uri":"capability://data.processing.analysis.gguf.format.model.loading.and.optimization","name":"gguf-format-model-loading-and-optimization","description":"Loads and optimizes the Wan2.1 model from GGUF binary format using memory-mapped I/O and layer-wise quantization metadata. GGUF (GPT-Generated Unified Format) is a binary serialization that stores model weights, quantization parameters, and hyperparameters in a single file with efficient random access, enabling partial model loading, GPU memory pooling, and automatic precision selection per layer. The format supports mixed-precision inference where attention layers remain FP16 while feedforward layers use INT8, reducing memory bandwidth without proportional quality loss.","intents":["Load a 14B model into 8GB VRAM by leveraging quantization and memory-mapped access patterns","Avoid full model deserialization overhead — load only required layers for inference","Automatically select optimal precision per layer based on hardware capabilities","Distribute model across CPU and GPU memory with transparent offloading"],"best_for":["Developers deploying models on resource-constrained hardware (laptops, edge devices, consumer GPUs)","Teams building inference servers requiring sub-second model load times and low memory fragmentation","Researchers comparing quantization strategies without retraining — GGUF enables A/B testing of different precision configurations"],"limitations":["GGUF is optimized for inference only — no gradient computation or fine-tuning support; requires conversion back to PyTorch/SafeTensors for training","Quantization metadata is static — cannot dynamically adjust precision during inference based on input complexity","Memory-mapped I/O adds ~50-200ms latency on first layer access due to page faults; subsequent accesses are cached","Limited tooling ecosystem compared to PyTorch — fewer debugging utilities and profiling hooks"],"requires":["GGUF-compatible inference runtime: llama.cpp, ollama, gpt4all, or custom C++ bindings","Model file in GGUF format (not PyTorch .pt or SafeTensors .safetensors)","CPU with AVX2 or ARM NEON for efficient quantized operations","Optional: NVIDIA CUDA 11.8+ for GPU acceleration of quantized matrix multiplications"],"input_types":["GGUF binary file (single-file format)","quantization metadata (embedded in GGUF header)"],"output_types":["loaded model in GPU/CPU memory with layer-wise precision configuration","inference-ready model state (no additional compilation required)"],"categories":["data-processing-analysis","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf__cap_2","uri":"capability://image.visual.diffusion.based.video.frame.synthesis.with.temporal.consistency","name":"diffusion-based-video-frame-synthesis-with-temporal-consistency","description":"Synthesizes video frames through iterative denoising in latent space, where a text-conditioned diffusion process progressively refines random noise into coherent video frames over 20-50 sampling steps. The model conditions each diffusion step on the text embedding and previous frame context (via cross-attention and temporal convolutions), enforcing temporal consistency across frames without explicit optical flow. Classifier-free guidance scales the influence of the text prompt (guidance_scale parameter) to trade off prompt adherence vs. visual quality and motion naturalness.","intents":["Generate temporally coherent video sequences where objects move smoothly and scenes evolve logically from text descriptions","Control the trade-off between prompt fidelity and visual quality via guidance_scale parameter","Produce deterministic outputs by seeding the random noise initialization for reproducible video generation","Synthesize videos with custom motion characteristics by adjusting diffusion sampling parameters"],"best_for":["Developers building video generation APIs requiring deterministic, reproducible outputs","Content creators iterating on video concepts with fine-grained control over motion and visual style","Researchers studying temporal consistency in generative models and diffusion-based synthesis"],"limitations":["Temporal consistency degrades over long sequences (>8 seconds) — flickering and object discontinuities increase exponentially with video length","Diffusion sampling requires 20-50 forward passes through the model per video, making inference 10-50x slower than autoregressive or flow-based alternatives","No explicit control over camera motion, object trajectories, or scene composition — only text-based conditioning","Guidance_scale >15 causes visual artifacts (oversaturation, unrealistic textures) due to adversarial gradient effects in the diffusion process","Lacks semantic understanding of temporal relationships — cannot reliably generate videos with specific action sequences or multi-step narratives"],"requires":["Text encoder (CLIP or similar) to embed prompts into 768-1024D vectors","Diffusion scheduler (DDIM, Euler, or DPM++ for efficient sampling)","Video VAE decoder to reconstruct full-resolution frames from latent codes","GPU with 8GB+ VRAM for batch inference; CPU inference impractical (>30 min per video)"],"input_types":["text prompt (10-150 tokens)","seed (integer, optional, for reproducibility)","guidance_scale (float 7.5-15.0, controls prompt adherence)","num_inference_steps (integer 20-50, higher = better quality but slower)"],"output_types":["video frames (sequence of 96-240 frames at 24-30 fps)","latent representations (intermediate diffusion states for analysis or editing)","metadata (seed, guidance_scale, sampling parameters for reproducibility)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf__cap_3","uri":"capability://text.generation.language.text.embedding.and.cross.attention.conditioning","name":"text-embedding-and-cross-attention-conditioning","description":"Encodes text prompts into dense embeddings (typically 768-1024 dimensions) using a frozen CLIP or similar text encoder, then injects these embeddings into the diffusion model via cross-attention layers. Cross-attention computes query-key-value interactions between visual features (from the diffusion UNet) and text embeddings, allowing the model to align generated video content with semantic concepts in the prompt. The text encoder is frozen (not fine-tuned) during video generation, ensuring consistent semantic understanding across different prompts.","intents":["Condition video generation on natural language descriptions without manual feature engineering","Align generated video content with specific semantic concepts and objects mentioned in prompts","Enable prompt-based control over video style, subject matter, and narrative without explicit parameter tuning","Support iterative refinement by modifying prompts and regenerating videos with consistent semantics"],"best_for":["Non-technical users generating videos via natural language prompts","Developers building prompt-based video generation APIs with semantic understanding","Content creators iterating on video concepts through prompt engineering"],"limitations":["Text encoder is frozen — cannot adapt to domain-specific vocabulary or rare concepts without retraining the entire model","Prompt length is limited to tokenizer max length (typically 77 tokens for CLIP), requiring careful prompt engineering for complex scenes","Cross-attention adds ~15-25% computational overhead per diffusion step compared to unconditional generation","Semantic alignment degrades for abstract concepts, negations, and compositional descriptions (e.g., 'a red ball next to a blue cube')","No explicit control over which parts of the prompt influence which regions of the video — attention maps are implicit"],"requires":["Text encoder (CLIP ViT-L/14 or equivalent, ~600MB)","Tokenizer compatible with the text encoder (BPE or WordPiece)","Cross-attention layers in the diffusion UNet (standard in modern video diffusion models)","Prompt in English or language supported by the text encoder"],"input_types":["text prompt (string, 10-150 tokens optimal)","optional: negative prompt (string, specifies content to avoid)"],"output_types":["text embeddings (768-1024D dense vectors)","cross-attention maps (optional, for interpretability)","conditioned video frames (visually aligned with prompt semantics)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-quantstack--wan2.1_14b_vace-gguf__cap_4","uri":"capability://data.processing.analysis.latent.space.video.compression.and.reconstruction","name":"latent-space-video-compression-and-reconstruction","description":"Compresses video frames into a compact latent representation using a trained Video VAE (Variational Autoencoder) with spatial and temporal compression. The VAE encoder reduces 512x512 RGB frames to 64x64 latent codes with 8x spatial compression and 2-4x temporal compression (every 2-4 frames encoded to a single latent vector), reducing memory requirements by 64-256x. The VAE decoder reconstructs full-resolution video from latent codes during inference, enabling diffusion to operate in low-dimensional latent space rather than pixel space, reducing sampling steps and memory bandwidth by 10-50x.","intents":["Reduce memory footprint of video generation by operating in compressed latent space instead of pixel space","Enable faster diffusion sampling by reducing the dimensionality of the denoising process","Reconstruct high-quality video from compact latent representations without perceptual loss","Support batch video generation by fitting multiple latent sequences in GPU memory"],"best_for":["Developers deploying video generation on memory-constrained hardware (consumer GPUs, edge devices)","Teams requiring fast video generation with minimal latency (seconds rather than minutes)","Researchers studying latent space properties and generative model compression"],"limitations":["VAE compression introduces quantization artifacts and information loss — reconstructed videos have slightly reduced detail compared to original frames","Temporal compression (every 2-4 frames) can cause motion jitter or frame interpolation artifacts in fast-moving scenes","VAE decoder is a bottleneck during inference — reconstruction from latent to pixel space adds 20-30% to total generation time","Latent space is not interpretable — cannot directly edit or manipulate latent codes without training additional models","VAE is trained on specific resolution (e.g., 512x512) — cannot easily scale to higher resolutions without retraining"],"requires":["Trained Video VAE encoder and decoder (included in model weights)","Latent space dimensionality: typically 4-8 channels, 64x64 spatial resolution","GPU with 8GB+ VRAM for efficient latent encoding/decoding"],"input_types":["video frames (RGB, 512x512 or model-specific resolution)","optional: compression ratio (trade-off between quality and speed)"],"output_types":["latent codes (4-8 channel, 64x64 spatial, 24-120 temporal frames)","reconstructed video frames (full-resolution RGB, 512x512)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","NVIDIA CUDA 11.8+ or compatible GPU (RTX 3060 12GB minimum; RTX 4090 recommended for <3min inference)","llama.cpp or compatible GGUF inference runtime (e.g., ollama, gpt4all, or custom C++ bindings)","8-12GB VRAM for GPU inference; 32GB+ system RAM for CPU offloading","HuggingFace transformers library 4.30+ for tokenizer and text encoding","~10GB disk space for model weights","GGUF-compatible inference runtime: llama.cpp, ollama, gpt4all, or custom C++ bindings","Model file in GGUF format (not PyTorch .pt or SafeTensors .safetensors)","CPU with AVX2 or ARM NEON for efficient quantized operations","Optional: NVIDIA CUDA 11.8+ for GPU acceleration of quantized matrix multiplications"],"failure_modes":["GGUF quantization reduces generation quality compared to full-precision FP32 baseline — expect 5-15% perceptual degradation in fine details and motion smoothness","Inference speed on CPU is prohibitively slow (5-30 minutes per video); requires NVIDIA GPU with 8GB+ VRAM for practical use (2-5 minutes per 4-8 second video)","Output videos limited to 4-8 seconds at typical resolutions (512x512 or 768x512) due to memory constraints during diffusion sampling","No built-in support for multi-prompt composition, video editing, or frame interpolation — single prompt generates single video","Requires manual prompt engineering; lacks semantic understanding of complex scene descriptions or temporal coherence across long sequences","GGUF is optimized for inference only — no gradient computation or fine-tuning support; requires conversion back to PyTorch/SafeTensors for training","Quantization metadata is static — cannot dynamically adjust precision during inference based on input complexity","Memory-mapped I/O adds ~50-200ms latency on first layer access due to page faults; subsequent accesses are cached","Limited tooling ecosystem compared to PyTorch — fewer debugging utilities and profiling hooks","Temporal consistency degrades over long sequences (>8 seconds) — flickering and object discontinuities increase exponentially with video length","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4134992614809227,"quality":0.2,"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":"2026-04-22T08:08:18.365Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":11425,"model_likes":242}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quantstack--wan2.1_14b_vace-gguf","compare_url":"https://unfragile.ai/compare?artifact=quantstack--wan2.1_14b_vace-gguf"}},"signature":"7wQ4d9bWNhDYE3WWWLuo+aFw7Bmce7gPmav6XVOfxKG5zbAYyNPOd9/sijnkJwFQs3zflVNnJufaXWcUxRGoBA==","signedAt":"2026-06-20T00:11:46.263Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quantstack--wan2.1_14b_vace-gguf","artifact":"https://unfragile.ai/quantstack--wan2.1_14b_vace-gguf","verify":"https://unfragile.ai/api/v1/verify?slug=quantstack--wan2.1_14b_vace-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"}}