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AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

[Paper] [PyTorch Implementation] [Paddle Implementation]

<a href="https://replicate.ai/huage001/adaattn"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=blue"></a>

Overview

This repository contains the officially unofficial PyTorch **re-**implementation of paper:

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer,

Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding

ICCV 2021

Updates

• [2022-12-07] Upload script of user control. Please see user_specify_demo.py
• [2022-12-07] Upload inference code of video style transfer. Please see inference_frame.py. Please download checkpoints from here and extract the package to the main directory of this repo before running.

Prerequisites

• Linux or macOS

• Python 3

• PyTorch 1.7+ and other dependencies (torchvision, visdom, dominate, and other common python libs)

• Getting Started

  • Clone this repository:

    git clone https://github.com/Huage001/AdaAttN
    cd AdaAttN
    

  • Inference:

    • Make a directory for checkpoints if there is not:

      mkdir checkpoints
      

    • Download pretrained model from Google Drive, move it to checkpoints directory, and unzip:

      mv [Download Directory]/AdaAttN_model.zip checkpoints/
      unzip checkpoints/AdaAttN_model.zip
      rm checkpoints/AdaAttN_model.zip
      

    • Configure content_path and style_path in test_adaattn.sh firstly, indicating paths to folders of testing content images and testing style images respectively.

    • Then, simply run:

      bash test_adaattn.sh
      

    • Check the results under results/AdaAttN folder.

  • Train:

    • Download 'vgg_normalised.pth' from here.

    • Download COCO dataset and WikiArt dataset and then extract them.

    • Configure content_path, style_path, and image_encoder_path in train_adaattn.sh, indicating paths to folders of training content images, training style images, and 'vgg_normalised.pth' respectively.

    • Before training, start visdom server:

      python -m visdom.server
      

    • Then, simply run:

      bash train_adaattn.sh
      

    • You can monitor training status at http://localhost:8097/ and models would be saved at checkpoints/AdaAttN folder.

    • You may feel free to try other training options written in train_adaattn.sh.

  Citation

  • If you find ideas or codes useful for your research, please cite:

    @inproceedings{liu2021adaattn,
      title={AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer},
      author={Liu, Songhua and Lin, Tianwei and He, Dongliang and Li, Fu and Wang, Meiling and Li, Xin and Sun, Zhengxing and Li, Qian and Ding, Errui},
      booktitle={Proceedings of the IEEE International Conference on Computer Vision},
      year={2021}
    }
    

  Acknowledgments

  • This implementation is developed based on the code framework of pytorch-CycleGAN-and-pix2pix by Junyan Zhu et al.