---
license: cc-by-nc-4.0
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- diffusion distillation
---

# DMD2 Model Card

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63363b864067f020756275b7/YhssMfS_1e6q5fHKh9qrc.jpeg)

> [**Improved Distribution Matching Distillation for Fast Image Synthesis**](https://arxiv.org/abs/2405.14867),            
> Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman        

## Contact 

Feel free to contact us if you have any questions about the paper!

Tianwei Yin [tianweiy@mit.edu](mailto:tianweiy@mit.edu)

## Huggingface Demo

Our 4-step (much higher quality, 2X slower) Text-to-Image demo is hosted at [DMD2-4step](https://9c0c372395fcc6b0fe.gradio.live)

Our 1-step Text-to-Image demo is hosted at [DMD2-1step](https://bfd3dfa06e72e0d9f2.gradio.live)

## Usage
Please refer to the [code repository](https://github.com/tianweiy/DMD2)


## License

Improved Distribution Matching Distillation is released under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).


## Citation 

If you find DMD2 useful or relevant to your research, please kindly cite our papers:

```bib
@article{yin2024improved,
    title={Improved Distribution Matching Distillation for Fast Image Synthesis},
    author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T},
    journal={arXiv:2405.14867},
    year={2024}
}

@inproceedings{yin2024onestep,
    title={One-step Diffusion with Distribution Matching Distillation},
    author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung},
    booktitle={CVPR},
    year={2024}
}
```


## Acknowledgments 

This work was done while Tianwei Yin was a full-time student at MIT. It was developed based on our reimplementation of the original DMD paper. This work was supported by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/), by NSF Grant 2105819, by NSF CISE award 1955864, and by funding from Google, GIST, Amazon, and Quanta Computer.