Awesome
Bi-level feature alignment for versatile image translation and manipulation
Preparation
Clone the Synchronized-BatchNorm-PyTorch repository.
cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../
VGG model for computing loss. Download from here, move it to models/
.
Datasets
For the datasets for translation, please refer to CoCosNet.
For the datasets for image editing, you can download it from Google Drive.
Translation Results
Some prediction results of our model are provided in Google Drive.
Training
Then run the command
bash train_ade.sh
Citation
If you use this code for your research, please cite our papers.
@article{zhan2021rabit,
title={Bi-level feature alignment for versatile image translation and manipulation},
author={Zhan, Fangneng and Yu, Yingchen and Wu, Rongliang and Cui, Kaiwen and Xiao, Aoran and Lu, Shijian and Shao, Ling},
journal={arXiv preprint arXiv:2107.03021},
year={2021}
}
Acknowledgments
This code borrows heavily from CoCosNet. We also thank SPADE, Synchronized Normalization.