Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: cc-by-nc-4.0
----
+---
+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/xxxx.xxxxx),            
+> Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman        
+> *arXiv technical report ([arXiv xxxx.xxxxx](https://arxiv.org/abs/xxxx.xxxxx))*  
+
+## Contact 
+
+Feel free to contact us if you have any questions about the paper!
+
+Tianwei Yin [[email protected]](mailto:[email protected])
+
+## Huggingface Demo
+
+Our 4-step Text-to-Image demo is hosted on [🤗 DMD2](https://huggingface.co/spaces/tianweiy/DMD2-SDXL-4Step-T2I)
+
+
+## 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](LICENSE.md).
+
+
+## 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:xxxx.xxxxx},
+    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.