TADSR

<div align="center"> <h2>:fire: Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution</h2>

🚩 Accepted by CVPR2026

<a href='https://arxiv.org/abs/2508.16557'><img src='https://img.shields.io/badge/Paper-arxiv-b31b1b.svg'></a>    <a href='https://zty557.github.io/TADSR_HomePage/'><img src='https://img.shields.io/badge/Project page-TADSR-1bb41b.svg'></a>    <a href=''><img src='https://img.shields.io/badge/Space-huggingface-ffd700.svg'></a>   

</div>

This is the official PyTorch codes for the paper

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution<br> Tianyi Zhang¹, Zhengpeng Duan¹, Peng-Tao Jiang², Bo Li Fu², MingMing Cheng¹, Chunle Guo^1,3,†, Chongyi Li^1,3 <br> ¹ VCIP, CS, Nankai University, ² vivo Mobile Communication Co. Ltd. , ³ NKIARI, Shenzhen Futian<br> ^†Corresponding author.

:star: If TADSR is helpful to your images or projects, please help star this repo. Thank you! :point_left:

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:boom: News

• 2025.08.25 Create this repo.

:runner: TODO

• [x] Release training and inference code
• [x] Release Checkpoints

:wrench: Dependencies and Installation

1. Clone repo

git clone https://github.com/zty557/TADSR.git
cd TADSR

2. Install packages

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

:surfer: Quick Inference

Step 1: Download Checkpoints

Download the [TADSR] checkpoints and place them in the directories preset/weights.

Step 2: Prepare testing data

Place low-quality images in preset/datasets/test_datasets/. You can download RealSR, DrealSR and RealLR200 from [SeeSR], Thanks for their awesome works.

Step 3: Running testing command

bash scripts/test_tadsr.sh

Replace the [image_path] and [output_dir] with their respective paths before running the command.

Step 4: Check the results

The processed results will be saved in the [output_dir] directory.

:muscle: Train

Step 1: Prepare the training data

• Download the training datasets LSDIR.
• Following [SeeSR], you can generate the LR-HR pairs for training using.
• Using bash_data/get_tag.sh to get the paths of each HR-LR pair and their corresponding prompts, and you will receive a dataset_list.txt file in the following format.

LSDIR/HR_image/0000001.png LSDIR/LR_image/0000001.png "tag prompt of 0000001.png"
LSDIR/HR_image/0000002.png LSDIR/LR_image/0000002.png "tag prompt of 0000002.png"
LSDIR/HR_image/0000003.png LSDIR/LR_image/0000003.png "tag prompt of 0000003.png"
...

Step 2: Start train

Use the following command to start the training process:

bash scripts/train_tadsr.sh

Replace the [txt_path] with the path to the dataset_list.txt file generated by your dataset.

📜 License

This project is licensed under the Pi-Lab License 1.0 - see the LICENSE file for details.

:book: Citation

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

@misc{zhang2025timeawarestepdiffusionnetwork,
    title={Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution}, 
    author={Tainyi Zhang and Zheng-Peng Duan and Peng-Tao Jiang and Bo Li and Ming-Ming Cheng and Chun-Le Guo and Chongyi Li},
    year={2025},
    eprint={2508.16557},
    archivePrefix={arXiv},
    primaryClass={eess.IV},
    url={https://arxiv.org/abs/2508.16557}, 
}

:postbox: Contact

For technical questions, please contact zty557@gmail.com