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AdaAttN

★ 247 Open GitHub ↗

Officially unofficial PyTorch re-implementation of paper: AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer, ICCV 2021.

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# AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

> [[Paper](https://arxiv.org/abs/2108.03647)] [[PyTorch Implementation](https://github.com/Huage001/AdaAttN)] [[Paddle Implementation](https://github.com/PaddlePaddle/PaddleGAN)]

<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

![](https://raw.githubusercontent.com/Huage001/AdaAttN/main/picture/picture.png)

## 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](https://drive.google.com/file/d/1ZrFPrbSR56WXL2l62Jh7qjmuaWugJtvL/view?usp=sharing) 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:

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

* Inference:

* Make a directory for checkpoints if there is not:

```shell
      mkdir checkpoints
      ```

* Download pretrained model from [Google Drive](https://drive.google.com/file/d/1XvpD1eI4JeCBIaW5uwMT6ojF_qlzM_lo/view?usp=sharing), move it to checkpoints directory, and unzip:

```shell
      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:

```shell
      bash test_adaattn.sh
      ```

* Check the results under results/AdaAttN folder.

* Train:

* Download 'vgg_normalised.pth' from [here](https://drive.google.com/file/d/1BinnwM5AmIcVubr16tPTqxMjUCE8iu5M/view?usp=sharing).

* Download [COCO dataset](http://images.cocodataset.org/zips/train2014.zip) and [WikiArt dataset](http://web.fsktm.um.edu.my/~cschan/source/ICIP2017/wikiart.zip) 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:

```shell
      python -m visdom.server
      ```

* Then, simply run:

```shell
      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](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)** by Junyan Zhu *et al.*

AdaAttN

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