PyTorch implementation of style transfer (landscape cartoonization) models

• CycleGAN
• CartoonGAN
• AnimeGAN

• Pytorch
• torchvision
• numpy
• PIL
• OpenCV
• tqdm
• click

1. Download dataset

• CycleGAN: Link
• CartoonGAN, AnimeGAN: Link

2. Place data

e.g.

.
└── data
    ├── summer2winter_yosemite
    |   ├── trainA
    |   ├── trainB
    |   ├── testA
    |   └── testB
    └── cartoon_dataset
        ├── photo
        |   ├── 0.jpg
        |   └── ...
        ├── cartoon
        |   ├── 0_1.jpg
        |   └── ...
        ├── cartoon_smoothed
        |   ├── 0_1.jpg
        |   └── ...
        └── val
            ├── 1.jpg
            └── ...

3. Train

• CycleGAN: python train_cyclegan.py

• CartoonGAN: python train_cartoongan.py

• AnimeGAN: python train_animegan.py

• arguments

  • dataset_type (only for CycleGAN): folder name to use for dataset (e.g. summer2winter_yosemite)
  • load_model: True/False
  • cuda_visible: CUDA_VISIBLE_DEVICES (e.g. 1)

4. Test

• CycleGAN: python test_cyclegan.py

• CartoonGAN: python test_cartoongan.py

• AnimeGAN: python test_cartoongan.py --model_name=animegan

• arguments

  • dataset_type (only for CycleGAN)
  • model_type (only for CycleGAN): x2y/y2x
  • image_path: folder path to convert the images
  • cuda_visible
  • model_name (only for CartoonGAN): cartoongan/animegan
  • is_crop (only for CartoonGAN): True/False. crop and resize image to (256, 256)

• We trained the models with Shinkai style

Photo	CartoonGAN	AnimeGAN

• AnimeGAN kept the original texture and color than CartoonGAN.
• CartoonGAN made good use of the unique black edge of cartoons.
• CartoonGAN and AnimeGAN were close to the texture of TVD/Movie, respectively.
• AnimeGAN did not reduce the Discriminator adversial loss from certain point.
• In CartoonGAN, the color expressions changed as the epoch increases, and was unified for all generated images.
• The performance was better when using generated cartoon data by cropping the high-resolution images than resizing them.

• https://github.com/TobiasSunderdiek/cartoon-gan
• https://github.com/mnmjh1215/diya-CartoonGAN
• https://github.com/TachibanaYoshino/AnimeGAN