mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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73 lines
2.0 KiB
Markdown
73 lines
2.0 KiB
Markdown
## Installation
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Prerequisites:
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- Python >= 3.10
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- PyTorch >= 1.13 (We recommend to use a >2.0 version)
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- CUDA >= 11.6
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We strongly recommend using Anaconda to create a new environment (Python >= 3.10) to run our examples:
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```shell
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conda create -n teacache python=3.10 -y
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conda activate teacache
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```
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Install TeaCache:
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```shell
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git clone https://github.com/LiewFeng/TeaCache
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cd TeaCache
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pip install -e .
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```
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## Evaluation of TeaCache
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We first generate videos according to VBench's prompts.
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And then calculate Vbench, PSNR, LPIPS and SSIM based on the video generated.
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1. Generate video
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```
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cd eval/teacache
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python experiments/latte.py
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python experiments/opensora.py
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python experiments/open_sora_plan.py
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python experiments/cogvideox.py
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```
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2. Calculate Vbench score
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```
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# vbench is calculated independently
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# get scores for all metrics
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python vbench/run_vbench.py --video_path aaa --save_path bbb
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# calculate final score
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python vbench/cal_vbench.py --score_dir bbb
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```
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3. Calculate other metrics
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```
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# these metrics are calculated compared with original model
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# gt video is the video of original model
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# generated video is our methods's results
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python common_metrics/eval.py --gt_video_dir aa --generated_video_dir bb
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```
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## Citation
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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.
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```
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@article{liu2024timestep,
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title={Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model},
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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},
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journal={arXiv preprint arXiv:2411.19108},
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year={2024}
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}
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```
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## Acknowledgements
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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).
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