Awesome
DMIT
Pytorch implementation of our paper: "Multi-mapping Image-to-Image Translation via Learning Disentanglement".
<p align="center"> <img src='images/framework.jpg' align="center" width='90%'> </p>Dependencies
you can install all the dependencies by
pip install -r requirements.txt
Getting Started
Datasets
-
Download and unzip preprocessed datasets by
- Season Transfer
bash ./scripts/download_datasets.sh summer2winter_yosemite
- Semantic Image Synthesis
bash ./scripts/download_datasets.sh birds
- Season Transfer
-
Or you can manually download them from CycleGAN and AttnGAN.
Training
- Season Transfer
bash ./scripts/train_season_transfer.sh
- Semantic Image Synthesis
bash ./scripts/train_semantic_image_synthesis.sh
- To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. More intermediate results can be found in environment
exp_name
.
Testing
- Run
bash ./scripts/test_season_transfer.sh bash ./scripts/test_semantic_image_synthesis.sh
- The testing results will be saved in
checkpoints/{exp_name}/results
directory.
Pretrained Models
Pretrained models can be downloaded from Google Drive or Baidu Wangpan with code 59tm
.
Custom Experiment
You can implement your Dataset and SubModel to start a new experiment.
Results
Season Transfer:
<p align="center"> <img src='images/season_transfer.jpg' align="center" width='90%'> </p>Semantic Image Synthesis:
<p align="center"> <img src='images/semantic_image_synthesis.jpg' align="center" width='90%'> </p>bibtex
If this work is useful for your research, please consider citing :
@inproceedings{yu2019multi,
title={Multi-mapping Image-to-Image Translation via Learning Disentanglement},
author={Yu, Xiaoming and Chen, Yuanqi and Liu, Shan and Li, Thomas and Li, Ge},
booktitle={Advances in Neural Information Processing Systems},
year={2019}
}
Acknowledgement
The code used in this research is inspired by BicycleGAN, MUNIT, DRIT, AttnGAN, and SingleGAN.
The diversity regulazation used in the current version is inspired by DSGAN and MSGAN.
Contact
Feel free to reach me if there is any questions (xiaomingyu@pku.edu.cn).