Pytorch implement of Yezhi Shu, Ran Yi, Mengfei Xia, Zipeng Ye, Wang Zhao, Yang Chen, Yu-Kun Lai, Yong-Jin Liu. GAN-based Multi-Style Photo Cartoonization. IEEE Transactions on Visualization and Computer Graphics, DOI (identifier) 10.1109/TVCG.2021.3067201, 2021.
Install PyTorch 1.2.0+ and torchvision from http://pytorch.org and other dependencies could be downloaded by
pip install -r requirements.txt- Prepare your own dataset and put it under the dataset folder. Note that your own dataset should contain subfoloders: train0, train1_[style1name], train2_[style2name], ..., edge1_[style1name], edge2_[style2name], ..., test0. Different style names corresponding to different styles you have collected.
├─datasets
├─dataset name
├─ edge1_[style1name]
├─ edge2_[style2name]
├─ edge3_[style3name]
├─ test0
├─ train0
├─ train1_[style1name]
├─ train2_[style2name]
└─ train3_[style3name]- Download the vgg19.pth from BaiduYun (https://pan.baidu.com/s/1zHx06jMfIUIdBY_X3ko6LA, extract code is z9p7) or Googledrive (https://drive.google.com/drive/folders/1jOSl_sBpGzm1wAvLgU5vDJOV9tg2PzIc?usp=sharing). Put it under Multi-Style Photo Cartoonization folder.
- Train the model using the commond. Set --init to use initialization phase. If you have pretrained model, you can set --continue_train and --which_epoch simultaneously.
python train.py --init --name train --dataroot [your dataset path]- You can use your own trained model to test the model, also you can download pretrained models by the link below.
- Put test real-world photo in test0 folder of your dataset.
python test.py --name train --serial_test --dataroot [your dataset path] --which_epoch 145- CycleGAN (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)
- CartoonGAN (https://github.com/FlyingGoblin/CartoonGAN)
- ComboGAN (https://github.com/AAnoosheh/ComboGAN)