{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-zai-org--cogvideo","slug":"zai-org--cogvideo","name":"CogVideo","type":"repo","url":"https://github.com/zai-org/CogVideo","page_url":"https://unfragile.ai/zai-org--cogvideo","categories":["video-generation"],"tags":["cogvideox","image-to-video","llm","sora","text-to-video","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-zai-org--cogvideo__cap_0","uri":"capability://image.visual.text.to.video.generation.with.diffusion.based.latent.space.synthesis","name":"text-to-video generation with diffusion-based latent space synthesis","description":"Generates videos from natural language prompts using a dual-framework architecture: HuggingFace Diffusers for production use and SwissArmyTransformer (SAT) for research. The system encodes text prompts into embeddings, then iteratively denoises latent video representations through diffusion steps, finally decoding to pixel space via a VAE decoder. Supports multiple model scales (2B, 5B, 5B-1.5) with configurable frame counts (8-81 frames) and resolutions (480p-768p).","intents":["Generate short-form videos from text descriptions for content creation workflows","Prototype video generation in research settings with full model control via SAT framework","Deploy text-to-video in production with optimized Diffusers pipelines and memory constraints","Fine-tune models on custom datasets using LoRA or full supervised fine-tuning"],"best_for":["Content creators and video producers building automated video generation pipelines","ML researchers experimenting with diffusion-based video synthesis architectures","Teams deploying video generation at scale with GPU memory constraints (4GB-10GB+)"],"limitations":["Inference latency ranges 90-1000 seconds per video depending on model size and frame count","Output resolution capped at 1360×768 for highest-quality models; lower resolutions (720×480) for faster inference","Requires BF16 or FP16 precision; INT8 quantization available but reduces quality","Text prompts must be reasonably detailed; vague descriptions produce lower-quality outputs","No built-in support for multi-shot or scene composition; generates single continuous video per prompt"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","NVIDIA GPU with 4GB VRAM minimum (CogVideoX-2B) or 10GB+ (CogVideoX1.5-5B)","diffusers>=0.32.2 for Diffusers framework OR SwissArmyTransformer for SAT framework","HuggingFace model weights (auto-downloaded on first run)"],"input_types":["text (natural language prompt, 10-500 characters typical)","optional: seed (integer for reproducibility)","optional: guidance_scale (float 1.0-15.0 for prompt adherence)"],"output_types":["video file (MP4, WebM, or raw tensor)","frame count: 8N+1 frames (9, 17, 25, 33, 41, 49 for base; up to 81 for 1.5 variant)","duration: 6 seconds (8fps) or 5-10 seconds (16fps for 1.5 variant)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_1","uri":"capability://image.visual.image.to.video.generation.with.temporal.coherence.synthesis","name":"image-to-video generation with temporal coherence synthesis","description":"Extends text-to-video by conditioning on an initial image frame, generating temporally coherent video continuations. Accepts an image and optional text prompt, encodes the image into the latent space as a keyframe, then applies diffusion-based temporal synthesis to generate subsequent frames. Maintains visual consistency with the input image while respecting motion cues from the text prompt. Implemented via CogVideoXImageToVideoPipeline in Diffusers and equivalent SAT pipeline.","intents":["Convert static images into short videos with natural motion and temporal flow","Create product demos or marketing videos by animating product images with text descriptions","Generate video continuations from reference frames for storyboarding or animation workflows","Maintain visual identity across generated videos by anchoring to brand/character images"],"best_for":["E-commerce platforms animating product images for listings","Animation studios using AI for in-between frame generation","Content creators extending static assets into video content","Researchers studying image-conditioned video synthesis and temporal consistency"],"limitations":["Output quality depends heavily on input image quality and resolution alignment","Cannot perform drastic scene changes; best for subtle motion and camera pans","Text prompts must describe motion/action, not scene composition (image already defines composition)","Variable-resolution I2V (1.5 variant) requires careful aspect ratio handling; fixed-resolution variant (720×480) simpler but less flexible","Temporal artifacts may appear at frame boundaries if motion is too complex"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","NVIDIA GPU with 5GB VRAM minimum (CogVideoX-5B-I2V) or 10GB+ (CogVideoX1.5-5B-I2V)","diffusers>=0.32.2 or SwissArmyTransformer","Input image in standard format (PNG, JPG, WebP) with recommended resolution 720×480 or 1360×768"],"input_types":["image (PIL Image or tensor, 720×480 or 1360×768 recommended)","text prompt (optional, describes motion/action, 10-300 characters typical)","seed (integer for reproducibility)","guidance_scale (float 1.0-15.0)"],"output_types":["video file (MP4, WebM, or raw tensor)","frame count: 8N+1 frames (9-49 for base; up to 81 for 1.5 variant)","duration: 6 seconds (8fps) or 5-10 seconds (16fps for 1.5 variant)","resolution: matches input image (720×480 or variable for 1.5)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_10","uri":"capability://data.processing.analysis.dataset.preparation.and.preprocessing.pipeline","name":"dataset preparation and preprocessing pipeline","description":"Provides utilities for preparing video datasets for training, including video decoding, frame extraction, caption annotation, and data validation. Handles variable-resolution videos, aspect ratio preservation, and caption quality checking. Integrates with HuggingFace Datasets for efficient data loading during training. Supports both manual caption annotation and automatic caption generation via vision-language models.","intents":["Convert raw video files into training-ready datasets with proper preprocessing","Validate dataset quality before expensive training runs","Generate or annotate captions for videos without manual labeling","Handle variable-resolution videos and aspect ratios in a unified pipeline"],"best_for":["Teams preparing custom datasets for fine-tuning or full training","Researchers building large-scale video generation datasets","Organizations with raw video data needing preprocessing before training","Data engineers setting up training pipelines"],"limitations":["Caption generation quality depends on vision-language model; may require manual review","Video decoding is I/O intensive; preprocessing large datasets takes hours/days","No built-in deduplication; requires external tools to remove duplicate videos","Aspect ratio handling may introduce black bars or distortion if not carefully tuned","Memory usage scales with dataset size; very large datasets may require distributed preprocessing"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","OpenCV (cv2) for video decoding","HuggingFace Datasets library","Vision-language model (optional, for automatic caption generation)","Raw video files in standard formats (MP4, WebM, etc.)"],"input_types":["video files (MP4, WebM, or other standard formats)","captions (JSON, CSV, or text files with video-caption pairs)","preprocessing config (target_resolution, frame_rate, caption_length, etc.)"],"output_types":["preprocessed dataset (HuggingFace Dataset format or directory structure)","data validation report (missing captions, corrupted videos, etc.)","statistics (dataset size, resolution distribution, caption length distribution)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_11","uri":"capability://tool.use.integration.model.architecture.configuration.and.variant.selection","name":"model architecture configuration and variant selection","description":"Provides flexible model configuration system supporting multiple CogVideoX variants (2B, 5B, 5B-1.5) with different resolutions, frame counts, and precision levels. Configuration is specified via YAML or Python dicts, enabling easy switching between model sizes and architectures. Supports both Diffusers and SAT frameworks with unified config interface. Includes pre-defined configs for common use cases (lightweight inference, high-quality generation, variable-resolution).","intents":["Select appropriate model size based on GPU memory and latency requirements","Switch between model variants without code changes (config-driven)","Experiment with different architectures and hyperparameters","Deploy different model variants for different use cases (lightweight vs. high-quality)"],"best_for":["Teams deploying multiple model variants for different use cases","Researchers experimenting with model architectures and configurations","DevOps engineers managing model deployments across environments","Organizations optimizing for different hardware (consumer GPUs vs. data centers)"],"limitations":["Configuration is model-specific; cannot easily add new model variants without code changes","Some parameters (e.g., num_layers, hidden_dim) are baked into model weights; cannot be changed at inference time","Configuration validation is minimal; invalid configs may fail at runtime","No automatic config recommendation based on hardware; users must manually select appropriate variant"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","diffusers>=0.32.2 or SwissArmyTransformer","Model config files (YAML or JSON)","Model weights for selected variant"],"input_types":["model config (YAML or Python dict with model_name, resolution, num_frames, precision, etc.)","variant selection (CogVideoX-2B, CogVideoX-5B, CogVideoX1.5-5B, etc.)"],"output_types":["model instance (CogVideoXPipeline or SAT model)","config validation report (warnings for unusual settings)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_2","uri":"capability://image.visual.video.to.video.editing.with.ddim.inversion.and.diffusion.refinement","name":"video-to-video editing with ddim inversion and diffusion refinement","description":"Enables video editing by inverting existing videos into latent space using DDIM inversion, then applying diffusion-based refinement conditioned on new text prompts. The inversion process reconstructs the latent trajectory of an input video, allowing selective modification of content while preserving temporal structure. Implemented via inference/ddim_inversion.py with configurable inversion steps and guidance scales to balance fidelity vs. editability.","intents":["Edit existing videos by changing content while preserving motion and camera work","Apply style transfers or thematic changes to video sequences","Extend or modify video segments without full re-generation","Research video editing techniques using diffusion-based inversion"],"best_for":["Video editors and post-production teams augmenting existing footage","Researchers studying video inversion and latent space manipulation","Content creators remixing or adapting existing video assets","Teams prototyping video editing workflows before full production implementation"],"limitations":["DDIM inversion is computationally expensive; typically requires 50-100 inversion steps plus 20-50 diffusion steps","Inversion quality degrades with video length; best results on short clips (6-10 seconds)","Temporal inconsistencies may appear if inversion steps are insufficient or guidance too high","Requires input video in compatible format and frame rate; resampling adds preprocessing overhead","Cannot perform structural edits (e.g., removing/adding objects); best for style/content refinement"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","NVIDIA GPU with 10GB+ VRAM (inversion + diffusion pipeline)","Input video file (MP4, WebM, or frame sequence)","diffusers>=0.32.2 or SwissArmyTransformer","inference/ddim_inversion.py module"],"input_types":["video file (MP4, WebM, or frame sequence)","text prompt (describes desired edits/changes, 10-300 characters)","inversion_steps (integer, 50-100 typical)","guidance_scale (float 1.0-15.0)","seed (integer for reproducibility)"],"output_types":["edited video file (MP4, WebM, or raw tensor)","frame count: matches input video","duration: matches input video","resolution: matches input video"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_3","uri":"capability://tool.use.integration.multi.framework.model.weight.conversion.and.interoperability","name":"multi-framework model weight conversion and interoperability","description":"Provides bidirectional weight conversion between SAT (SwissArmyTransformer) and Diffusers frameworks via tools/convert_weight_sat2hf.py and tools/export_sat_lora_weight.py. Enables researchers to train models in SAT (with fine-grained control) and deploy in Diffusers (with production optimizations), or vice versa. Handles parameter mapping, precision conversion (BF16/FP16/INT8), and LoRA weight extraction for efficient fine-tuning.","intents":["Train models in SAT framework then deploy in optimized Diffusers pipelines","Export LoRA adapters from SAT training for lightweight deployment","Migrate existing Diffusers checkpoints to SAT for research-grade fine-tuning","Maintain a single model codebase while supporting multiple inference frameworks"],"best_for":["ML teams balancing research flexibility (SAT) with production deployment (Diffusers)","Researchers publishing models that need to work across frameworks","Organizations with existing SAT infrastructure looking to adopt Diffusers optimizations","Fine-tuning teams using LoRA and needing to export adapters for inference"],"limitations":["Conversion is one-way for some features; not all SAT optimizations map to Diffusers equivalents","LoRA weight export requires SAT training infrastructure; cannot extract LoRA from pre-trained Diffusers models","Precision conversion (BF16→FP16) may introduce subtle numerical differences affecting output quality","Conversion tools are model-specific; adding new model variants requires updating conversion logic","No automatic validation of conversion correctness; manual testing recommended"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+","SwissArmyTransformer library (for SAT→Diffusers conversion)","diffusers>=0.32.2 (for Diffusers target)","Source model weights in SAT or Diffusers format","tools/convert_weight_sat2hf.py or tools/export_sat_lora_weight.py scripts"],"input_types":["SAT model checkpoint (PyTorch .pt or .pth file)","Diffusers model directory (with config.json and model weights)","LoRA adapter weights (from SAT training)","precision specification (BF16, FP16, INT8)"],"output_types":["Diffusers-compatible model directory (config.json + model weights)","SAT-compatible checkpoint (.pt or .pth)","LoRA adapter weights in Diffusers format","Conversion report (parameter mapping, precision changes)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_4","uri":"capability://automation.workflow.memory.optimized.inference.with.sequential.cpu.offloading.and.vae.tiling","name":"memory-optimized inference with sequential cpu offloading and vae tiling","description":"Reduces GPU memory usage by 3x through sequential CPU offloading (pipe.enable_sequential_cpu_offload()) and VAE tiling (pipe.vae.enable_tiling()). Offloading moves model components to CPU between diffusion steps, keeping only the active component in VRAM. VAE tiling processes large latent maps in tiles, reducing peak memory during decoding. Supports INT8 quantization via TorchAO for additional 20-30% memory savings with minimal quality loss.","intents":["Run CogVideoX-5B models on 4GB GPUs instead of requiring 5GB+ VRAM","Deploy video generation on consumer GPUs (RTX 3060, RTX 4060) with memory constraints","Reduce inference latency variance by avoiding OOM errors and retries","Enable batch processing on single-GPU systems by managing memory more efficiently"],"best_for":["Developers deploying on edge devices or consumer GPUs with <8GB VRAM","Teams running inference on shared GPU clusters with strict memory limits","Cost-conscious deployments prioritizing cheaper GPUs over raw performance","Research teams studying memory-efficient diffusion inference"],"limitations":["Sequential CPU offloading adds ~50-100ms latency per diffusion step due to PCIe transfers","VAE tiling may introduce subtle artifacts at tile boundaries if tile size is too small","INT8 quantization reduces output quality slightly; best for non-critical applications","Memory savings plateau beyond 3x; further optimization requires architectural changes","Offloading effectiveness depends on CPU speed and PCIe bandwidth; slow CPUs may negate benefits"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+ with CUDA support","NVIDIA GPU with 4GB+ VRAM (minimum)","diffusers>=0.32.2","TorchAO library (for INT8 quantization, optional)"],"input_types":["CogVideoXPipeline or CogVideoXImageToVideoPipeline instance","offload_strategy: 'sequential' or 'none'","vae_tiling: boolean (enable/disable)","quantization_dtype: 'FP16', 'BF16', or 'INT8' (optional)"],"output_types":["video tensor or file (same as non-optimized inference)","memory usage metrics (peak VRAM, offload overhead)","inference latency (with offloading overhead)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_5","uri":"capability://automation.workflow.lora.based.parameter.efficient.fine.tuning.with.distributed.training","name":"lora-based parameter-efficient fine-tuning with distributed training","description":"Implements Low-Rank Adaptation (LoRA) fine-tuning for video generation models, reducing trainable parameters from billions to millions while maintaining quality. LoRA adapters are applied to attention layers and linear projections, enabling efficient adaptation to custom datasets. Supports distributed training via SAT framework with multi-GPU synchronization, gradient accumulation, and mixed-precision training (BF16). Adapters can be exported and loaded independently via tools/export_sat_lora_weight.py.","intents":["Fine-tune CogVideoX on custom datasets without full model training (weeks→hours)","Adapt models to specific visual styles, domains, or artistic directions","Train on consumer GPUs by reducing memory footprint from 24GB to 8-12GB","Create multiple specialized adapters for different use cases without duplicating base model"],"best_for":["Teams customizing video generation for specific brands, styles, or domains","Researchers studying parameter-efficient adaptation in diffusion models","Organizations with limited GPU budgets needing to fine-tune without full training","Production teams deploying multiple specialized models from a single base"],"limitations":["LoRA rank (typically 8-64) limits expressiveness; cannot learn entirely new concepts as well as full fine-tuning","Requires high-quality, curated training data; garbage in = garbage out","Training still requires 8-12GB VRAM minimum; not suitable for <4GB GPUs","Convergence may be slower than full fine-tuning; requires careful learning rate tuning","LoRA adapters are model-specific; cannot transfer between different base model versions"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+ with CUDA support","NVIDIA GPU with 8GB+ VRAM (for distributed training, 24GB+ recommended)","SwissArmyTransformer library (for SAT training)","Training dataset (video clips + text captions, 100-10k examples typical)","finetune/ directory with training scripts"],"input_types":["training dataset (video files + JSON captions, or HuggingFace dataset)","base model checkpoint (CogVideoX-5B or variant)","LoRA config (rank, alpha, target modules)","training hyperparameters (learning_rate, batch_size, num_epochs, warmup_steps)"],"output_types":["LoRA adapter weights (.pt or .pth file)","training logs (loss curves, validation metrics)","checkpoint directory (for resuming training)","exported adapter in Diffusers format (via tools/export_sat_lora_weight.py)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_6","uri":"capability://automation.workflow.supervised.fine.tuning.with.full.model.training.and.dataset.preparation","name":"supervised fine-tuning with full model training and dataset preparation","description":"Enables full supervised fine-tuning (SFT) of CogVideoX models on custom video datasets via SAT framework. Implements end-to-end training pipeline including dataset preparation (video preprocessing, caption generation/annotation), distributed training with gradient checkpointing, and checkpoint management. Supports variable-resolution training and mixed-precision (BF16) for efficient multi-GPU training on A100/H100 clusters.","intents":["Train custom video generation models from scratch on proprietary datasets","Adapt base models to highly specialized domains (medical imaging, scientific visualization, etc.)","Research video generation architectures and training techniques","Build production models with full control over training data and hyperparameters"],"best_for":["Organizations with large proprietary video datasets and GPU clusters","Research teams publishing new video generation models and techniques","Teams requiring complete control over training data and model behavior","Enterprises building domain-specific video generation (medical, industrial, etc.)"],"limitations":["Requires 24GB+ VRAM per GPU; typically needs 4-8 A100/H100 GPUs for reasonable training time","Training time: 1-4 weeks depending on dataset size and model variant","Requires high-quality, large-scale training data (10k-100k+ video clips with captions)","Hyperparameter tuning is critical; poor choices lead to mode collapse or divergence","Distributed training adds complexity; requires expertise in multi-GPU synchronization and gradient accumulation"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+ with CUDA support","NVIDIA GPU cluster with 24GB+ VRAM per GPU (A100/H100 recommended)","SwissArmyTransformer library","Large training dataset (10k-100k+ video clips with text captions)","sat/ directory with training scripts and configuration","Distributed training framework (torch.distributed or DeepSpeed, optional)"],"input_types":["training dataset (video files + captions, or HuggingFace dataset)","validation dataset (for monitoring convergence)","model architecture config (model_size, num_layers, hidden_dim, etc.)","training hyperparameters (learning_rate, batch_size, num_epochs, warmup_steps, gradient_accumulation_steps)","distributed training config (num_gpus, num_nodes, backend)"],"output_types":["trained model checkpoint (.pt or .pth file)","training logs (loss curves, validation metrics, learning rate schedule)","checkpoint directory (for resuming training or inference)","model config (for reproducibility and deployment)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_7","uri":"capability://automation.workflow.cli.based.inference.with.configurable.generation.parameters","name":"cli-based inference with configurable generation parameters","description":"Provides command-line interface (inference/cli_demo.py) for running text-to-video, image-to-video, and video-to-video generation without code. Exposes key parameters as CLI arguments: prompt, image_path, video_path, num_frames, guidance_scale, seed, output_path. Supports both Diffusers and SAT backends via --framework flag. Includes progress bars, memory monitoring, and error handling for batch processing.","intents":["Run video generation from shell scripts or CI/CD pipelines without Python coding","Batch process multiple prompts or images in automated workflows","Quickly prototype video generation without writing inference code","Integrate CogVideoX into existing command-line tools and scripts"],"best_for":["DevOps engineers integrating video generation into CI/CD pipelines","Content creators using video generation in batch workflows","Researchers prototyping without writing custom inference code","Teams building command-line tools that wrap video generation"],"limitations":["CLI arguments are limited to common parameters; advanced options require Python API","No built-in batching optimization; processing multiple videos sequentially is slower than optimized batch inference","Error messages may be cryptic for non-technical users; requires GPU knowledge to debug","No progress estimation; users don't know how long generation will take","Output format is fixed (MP4 or WebM); no option for raw tensor export via CLI"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+ with CUDA support","NVIDIA GPU with 4GB+ VRAM","diffusers>=0.32.2 or SwissArmyTransformer","inference/cli_demo.py script","Input files (text prompt, image, or video file)"],"input_types":["command-line arguments: --prompt, --image_path, --video_path, --num_frames, --guidance_scale, --seed, --output_path, --framework","input files: image (PNG/JPG) or video (MP4/WebM) if using I2V or V2V"],"output_types":["video file (MP4 or WebM, default MP4)","console output (progress bar, memory usage, generation time)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_8","uri":"capability://tool.use.integration.web.based.inference.interface.with.gradio.ui","name":"web-based inference interface with gradio ui","description":"Provides interactive web interface (inference/gradio_web_demo.py) for video generation using Gradio framework. Exposes text-to-video, image-to-video, and video-to-video modes via tabbed interface. Includes real-time parameter sliders (guidance_scale, num_frames, seed), file upload widgets, and live generation preview. Supports both Diffusers and SAT backends with automatic framework detection.","intents":["Enable non-technical users to generate videos via web browser without CLI/Python knowledge","Prototype video generation features for user testing and feedback","Deploy video generation as a web service for internal teams or public demos","Provide interactive parameter tuning interface for experimentation"],"best_for":["Product teams demoing video generation to stakeholders","Internal tools teams building self-service video generation for non-technical users","Researchers sharing models via public Gradio links (HuggingFace Spaces)","Teams prototyping UI/UX for video generation applications"],"limitations":["Gradio UI is basic; lacks advanced features like batch processing, scheduling, or result history","File uploads are limited by browser/server constraints; large videos may timeout","No authentication or rate limiting; public deployments are vulnerable to abuse","Gradio performance degrades with concurrent users; not suitable for high-traffic production","Parameter tuning is manual; no automatic hyperparameter search or optimization"],"requires":["Python 3.10-3.12","PyTorch 2.5.1+ with CUDA support","NVIDIA GPU with 4GB+ VRAM","diffusers>=0.32.2 or SwissArmyTransformer","Gradio library (pip install gradio)","inference/gradio_web_demo.py script"],"input_types":["text prompt (via text input field)","image file (via file upload widget, PNG/JPG)","video file (via file upload widget, MP4/WebM)","parameters: guidance_scale (slider), num_frames (slider), seed (number input)"],"output_types":["video preview (embedded in web page)","downloadable video file (MP4 or WebM)","generation metadata (time taken, memory used)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zai-org--cogvideo__cap_9","uri":"capability://data.processing.analysis.quantization.aware.inference.with.int8.and.fp8.precision","name":"quantization-aware inference with int8 and fp8 precision","description":"Supports INT8 and FP8 quantization via TorchAO library for reduced memory usage and faster inference. Quantizes model weights and activations to 8-bit precision while maintaining output quality through calibration on representative data. Integrated into inference pipeline via inference/cli_demo_quantization.py. 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