AnimeGANv2

[Open Source]. The improved version of AnimeGAN.
Landscape photos/videos to anime

Focus:

     Different styles of training have different loss weights!

News:

The improvement directions of AnimeGANv2 mainly include the following 4 points:  

• [x] 1. Solve the problem of high-frequency artifacts in the generated image.
• [x] 2. It is easy to train and directly achieve the effects in the paper.
• [x] 3. Further reduce the number of parameters of the generator network. (generator size: 8.17 Mb)
• [x] 4. Use new high-quality style data, which come from BD movies as much as possible.

      AnimeGAN can be accessed from here.

Requirements

• python 3.6
• tensorflow-gpu
• tensorflow-gpu 1.8.0 (ubuntu, GPU 1080Ti or Titan xp, cuda 9.0, cudnn 7.1.3)
• tensorflow-gpu 1.15.0 (ubuntu, GPU 2080Ti, cuda 10.0.130, cudnn 7.6.0)
• opencv
• tqdm
• numpy
• glob
• argparse

Usage

1. Inference

python test.py --checkpoint_dir checkpoint/generator_Hayao_weight --test_dir dataset/test/HR_photo --style_name Hayao/HR_photo

2. Convert video to anime

python video2anime.py --video video/input/お花見.mp4 --checkpoint_dir checkpoint/generator_Paprika_weight

Results

:heart_eyes: Photo to Paprika Style

:heart_eyes: Photo to Hayao Style

:heart_eyes: Photo to Shinkai Style