{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-city96--wan2.1-t2v-14b-gguf","slug":"city96--wan2.1-t2v-14b-gguf","name":"Wan2.1-T2V-14B-gguf","type":"model","url":"https://huggingface.co/city96/Wan2.1-T2V-14B-gguf","page_url":"https://unfragile.ai/city96--wan2.1-t2v-14b-gguf","categories":["video-generation"],"tags":["gguf","text-to-video","base_model:Wan-AI/Wan2.1-T2V-14B","base_model:quantized:Wan-AI/Wan2.1-T2V-14B","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-city96--wan2.1-t2v-14b-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 short video sequences from natural language text prompts using a 14-billion parameter diffusion model architecture. The model processes text embeddings through a latent diffusion pipeline, iteratively denoising a random noise tensor into coherent video frames across temporal dimensions. Quantized to GGUF format for CPU/GPU inference without requiring 28GB+ VRAM, enabling local deployment on consumer hardware while maintaining visual quality through post-training optimization.","intents":["Generate short video clips from text descriptions without cloud API costs or latency","Create visual content for prototypes, demos, or creative projects locally","Run inference on edge devices or resource-constrained environments","Integrate video generation into applications without external API dependencies"],"best_for":["indie developers and researchers building local video generation pipelines","teams needing cost-effective, privacy-preserving video synthesis","creators prototyping visual content without cloud service subscriptions","organizations with strict data residency requirements"],"limitations":["GGUF quantization reduces model precision (typically 4-8 bit) compared to full FP32, potentially affecting fine detail coherence in generated frames","Inference speed on CPU is significantly slower than GPU (10-60x depending on hardware); typical generation takes 2-10 minutes per 4-8 second video","Output video length is fixed or severely limited (likely 4-8 seconds based on typical T2V model constraints); cannot generate long-form content","No built-in support for video editing, frame interpolation, or post-processing; output is raw diffusion result","Temporal consistency across frames depends on model training; may produce flickering or discontinuous motion in complex scenes","No control over specific camera movements, object trajectories, or fine-grained temporal dynamics"],"requires":["Python 3.8+ with llama-cpp-python or compatible GGUF inference library","8GB+ RAM for model loading (quantized weights); 16GB+ recommended for smooth inference","GPU with CUDA/Metal support strongly recommended (NVIDIA 6GB+ VRAM, Apple Silicon, or AMD ROCm)","Disk space: ~7-8GB for quantized model weights","ffmpeg or similar for video encoding/muxing if post-processing output frames"],"input_types":["text (natural language prompt, typically 10-100 tokens)"],"output_types":["video (MP4, WebM, or raw frame sequence; resolution typically 512x512 or 768x768)","frame sequence (PNG/JPEG frames at 24-30fps)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-city96--wan2.1-t2v-14b-gguf__cap_1","uri":"capability://data.processing.analysis.gguf.format.model.weight.quantization.and.inference.optimization","name":"gguf-format model weight quantization and inference optimization","description":"Implements GGUF (GPT-Generated Unified Format) serialization for the Wan2.1-T2V-14B model, enabling efficient loading and inference through llama.cpp's quantization kernels. The model weights are pre-quantized (likely INT4 or INT8) and stored in a binary format optimized for memory-mapped I/O, allowing rapid model initialization without full decompression and enabling CPU inference through SIMD-optimized matrix operations. This approach trades minimal precision loss for 4-8x memory reduction and 2-4x faster inference on CPU compared to FP32 baseline.","intents":["Deploy large video generation models on laptops and edge devices without GPU","Reduce model loading time from minutes to seconds through memory-mapped weights","Run inference with predictable memory usage (no dynamic allocation surprises)","Integrate quantized models into resource-constrained applications or containers"],"best_for":["developers building offline-first or edge-deployed AI applications","teams optimizing inference cost and latency for production workloads","researchers experimenting with quantization trade-offs on large models","organizations deploying models in air-gapped or bandwidth-limited environments"],"limitations":["Quantization introduces 1-3% quality degradation in video coherence and fine details, particularly noticeable in high-frequency motion or texture details","GGUF format is primarily optimized for CPU inference; GPU acceleration is limited compared to native CUDA/cuDNN implementations","No dynamic quantization or per-layer precision tuning; fixed quantization scheme applied uniformly across all weights","Requires llama.cpp or compatible inference engine; not directly compatible with PyTorch, TensorFlow, or standard ONNX runtimes","Model updates or fine-tuning require re-quantization and GGUF re-export; no in-place weight updates"],"requires":["llama.cpp (latest version) or Python binding (llama-cpp-python 0.2.0+)","GGUF-compatible inference library (e.g., ollama, LM Studio, or custom llama.cpp binary)","Quantization toolchain if converting from original weights (requires original model + quantization script)","C++ compiler or pre-built llama.cpp binary for your platform (Linux, macOS, Windows)"],"input_types":["GGUF binary file (model weights)"],"output_types":["loaded model in memory (ready for inference)","inference output (video frames or embeddings)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-city96--wan2.1-t2v-14b-gguf__cap_2","uri":"capability://automation.workflow.local.video.generation.without.cloud.api.dependencies","name":"local video generation without cloud api dependencies","description":"Enables completely self-contained video generation inference by bundling the quantized model weights with a local inference engine, eliminating the need for external API calls, authentication tokens, or network connectivity. The model runs entirely on the user's hardware (CPU or local GPU), with no telemetry, logging, or data transmission to external servers. This architecture pattern supports air-gapped deployment, offline operation, and full data privacy.","intents":["Generate videos in environments with no internet connectivity or strict network policies","Avoid API rate limits, per-minute billing, or quota restrictions from commercial video generation services","Maintain complete data privacy by keeping video generation and prompts local","Integrate video generation into closed-source or proprietary applications without licensing concerns"],"best_for":["enterprises with data residency or compliance requirements (HIPAA, GDPR, SOC 2)","developers building offline-first or air-gapped applications","teams avoiding vendor lock-in or unpredictable API pricing","researchers and hobbyists prioritizing privacy and cost over speed"],"limitations":["No access to cloud-scale compute; inference speed is limited by local hardware (typically 2-10 minutes per 4-8 second video on consumer GPU)","No automatic model updates or improvements; users must manually download new model versions","Requires significant local storage (7-8GB for model weights) and RAM (8-16GB minimum)","No built-in monitoring, logging, or observability; debugging inference issues requires manual inspection","Limited to the capabilities of the base model; no access to ensemble methods or model switching"],"requires":["Local inference engine (llama.cpp, ollama, or similar) installed and configured","Sufficient disk space for model weights (7-8GB)","Python 3.8+ if using Python bindings for inference","GPU drivers (CUDA, Metal, ROCm) if GPU acceleration is desired"],"input_types":["text prompt (natural language description)"],"output_types":["video file (MP4, WebM, or raw frames)","local file path to generated video"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-city96--wan2.1-t2v-14b-gguf__cap_3","uri":"capability://automation.workflow.multi.platform.inference.execution.cpu.nvidia.gpu.apple.silicon.amd.rocm","name":"multi-platform inference execution (cpu, nvidia gpu, apple silicon, amd rocm)","description":"Supports inference across diverse hardware platforms through llama.cpp's abstracted compute backend, automatically selecting optimized kernels for the available hardware (x86 SIMD, ARM NEON, NVIDIA CUDA, Apple Metal, AMD ROCm). The GGUF format is platform-agnostic; the same quantized weights run on CPU, discrete GPU, or integrated GPU without recompilation or format conversion. Backend selection is typically automatic based on environment variables or runtime detection.","intents":["Deploy the same model across heterogeneous hardware (laptops, servers, edge devices) without maintaining separate builds","Optimize inference for whatever hardware is available without code changes","Run on Apple Silicon Macs with native Metal acceleration without NVIDIA dependency","Support AMD GPU users without requiring NVIDIA CUDA ecosystem"],"best_for":["cross-platform development teams supporting Windows, macOS, and Linux","organizations with mixed hardware deployments (some NVIDIA, some AMD, some CPU-only)","developers building consumer applications targeting diverse user hardware","researchers benchmarking inference across different compute backends"],"limitations":["Performance varies significantly across platforms; CPU inference is 10-50x slower than GPU, and GPU performance depends on VRAM and architecture","Metal acceleration on Apple Silicon is less mature than CUDA; some operations may fall back to CPU","AMD ROCm support is newer and less tested than NVIDIA CUDA; compatibility issues may arise with specific GPU models","No automatic hardware detection or fallback; users must manually configure backend if auto-detection fails","Quantization quality may vary slightly across backends due to different numerical precision in SIMD operations"],"requires":["llama.cpp compiled with support for target backend (CUDA, Metal, ROCm, or CPU)","Platform-specific drivers: NVIDIA CUDA Toolkit 11.8+ (NVIDIA), Metal (macOS 11+), ROCm 5.0+ (AMD)","For CPU inference: no special drivers, but modern CPU with AVX2 or AVX-512 support recommended","Environment variables or configuration file to specify backend (e.g., GGML_CUDA=1, GGML_METAL=1)"],"input_types":["GGUF model file (platform-agnostic binary)"],"output_types":["video frames or video file (output format independent of compute backend)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-city96--wan2.1-t2v-14b-gguf__cap_4","uri":"capability://image.visual.memory.efficient.video.diffusion.inference.with.streaming.frame.output","name":"memory-efficient video diffusion inference with streaming frame output","description":"Implements streaming or incremental frame generation during the diffusion process, allowing partial video output before full inference completion. Rather than buffering all frames in memory before output, the model can emit frames as they are denoised, reducing peak memory usage and enabling progressive video preview. This is particularly valuable for long-running inference on memory-constrained devices, as it avoids the need to hold the entire video tensor in VRAM simultaneously.","intents":["Generate videos on devices with limited VRAM (4-6GB) by streaming frames instead of buffering","Provide real-time preview of video generation progress to users","Reduce peak memory footprint during inference for more stable operation","Enable cancellation or early stopping of video generation if preview is unsatisfactory"],"best_for":["developers building interactive video generation UIs with progress feedback","teams deploying on edge devices or mobile hardware with <8GB RAM","applications requiring responsive user experience during long inference","researchers studying diffusion model behavior across denoising steps"],"limitations":["Streaming frame output may introduce latency or synchronization overhead compared to batch processing","Early frames in diffusion process are low-quality noise; streaming preview may be misleading or confusing to users","Frame-by-frame output requires video encoding overhead; total wall-clock time may be longer than buffered approach","Temporal consistency may be affected if frames are output before full diffusion convergence; later denoising steps may refine earlier frames","Requires custom inference loop or streaming-aware inference engine; not all GGUF loaders support this pattern"],"requires":["Inference engine with streaming or callback support (custom llama.cpp integration or compatible library)","Video encoder (ffmpeg) for real-time frame encoding if streaming to file","Sufficient disk I/O bandwidth for writing frames during inference","Python 3.8+ if using Python-based streaming implementation"],"input_types":["text prompt (natural language description)"],"output_types":["streaming video frames (PNG/JPEG emitted incrementally)","progressive video file (MP4 with frames appended as generated)","frame callback or queue for real-time processing"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+ with llama-cpp-python or compatible GGUF inference library","8GB+ RAM for model loading (quantized weights); 16GB+ recommended for smooth inference","GPU with CUDA/Metal support strongly recommended (NVIDIA 6GB+ VRAM, Apple Silicon, or AMD ROCm)","Disk space: ~7-8GB for quantized model weights","ffmpeg or similar for video encoding/muxing if post-processing output frames","llama.cpp (latest version) or Python binding (llama-cpp-python 0.2.0+)","GGUF-compatible inference library (e.g., ollama, LM Studio, or custom llama.cpp binary)","Quantization toolchain if converting from original weights (requires original model + quantization script)","C++ compiler or pre-built llama.cpp binary for your platform (Linux, macOS, Windows)","Local inference engine (llama.cpp, ollama, or similar) installed and configured"],"failure_modes":["GGUF quantization reduces model precision (typically 4-8 bit) compared to full FP32, potentially affecting fine detail coherence in generated frames","Inference speed on CPU is significantly slower than GPU (10-60x depending on hardware); typical generation takes 2-10 minutes per 4-8 second video","Output video length is fixed or severely limited (likely 4-8 seconds based on typical T2V model constraints); cannot generate long-form content","No built-in support for video editing, frame interpolation, or post-processing; output is raw diffusion result","Temporal consistency across frames depends on model training; may produce flickering or discontinuous motion in complex scenes","No control over specific camera movements, object trajectories, or fine-grained temporal dynamics","Quantization introduces 1-3% quality degradation in video coherence and fine details, particularly noticeable in high-frequency motion or texture details","GGUF format is primarily optimized for CPU inference; GPU acceleration is limited compared to native CUDA/cuDNN implementations","No dynamic quantization or per-layer precision tuning; fixed quantization scheme applied uniformly across all weights","Requires llama.cpp or compatible inference engine; not directly compatible with PyTorch, TensorFlow, or standard ONNX runtimes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4534535505402218,"quality":0.2,"ecosystem":0.48000000000000004,"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":21862,"model_likes":191}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=city96--wan2.1-t2v-14b-gguf","compare_url":"https://unfragile.ai/compare?artifact=city96--wan2.1-t2v-14b-gguf"}},"signature":"/9VCJ6asNmPTVFxJ8KlLzJ5s9vXxwD2e226/7yETj+S2GSDPzmJJNz5QX3UF7NRHQmtMpSIjgFJzx4Ueo2JfCw==","signedAt":"2026-06-21T22:16:01.147Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/city96--wan2.1-t2v-14b-gguf","artifact":"https://unfragile.ai/city96--wan2.1-t2v-14b-gguf","verify":"https://unfragile.ai/api/v1/verify?slug=city96--wan2.1-t2v-14b-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"}}