#2424th of 24 in Image and videoFor image, video and design, video and animation
cogvideo
Open-weight text-to-video and image-to-video diffusion models with inference and LoRA code
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- Stars
- 13.1k
- License
- Apache-2.0
- Last commit
- Nov 2025
- Last release
- Nov 2024
- Language
- Python
12,443 on 31 Mar 202613,092 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
CogVideoX is a family of open text-to-video and image-to-video transformer models (2B and 5B, plus 1.5 versions) from Zhipu AI/THUDM, with inference scripts for diffusers and SAT. The repo also includes LoRA fine-tuning code, a 3D causal VAE and prompt-optimization examples. Diffusers inference runs from about 4GB (2B) or 5GB (5B) VRAM with offloading enabled.
Who it is for: Researchers and self-hosters generating video from text or images on NVIDIA GPUs
Strengths
- Low VRAM paths: 2B from 4GB, 5B from 5GB with diffusers offloading, INT8 via torchao
- Covers text-to-video, image-to-video and video continuation; 1.5 adds 10-second clips
- Includes inference, LoRA fine-tuning and DDIM inversion code in one repo
- Two inference stacks: diffusers and SAT, with weights on HuggingFace and ModelScope
Weaknesses
- Prompts must be English and long; README recommends LLM rewriting, with a 224-226 token limit
- Slow: CogVideoX1.5-5B takes about 1000 seconds for 5 seconds of video on one A100
- Memory figures only tested on A100/H100; peak is about 3x without offloading
- Last release was 2024-11-08; newer work is moving to the separate CogKit repo
What it needs
- GPU required
- Needs PyTorch, diffusers, SwissArmyTransformer, torchao
- Models: CogVideoX-2B, CogVideoX-5B, CogVideoX-5B-I2V, CogVideoX1.5-5B, CogVideoX1.5-5B-I2V
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