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:
conda create -n teacache python=3.10 -y
conda activate teacacheInstall TeaCache:
git clone https://github.com/LiewFeng/TeaCache
cd TeaCache
pip install -e .We first generate videos according to VBench's prompts.
And then calculate Vbench, PSNR, LPIPS and SSIM based on the video generated.
- Generate video
cd eval/teacache
python experiments/latte.py
python experiments/opensora.py
python experiments/open_sora_plan.py
python experiments/cogvideox.py
- 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
- 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
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}
}
We would like to thank the contributors to the Open-Sora, Open-Sora-Plan, Latte, CogVideoX and VideoSys.