## Installation Prerequisites: - Python >= 3.10 - PyTorch >= 1.13 (We recommend to use a >2.0 version) - CUDA >= 11.6 We strongly recommend using Anaconda to create a new environment (Python >= 3.10) to run our examples: ```shell conda create -n teacache python=3.10 -y conda activate teacache ``` Install TeaCache: ```shell git clone https://github.com/LiewFeng/TeaCache cd TeaCache pip install -e . ``` ## Evaluation of TeaCache We first generate videos according to VBench's prompts. And then calculate Vbench, PSNR, LPIPS and SSIM based on the video generated. 1. Generate video ``` cd eval/teacache python experiments/latte.py python experiments/opensora.py python experiments/open_sora_plan.py python experiments/cogvideox.py ``` 2. Calculate Vbench score ``` # vbench is calculated independently # get scores for all metrics python vbench/run_vbench.py --video_path aaa --save_path bbb # calculate final score python vbench/cal_vbench.py --score_dir bbb ``` 3. Calculate other metrics ``` # these metrics are calculated compared with original model # gt video is the video of original model # generated video is our methods's results python common_metrics/eval.py --gt_video_dir aa --generated_video_dir bb ``` ## Citation If you find TeaCache is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. ``` @article{liu2024timestep, title={Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model}, author={Liu, Feng and Zhang, Shiwei and Wang, Xiaofeng and Wei, Yujie and Qiu, Haonan and Zhao, Yuzhong and Zhang, Yingya and Ye, Qixiang and Wan, Fang}, journal={arXiv preprint arXiv:2411.19108}, year={2024} } ``` ## Acknowledgements We would like to thank the contributors to the [Open-Sora](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [Latte](https://github.com/Vchitect/Latte), [CogVideoX](https://github.com/THUDM/CogVideo) and [VideoSys](https://github.com/NUS-HPC-AI-Lab/VideoSys).