Awesome

StyA2K

This repository is an implementation of the ICCV 2023 paper "All-to-key Attention for Arbitrary Style Transfer".

Requirements

• Ubuntu 18.04
• Anaconda (Python, Numpy, PIL, etc.)
• PyTorch 1.9.0
• torchvision 0.10.0

Getting Started

• Inference:

  • Download vgg_normalised.pth.

  • The pre-trained models are right in the ./checkpoints/A2K directory, including: latest_net_A2K.pth, latest_net_decoder.pth, and latest_net_transform.pth

  • Configure content_path and style_path in test_A2K.sh to specify the paths to testing content and style images folders, respectively.

  • Run:

    bash test_A2K.sh
    

  • Check the results under the ./results/A2K directory.

• Train:

  • Download vgg_normalised.pth.

  • Download COCO dataset and WikiArt dataset.

  • Configure content_path, style_path, and image_encoder_path in train_A2K.sh to specify the paths to training content images folders, training style images folders, and "vgg_normalised.pth", respectively.

  • Then, simply run:

    bash train_A2K.sh
    

  • Monitor the training status at http://localhost:8097/. Trained models would be saved in the ./checkpoints/A2k folder.

  • Try other training options in train_A2K.sh.

Acknowledgments

• This code builds heavily on pytorch-CycleGAN-and-pix2pix and AdaAttN. Thanks for open-sourcing!