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<div align="center"> <div class="logo"> <img src="assets/logo_lora.png" style="width:180px"> </a> </div> <h1>LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration</h1> <div> <a href='https://scholar.google.com/citations?user=2Qp7Y5kAAAAJ' target='_blank'>Yuang Ai</a><sup>1,2</sup>&emsp; <a href='https://scholar.google.com/citations?user=XMvLciUAAAAJ' target='_blank'>Huaibo Huang</a><sup>1,2</sup>&emsp; <a href='https://scholar.google.com/citations?user=ayrg9AUAAAAJ' target='_blank'>Ran He</a><sup>1,2</sup> </div> <div> <sup>1</sup>MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences&emsp;<br> <sup>2</sup>School of Artificial Intelligence, University of Chinese Academy of Sciences&emsp; </div> <div> </div> <div> <strong>arXiv 2024</strong> </div> <div> <h4 align="center"> <a href="https://arxiv.org/abs/2410.15385" target='_blank'> <img src="https://img.shields.io/badge/arXiv%20paper-2410.15385-b31b1b.svg"> </a> <a href="https://huggingface.co/shallowdream204/LoRA-IR/tree/main" target='_blank'> <img src="https://img.shields.io/badge/🤗%20Weights-LoRA--IR-yellow"> </a> <img src="https://visitor-badge.laobi.icu/badge?page_id=shallowdream204/LoRA-IR"> </h4> </div>

⭐ If LoRA-IR is helpful to your projects, please help star this repo. Thanks! 🤗

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🔥 News

2024.10.20: Release training&inference code, pre-trained models of Setting Ⅰ.
2024.10.20: This repo is created.

🏗️ Overall Framework

lorair

🔧 Dependencies and Installation

Clone this repo and navigate to LoRA-IR folder

git clone https://github.com/shallowdream204/LoRA-IR.git
cd LoRA-IR

Create Conda Environment and Install Package

conda create -n lorair python=3.11 -y
conda activate lorair
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip3 install -r requirements.txt
python3 setup.py develop --no_cuda_ext

⚡ Train & Inference

Training and Testing instructions for different settings are provided in their respective directories. Here is a summary table containing hyperlinks for easy navigation:

<table> <tr> <th align="left">Setting</th> <th align="center">Training Instructions</th> <th align="center"> Evaluation Instructions</th> <th align="center">Pre-trained Models</th> </tr> <tr> <td align="left">Setting Ⅰ</td> <td align="center"><a href="Setting1/README.md#Train">Link</a></td> <td align="center"><a href="Setting1/README.md#Evaluation">Link</a></td> <td align="center"><a href="https://huggingface.co/shallowdream204/LoRA-IR/tree/main">Download</a></td> </tr> </table>

🪪 License

The provided code and pre-trained weights are licensed under the Apache 2.0 license.

🤗 Acknowledgement

This code is based on NAFNet and BasicSR. Some code are brought from loralib, LLaVA and Restormer. We thank the authors for their awesome work.

📧 Contact

If you have any questions, please feel free to reach me out at shallowdream555@gmail.com.

📖 Citation

If you find our work useful for your research, please consider citing our paper:

@article{ai2024lora,
      title={LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration},
      author={Ai, Yuang and Huang, Huaibo and He, Ran},
      journal={arXiv preprint arXiv:2410.15385},
      year={2024}
}