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---
license: mit
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- diffusion distillation
---
> [**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 [zhangshengyuan@zju.edu.cn](mailto:zhangshengyuan@zju.edu.cn)
## 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! |