---
license: mit
---

## Latte: Latent Diffusion Transformer for Video Generation

This repo contains pre-trained weights for our paper exploring  latent diffusion models with transformers (Latte). You can find more visualizations on our [project page](https://maxin-cn.github.io/latte_project/).

## News
- (🔥 New) May. 23, 2024. 💥 The updated LatteT2V model is released at [here](https://huggingface.co/maxin-cn/Latte/blob/main/t2v_v20240523.pt). If you want to use the updated model to generate images directly, please make sure `video_length=1`, `enable_temporal_attentions=True` and `enable_vae_temporal_decoder=False` in [t2v_sample.yaml](configs/t2v/t2v_sample.yaml).

- (🔥 New) Mar. 20, 2024. 💥 An updated LatteT2V model is coming soon, stay tuned!

- (🔥 New) Feb. 24, 2024. 💥 We are very grateful that researchers and developers like our work. We will continue to update our LatteT2V model, hoping that our efforts can help the community develop. Our Latte [discord](https://discord.gg/RguYqhVU92) channel is created for discussions. Coders are welcome to contribute.

- (🔥 New) Jan. 9, 2024. 💥 An updated LatteT2V model initialized with the [PixArt-α](https://github.com/PixArt-alpha/PixArt-alpha) is released, the checkpoint can be found [here](https://huggingface.co/maxin-cn/Latte/resolve/main/t2v.pt?download=true).

- (🔥 New) Oct. 31, 2023. 💥 The training and inference code is released. All checkpoints (including FaceForensics, SkyTimelapse, UCF101, and Taichi-HD) can be found [here](https://huggingface.co/maxin-cn/Latte/tree/main). In addition, the LatteT2V inference code is provided.

## Contact Us
**Yaohui Wang**: [wangyaohui@pjlab.org.cn](mailto:wangyaohui@pjlab.org.cn)
**Xin Ma**: [xin.ma1@monash.edu](mailto:xin.ma1@monash.edu)

## Citation
If you find this work useful for your research, please consider citing it.
```bibtex
@article{ma2024latte,
  title={Latte: Latent Diffusion Transformer for Video Generation},
  author={Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Liu, Ziwei and Li, Yuan-Fang and Chen, Cunjian and Qiao, Yu},
  journal={arXiv preprint arXiv:2401.03048},
  year={2024}
}
```


## Acknowledgments
Latte has been greatly inspired by the following amazing works and teams: [DiT](https://github.com/facebookresearch/DiT) and [PixArt-α](https://github.com/PixArt-alpha/PixArt-alpha), we thank all the contributors for open-sourcing.