mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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41 lines
1.4 KiB
Markdown
41 lines
1.4 KiB
Markdown
<!-- ## **TeaCache4TangoFlux** -->
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# TeaCache4TangoFlux
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[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [TangoFlux](https://github.com/declare-lab/TangoFlux) 2x without much audio quality degradation, in a training-free manner.
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## 📈 Inference Latency Comparisons on a Single A800
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| TangoFlux | TeaCache (0.25) | TeaCache (0.4) |
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|:-------------------:|:----------------------------:|:--------------------:|
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| ~4.08 s | ~2.42 s | ~1.95 s |
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## Installation
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```shell
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pip install git+https://github.com/declare-lab/TangoFlux
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```
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## Usage
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You can modify the thresh in line 266 to obtain your desired trade-off between latency and audio quality. For single-gpu inference, you can use the following command:
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```bash
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python teacache_tango_flux.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 [TangoFlux](https://github.com/declare-lab/TangoFlux). |