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--- |
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license: cc-by-nc-4.0 |
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library_name: diffusers |
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tags: |
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- text-to-image |
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- stable-diffusion |
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- diffusion distillation |
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--- |
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# DMD2 Model Card |
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> [**Improved Distribution Matching Distillation for Fast Image Synthesis**](https://tianweiy.github.io/dmd2/dmd2.pdf), |
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> Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman |
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## Contact |
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Feel free to contact us if you have any questions about the paper! |
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Tianwei Yin [[email protected]](mailto:[email protected]) |
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<!-- ## Huggingface Demo |
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Our 4-step Text-to-Image demo is hosted at [🤗 DMD2](https://huggingface.co/spaces/tianweiy/DMD2-SDXL-4Step-T2I) --> |
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## Usage |
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Please refer to the [code repository](https://github.com/tianweiy/DMD2) |
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## License |
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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). |
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## Citation |
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If you find DMD2 useful or relevant to your research, please kindly cite our papers: |
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```bib |
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@article{yin2024improved, |
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title={Improved Distribution Matching Distillation for Fast Image Synthesis}, |
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author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T}, |
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journal={arXiv:xxxx.xxxxx}, |
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year={2024} |
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} |
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@inproceedings{yin2024onestep, |
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title={One-step Diffusion with Distribution Matching Distillation}, |
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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}, |
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booktitle={CVPR}, |
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year={2024} |
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} |
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``` |
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## Acknowledgments |
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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. |
