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Overview: We propose CAMixerSR, a new approach integrating content-aware accelerating framework and token mixer design, to pursue more efficient SR inference via assigning convolution for simple regions but window-attention for complex textures. It exhibits excellent generality and attains competitive results among state-of-the-art models with better complexity-performance trade-offs on large-image SR, lightweight SR, and omnidirectional-image SR.

This repository contains PyTorch implementation for CAMixerSR (CVPR 2024).

The main codes and pre-trained models have been uploaded. We are planning to replenish the README of CAMixerSR in a few weeks.

• PyTorch >= 1.7
• BasicSR == 1.4.2
• einops

pip install -r requirements.txt

Training: DIV2K.

Testing: F2K, Test2K, Test4K, Test8K (Google Drive/Baidu Netdisk).

Training: DIV2K or DF2K.

Testing: Set5, Set14, BSD100, Urban100, Manga109 (Google Drive/Baidu Netdisk).

Training: lau dataset.

Clone this repository and change the directory to ./codes

git clone https://github.com/icandle/CAMixerSR.git
cd codes

Testing: Change the dataset path of example option to your datasets and test with the command:

python basicsr/test.py -opt ../options/test/test_x4.yml

We would thank BasicSR, ClassSR, and OSRT for their enlightening work!

@article{wang2024camixersr,
  title={CAMixerSR: Only Details Need More ``Attention"},
  author={Wang, Yan and Liu, Yi and Zhao, Shijie and Li, Junlin and Zhang, Li},
  journal={arXiv preprint arXiv:2402.19289},
  year={2024}
}