Awesome
[CVPR 24]SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Welcome! This is the official implementation of the paper "SinSR: Diffusion-Based Image Super-Resolution in a Single Step".
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Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C. Kot, Bihan Wen
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$^1$ Nanyang Technological University, $^2$ Peng Cheng Laboratory, $^3$ Shanghai Artificial Intelligence Laboratory, $^4$ The Hong Kong Polytechnic University
:turtle: Requirements
- Python 3.10, Pytorch 2.1.2, xformers 0.0.23
- More detail (See environment.yml)
A suitable conda environment named
resshift
can be created and activated with:
conda env create -n SinSR python=3.10
conda activate SinSR
pip install -r requirements.txt
or
conda env create -f environment.yml
conda activate SinSR
:whale: Demo
You can try our method through an online demo:
python app.py
(The time taken for the initial run of the model includes loading the model. Besides, it includes a significant amount of time overhead apart from the algorithms itself, e.g., I/O cost, and web frameworks.)
:rocket: Fast Testing
python3 inference.py -i [image folder/image path] -o [result folder] --ckpt weights/SinSR_v1.pth --scale 4 --one_step
Run it on Colab
You can run the code on Google Colab by clicking on the following link:
Requirements
:dolphin: Reproducing the results in the paper
Results in Table 1
# Results on RealSet65
python inference.py -i testdata/RealSet65 -o results/SinSR/RealSet65 --scale 4 --ckpt weights/SinSR_v1.pth --one_step
## Re-evaulated on a RTX3090
# clipiqa: 0.72046
# musiq: 62.25337
# Results on RealSR
python inference.py -i testdata/RealSet65 -o results/SinSR/RealSR --scale 4 --ckpt weights/SinSR_v2.pth --one_step
## Re-evaulated on a RTX3090
### Similar to ResShift, this model is obtained by early stop
# clipiqa: 0.69152
# musiq: 61.43469
If you are running on a GPU with limited memory, you could reduce the patch size by setting --chop_size 256
to avoid out of memory. However, this will slightly degrade the performance.
# Results on RealSet65
python inference.py -i testdata/RealSet65 -o results/SinSR/RealSet65 --scale 4 --ckpt weights/SinSR_v1.pth --one_step --chop_size 256 --task SinSR
# Results on RealSR
python inference.py -i testdata/RealSR -o results/SinSR/RealSR --scale 4 --ckpt weights/SinSR_v2.pth --one_step --chop_size 256 --task SinSR
Results in Table 2
- Download the image ImageNet-Test (Link) to the testdata folder.
- Unzip the downloaded dataset.
- Test the model
python inference.py -i testdata/imagenet256/lq/ -o results/SinSR/imagenet -r testdata/imagenet256/gt/ --scale 4 --ckpt weights/SinSR_v1.pth --one_step
## Re-evaulated on a RTX3090
# clipiqa: 0.60969
# musiq: 53.51805
# psnr: 24.70071
# lpips: 0.21882
# ssim: 0.66364
:airplane: Training
Preparing stage
- Download the necessary pre-trained model, i.e., pretrained ResShift, and Autoencoder. This can be achieved by inferece using ResShift and the needed models will be downloaded automatically.
# Method 1
python3 app.py # Select the model to ResShift in the webpage
# Method 2
python inference.py --task realsrx4 -i [image folder/image path] -o [result folder] --scale 4 # Inference using ResShift
- Adjust the data path in the config file. Specifically, correct and complete paths in files of traindata
- Adjust batchsize according your GPUS.
- configs.train.batch: [training batchsize, validation btatchsize]
- configs.train.microbatch: total batchsize = microbatch * #GPUS * num_grad_accumulation
Train the model
python3 main_distill.py --cfg_path configs/SinSR.yaml --save_dir logs/SinSR
We find that the model can converge very quickly, e.g., a few thousand iterations. Therefore, we believe that the proposed method could be applied to other diffuson-based SR models and encourage a try if you are interested.
:heart: Acknowledgement
This project is based on ResShift. Thanks for the help from the author.
:star: Citation
Please cite our paper if you find our work useful. Thanks!
@inproceedings{wang2024sinsr,
title={SinSR: diffusion-based image super-resolution in a single step},
author={Wang, Yufei and Yang, Wenhan and Chen, Xinyuan and Wang, Yaohui and Guo, Lanqing and Chau, Lap-Pui and Liu, Ziwei and Qiao, Yu and Kot, Alex C and Wen, Bihan},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={25796--25805},
year={2024}
}
:email: Contact
If you have any questions, please feel free to contact me via yufei001@ntu.edu.sg
.