Real-SRGD: Enhancing Real-World Image Super-Resolution with Classifier-Free Guided Diffusion [ACCV2024]

[Paper]

This is the official PyTorch implementation of "Real-SRGD: Enhancing Real-World Image Super-Resolution with Classifier-Free Guided Diffusion (ACCV2024)".

Installation

This repository uses Git LFS (Large File Storage) to manage large files. Please ensure you have Git LFS installed before cloning the repository. Follow the steps below to install Git LFS and set up the project:

1. Install Git LFS
   If you don't have Git LFS installed, you can install it by following the instructions on the Git LFS website.
   Alternatively, you can install it using a package manager:

• For macOS:

  brew install git-lfs

• For Windows: Download and run the Git LFS installer.

• For Linux: Use your distribution's package manager. For example, on Ubuntu:

  sudo apt-get install git-lfs

2. Initialize Git LFS
   After installing, initialize Git LFS in your repository:

   git lfs install

3. Clone the repository
   Use Git to clone this repository. Please note that the download may take some time due to large files managed by Git LFS:

   git clone https://github.com/yahoojapan/srgd
   cd srgd

4. Install packages

   pip install -r requirements.txt
   

Inference

Step 1: Prepare testing data

Create a directory with an appropriate name and place all the images you want to super-resolve into this directory.
This will be your input_dir.

Step 2: Running testing command

Execute the following command to run the inference script. Make sure to specify your input_dir and output_dir paths accordingly:

input_dir=path/to/input_images
output_dir=path/to/output_images

conf="conf/conditional_continuous_linear_df8kost_dim128.yaml"
model="models/srgd/conditional_continuous_linear_df8kost_dim128_epoch300.pth"
test_label=0
class_cond_scale=1.0
seed=71

python inference.py -c ${conf} -m ${model} \
  --class_cond_scale ${class_cond_scale} --test_label ${test_label} --seed ${seed} \
  --input_dir ${input_dir} --output_dir ${output_dir}

Replace path/to/input_images with the path to your input directory and path/to/output_images with the path where you want the super-resolved images to be saved. This script will process the images in the input_dir and save the results to the output_dir.

A sample script inference_sample.sh is provided in the repository to help you get started with the inference process. You can modify this script to fit your specific needs.

Citation

@inproceedings{doi2024,
  title={Real-SRGD: Enhancing Real-World Image Super-Resolution with Classifier-Free Guided Diffusion},
  author={Kenji Doi and Shuntaro Okada and Ryota Yoshihashi and Hirokatsu Kataoka},
  booktitle={Proceedings of the Asian Conference on Computer Vision (ACCV)},
  year={2024},
}

License

MIT