• 04/2022: We include Marginal Contrastive Correspondence for Guided Image Generation [CVPR 2022] in this project. Marginal contrastive learning is introduced for building correspondence. Please check models/networks/nceloss.py and models/networks/correspondence.py.

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/.

For the preparation of datasets, please refer to CoCosNet.

The pretrained models can be downloaded from here. Saving the pretrained models in checkpoints.

Then run the command

bash test_ade.sh

Then run the command

bash train_ade.sh

If you want to use marginal contrastive learning for building corresondence, please add --mcl and set the weight --nce_w in train_xxx.sh. Then run:

bash train_xxx.sh

If you use this code for your research, please cite our papers.

@inproceedings{zhan2021unite,
  title={Unbalanced Feature Transport for Exemplar-based Image Translation},
  author={Zhan, Fangneng and Yu, Yingchen and Cui, Kaiwen and Zhang, Gongjie and Lu, Shijian and Pan, Jianxiong and Zhang, Changgong and Ma, Feiying and Xie, Xuansong and Miao, Chunyan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15028--15038},
  year={2021}
}

This code borrows heavily from CoCosNet. We also thank SPADE, Synchronized Normalization and Geometric Loss.