One-Step Effective Diffusion Network for Real-World Image Super-Resolution
1The Hong Kong Polytechnic University, 2OPPO Research Institute
[[paper]](https://arxiv.org/pdf/2406.08177v2)
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🔥 News
- [2024.07] Release OSEDiff-SD21base.
- [2024.06] This repo is created.
🎬 Overview
🔧 Dependencies and Installation
-
Clone repo
git clone https://github.com/cswry/OSEDiff.git cd OSEDiff -
Install dependent packages
conda create -n OSEDiff python=3.10 -y conda activate OSEDiff pip install --upgrade pip pip install -r requirements.txt -
Download Models
Dependent Models
⚡ 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
Quantitative Comparisons (click to expand)
Visual Comparisons (click to expand)
📧 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}
}