Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: diffusers
+tags:
+- text-to-image
+- stable-diffusion
+- diffusion distillation
+
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63943c882b9483beb473ec25/f8ws6nGK2ZkPEiizha2t9.png)
+
+> [**Distribution Backtracking Builds A Faster Convergence Trajectory for One-step Diffusion Distillation**](https://github.com/SYZhang0805/DisBack),            
+> *[Shengyuan Zhang](https://github.com/SYZhang0805)<sup>1</sup>, [Ling Yang](https://github.com/YangLing0818)<sup>2</sup>, [Zejian Li*](https://zejianli.github.io/)<sup>1</sup>, An Zhao<sup>1</sup>, Chenye Meng<sup>1</sup>, Changyuan Yang<sup>3</sup>, Guang Yang<sup>3</sup>, Zhiyuan Yang<sup>3</sup>, [Lingyun Sun](https://person.zju.edu.cn/sly)<sup>1</sup>*  
+> <sup>1</sup>Zhejiang University <sup>2</sup>Peking University <sup>3</sup>Alibaba Group*
+> 
+## Contact 
+
+Feel free to contact us if you have any questions about the paper!
+
+Shengyuan Zhang [[email protected]](mailto:[email protected])
+
+## Usage
+
+For one-step text-to-image generation, DisBack can use the standard diffuser pipeline:
+
+```python
+import torch
+from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
+from huggingface_hub import hf_hub_download
+
+base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+repo_name = "SYZhang0805/DisBack"
+ckpt_name = "SDXL_DisBack.bin"
+
+unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
+unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
+
+pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+prompt="A photo of a dog." 
+image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[399], height=1024, width=1024).images[0]
+image.save('output.png', 'PNG')
+```
+
+For more details, please refer to our [github repository](https://github.com/SYZhang0805/DisBack)
+
+## License
+
+DisBack is released under [MIT license](https://choosealicense.com/licenses/mit/)
+
+## Citation
+If you find our paper useful or relevant to your research, please kindly cite our papers:
+```bib
+@article{zhang2024distributionbacktrackingbuildsfaster,
+    title={Distribution Backtracking Builds A Faster Convergence Trajectory for One-step Diffusion Distillation}, 
+    author={Shengyuan Zhang and Ling Yang and Zejian Li and An Zhao and Chenye Meng and Changyuan Yang and Guang Yang and Zhiyuan Yang and Lingyun Sun},
+    journal={arXiv 2408.15991},
+    year={2024}
+}
+```
+
+## Credits
+
+DisBack is highly built on the following amazing open-source projects:
+
+[DMD2](https://tianweiy.github.io/dmd2/): Improved Distribution Matching Distillation for Fast Image Synthesis
+
+[Diff-Instruct](https://github.com/pkulwj1994/diff_instruct/tree/main): Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
+
+[ScoreGAN](https://github.com/White-Link/gpm): Unifying GANs and Score-Based Diffusion as Generative Particle Models
+
+Thanks to the maintainers of these projects for their contribution to this project!