BlindDiff

Introduction

This is the official code of our work BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution.

The pretrained models are Baidu Disk and Google Drive

This repo is built on the basis of BasicSR and guided-diffusion, thanks for their open-sourcing!

Environment

• Python3
• pytorch>=1.7

Installations

Run the command:

pip install -r requirement.txt

and

python setup.py develop

Train

1. Download trainning dataset DIV2K and Flickr2K for the natural images and FFHQ for the face images.
2. Configure options/train.yml for your training.
3. Run the command:

python basicsr/train.py -opt=options/train_setting.yml

Test

1. Configure options/test.yml for your training. The testing dataset used in the paper is here.
2. Run the command:

python basicsr/train.py -opt=options/test.yml

Citation

If you find our work useful in your research or publications, please consider citing:

@article{li2024blinddiff,
  title={BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution},
  author={Li, Feng and Wu, Yixuan and Liang, Zichao and Cong, Runmin and Bai, Huihui, Zhao, Yao and Wang, Meng},
  journal={arXiv preprint arXiv:2403.10211},
  year={2024}
}