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license: mit
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Latte: Latent Diffusion Transformer for Video Generation
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license: mit
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---
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## Latte: Latent Diffusion Transformer for Video Generation
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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/).
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## News
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- (π₯ New) May. 23, 2024. π₯ 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).
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- (π₯ New) Mar. 20, 2024. π₯ An updated LatteT2V model is coming soon, stay tuned!
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- (π₯ New) Feb. 24, 2024. π₯ 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.
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- (π₯ New) Jan. 9, 2024. π₯ An updated LatteT2V model initialized with the [PixArt-Ξ±](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).
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- (π₯ New) Oct. 31, 2023. π₯ 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.
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## Contact Us
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**Yaohui Wang**: [wangyaohui@pjlab.org.cn](mailto:wangyaohui@pjlab.org.cn)
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**Xin Ma**: [xin.ma1@monash.edu](mailto:xin.ma1@monash.edu)
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## Citation
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If you find this work useful for your research, please consider citing it.
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```bibtex
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@article{ma2024latte,
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title={Latte: Latent Diffusion Transformer for Video Generation},
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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},
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journal={arXiv preprint arXiv:2401.03048},
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year={2024}
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}
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```
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## Acknowledgments
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Latte has been greatly inspired by the following amazing works and teams: [DiT](https://github.com/facebookresearch/DiT) and [PixArt-Ξ±](https://github.com/PixArt-alpha/PixArt-alpha), we thank all the contributors for open-sourcing.
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