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CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference, fine-tuning, and optimization, making it suitable for both research and production use. It supports efficient deployment on a range of GPUs, including consumer hardware with quantization techniques. Overall, CogVideo provides a powerful framework for generating high-quality AI videos and experimenting with cutting-edge multimodal AI systems.
Features
- Multiple tasks: text-to-video, image-to-video, and video-to-video generation.
- Dual stacks: SAT implementations and Diffusers pipelines with shared demos.
- Fine-tuning recipes (incl. LoRA), plus cogvideox-factory for single-GPU (4090) training.
- Quantized inference (INT8 via TorchAO) and memory optimizations (CPU offload, tiling, slicing).
- Ready-to-run assets: Colab notebooks, CLI demos, and a Gradio web UI with tools (SR/interp).
- Utilities & ecosystem: weight converters (SAT→HF), captioning tools, and third-party integrations (ComfyUI, ControlNet, xDiT, VideoSys).
Project Samples
