mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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43 lines
1.8 KiB
Markdown
43 lines
1.8 KiB
Markdown
<!-- ## **TeaCache4LTX-Video** -->
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# TeaCache4LTX-Video
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[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [LTX-Video](https://github.com/Lightricks/LTX-Video) 2x without much visual quality degradation, in a training-free manner. The following video presents the videos generated by TeaCache-LTX-Video with various rel_l1_thresh values: 0 (original), 0.03 (1.6x speedup), 0.05 (2.1x speedup).
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https://github.com/user-attachments/assets/1f4cf26c-b8c6-45e3-b402-840bcd6ba00e
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## 📈 Inference Latency Comparisons on a Single A800
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| LTX-Video-0.9.1 | TeaCache (0.03) | TeaCache (0.05) |
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|:--------------------------:|:----------------------------:|:---------------------:|
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| ~32 s | ~20 s | ~16 s |
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## Installation
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```shell
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pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece imageio
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```
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## Usage
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You can modify the thresh in line 187 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:
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```bash
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python teacache_ltx.py
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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 [LTX-Video](https://github.com/Lightricks/LTX-Video) and [Diffusers](https://github.com/huggingface/diffusers). |