<!-- ## **TeaCache4FLUX** -->
# TeaCache4FLUX

[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [FLUX](https://github.com/black-forest-labs/flux) 2x without much visual quality degradation, in a training-free manner.  The following image shows the results generated by TeaCache-FLUX with various `rel_l1_thresh` values: 0 (original), 0.25 (1.5x speedup), 0.4 (1.8x speedup), 0.6 (2.0x speedup), and 0.8 (2.25x speedup).

![visualization](../assets/TeaCache4FLUX.png)

## 📈 Inference Latency Comparisons on a Single A800


|      FLUX.1 [dev]       |        TeaCache (0.25)       |    TeaCache (0.4)    |     TeaCache (0.6)    |     TeaCache (0.8)    |
|:-----------------------:|:----------------------------:|:--------------------:|:---------------------:|:---------------------:|
|         ~18 s           |        ~12 s                 |     ~10 s            |       ~9 s             |       ~8 s             |

## Installation

```shell
pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece
```

## Usage

You can modify the `rel_l1_thresh` in line 320 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:

```bash
python teacache_flux.py
```

## 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 [FLUX](https://github.com/black-forest-labs/flux) and [Diffusers](https://github.com/huggingface/diffusers).