Focus:

     Different styles of training have different loss weights!

News:

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

• 1. Solve the problem of high-frequency artifacts in the generated image.

• 2. It is easy to train and directly achieve the effects in the paper.

• 3. Further reduce the number of parameters of the generator network. (generator size: 8.17 Mb)

• 4. Use new high-quality style data, which come from BD movies as much as possible.

        AnimeGAN can be accessed from here.

• 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

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

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

😍 Photo to Paprika Style

😍 Photo to Hayao Style

😍 Photo to Shinkai Style
TBD