• Visual Results
• Installation
• Pretrained Models
• Dataset
• Inference
• Training
• Citation
• Acknowledgements
• Contact

# Clone this repository
git clone https://github.com/wwangcece/TADM.git

# Create a conda environment with Python >= 3.9
conda create -n TADM python=3.9
conda activate TADM

# Install required packages
pip install -r requirements.txt

Download the pretrained models from the link below and place them in the checkpoints/ directory:

Download from Google Drive

1. Refer to the BasicSR dataset preparation guide to prepare high-resolution datasets.
2. Use the script src/get_z.py to encode high-resolution images into latent features and save them as .npy files.

1. Modify the validation dataset configuration in configs/tadm_test.yaml and update the pretrained model path in run_inference.sh.
2. Run the inference script:

sh run_inference.sh

1. Modify the training dataset configuration in configs/tadm_train.yaml and update settings in run_training_dfrm.sh.
2. Train the feature rescaling module:

sh run_training_dfrm.sh

3. Then modify run_training_tadm.sh as needed and train the TADM model:

sh run_training_tadm.sh

If you find this work helpful, please consider citing:

@misc{wang2025timestepawarediffusionmodelextreme,
  title={Timestep-Aware Diffusion Model for Extreme Image Rescaling},
  author={Ce Wang and Zhenyu Hu and Wanjie Sun and Zhenzhong Chen},
  year={2025},
  eprint={2408.09151},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2408.09151}
}

This project is based on S3Diff. We thank the authors for their excellent work.

If you have any questions, feel free to reach out to: Ce Wang — cewang@whu.edu.cn