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
- [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.
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Linux or macOS
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Python 3
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PyTorch 1.7+ and other dependencies (torchvision, visdom, dominate, and other common python libs)
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Clone this repository:
git clone https://github.com/Huage001/AdaAttN cd AdaAttN
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Inference:
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Make a directory for checkpoints if there is not:
mkdir checkpoints
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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
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Configure content_path and style_path in test_adaattn.sh firstly, indicating paths to folders of testing content images and testing style images respectively.
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Then, simply run:
bash test_adaattn.sh
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Check the results under results/AdaAttN folder.
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Train:
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Download 'vgg_normalised.pth' from here.
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Download COCO dataset and WikiArt dataset and then extract them.
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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.
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Before training, start visdom server:
python -m visdom.server
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Then, simply run:
bash train_adaattn.sh
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You can monitor training status at http://localhost:8097/ and models would be saved at checkpoints/AdaAttN folder.
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You may feel free to try other training options written in train_adaattn.sh.
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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} }
- This implementation is developed based on the code framework of pytorch-CycleGAN-and-pix2pix by Junyan Zhu et al.
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