cogvideo

We have hosted the application cogvideo in order to run this application in our online workstations with Wine or directly.

Quick description about cogvideo:

CogVideo is an open source text-/image-/video-to-Video generation project that hosts the CogVideoX family of diffusion-transformer models and end-to-end tooling. The repo includes SAT and Diffusers implementations, turnkey demos, and fine-tuning pipelines (including LoRA) designed to run across a wide range of NVIDIA GPUs, from desktop cards (e.g., RTX 3060) to data-center hardware (A100/H100). Current releases cover CogVideoX-2B, CogVideoX-5B, and the upgraded CogVideoX1.5-5B variants, plus image-to-Video (I2V) models, with options for BF16/FP16/FP32—and INT8 quantized inference via TorchAO for memory-constrained setups. The codebase emphasizes practical deployment: prompt-optimization utilities (LLM-assisted long-prompt expansion), Colab notebooks, a Gradio web app, and multiple performance knobs (tiling/slicing, CPU offload, torch.compile, multi-GPU, and FA3 backends via partner projects).

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).

Programming Language: Python, Unix Shell.
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