<!-- ## **TeaCache4LuminaT2X** -->
# TeaCache4LuminaT2X

[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X) 2x without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Next with various rel_l1_thresh values: 0 (original), 0.2 (1.5x speedup), 0.3 (1.9x speedup), 0.4 (2.4x speedup), and 0.5 (2.8x speedup).

![visualization](../assets/TeaCache4LuminaT2X.png)

## 📈 Inference Latency Comparisons on a Single A800


|      Lumina-Next-SFT      |        TeaCache (0.2)       |    TeaCache (0.3)    |     TeaCache (0.4)    |     TeaCache (0.5)    |
|:-------------------------:|:---------------------------:|:--------------------:|:---------------------:|:---------------------:|
|         ~17 s             |        ~11 s                |     ~9 s            |       ~7 s             |       ~6 s             |

## Installation

```shell
pip install --upgrade diffusers[torch] transformers protobuf tokenizers sentencepiece
pip install flash-attn --no-build-isolation
```

## Usage

You can modify the thresh in line 113 to obtain your desired trade-off between latency and visul quality. For single-gpu inference, you can use the following command:

```bash
python teacache_lumina_next.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 [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X) and [Diffusers](https://github.com/huggingface/diffusers).