https://github.com/lifengcs/BlindDiff

https://github.com/lifengcs/BlindDiff

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/lifengcs/BlindDiff
• Owner: lifengcs
• Created: 2024-03-15T09:38:34.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-07-06T07:42:10.000Z (4 months ago)
• Last Synced: 2024-08-01T18:41:00.976Z (3 months ago)
• Language: Python
• Size: 11.7 MB
• Stars: 14
• Watchers: 4
• Forks: 1
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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• awesome-diffusion-categorized - [Code

README

        
# BlindDiff

## Introduction

This is the official code of our work [*BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution*](https://arxiv.org/abs/2403.10211).

The pretrained models are [Baidu Disk](https://pan.baidu.com/s/1C2HCOlUOfzNnIxxIB_-hSA?pwd=s4hd) and [Google Drive](https://drive.google.com/file/d/1lL8MgElQZW9OPglbwDrwkv558CL8dO-H/view?usp=drive_link)

This repo is built on the basis of [BasicSR](https://github.com/XPixelGroup/BasicSR) and [guided-diffusion](https://github.com/openai/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](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](https://github.com/sanghyun-son/EDSR-PyTorch) for the natural images and [FFHQ](https://www.kaggle.com/datasets/denislukovnikov/ffhq256-images-only) 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](https://pan.baidu.com/s/1KnaMSYx9plRgBNT9eaH5dg?pwd=1znj).

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}

}

```