One-Step Effective Diffusion Network for Real-World Image Super-Resolution

Rongyuan Wu^1,2,*  Lingchen Sun^1,2,*  Zhiyuan Ma^1,*  Lei Zhang^1,2,†

¹The Hong Kong Polytechnic University, ²OPPO Research Institute 

[paper]

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🚩Accepted by NeurIPS2024

🔥 News

• [2024.07] Release OSEDiff-SD21base.
• [2024.06] This repo is created.

🎬 Overview

🔧 Dependencies and Installation

1. Clone repo

   git clone https://github.com/cswry/OSEDiff.git
   cd OSEDiff

2. Install dependent packages

   conda create -n OSEDiff python=3.10 -y
   conda activate OSEDiff
   pip install --upgrade pip
   pip install -r requirements.txt

3. Download Models

Dependent Models

• SD21 Base
• RAM
• DAPE

⚡ Quick Inference

python test_osediff.py \
-i preset/datasets/test_dataset/input \
-o preset/datasets/test_dataset/output \
--osediff_path preset/models/osediff.pkl \
--pretrained_model_name_or_path SD21BASE_PATH \
--ram_ft_path DAPE_PATH \
--ram_path RAM_PATH

📷 Results

• For convenient evaluation and comparison, we have published the test results of DIV2K_val, RealSR, and DRealSR on Google Drive.

Quantitative Comparisons (click to expand)

Visual Comparisons (click to expand)

🎫 License

This project is released under the Apache 2.0 license.

📧 Contact

If you have any questions, please feel free to contact: rong-yuan.wu@connect.polyu.hk

🎓Citations

@article{wu2024one,
  title={One-Step Effective Diffusion Network for Real-World Image Super-Resolution},
  author={Wu, Rongyuan and Sun, Lingchen and Ma, Zhiyuan and Zhang, Lei},
  journal={arXiv preprint arXiv:2406.08177},
  year={2024}
}

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