「Open Source」. The improved version of AnimeGAN.
「Project Page」 | Landscape photos/videos to anime
Focus:
| Anime style | Film | Picture Number | Quality | Download Style Dataset |
|---|---|---|---|---|
| Miyazaki Hayao | The Wind Rises | 1752 | 1080p | Link |
| Makoto Shinkai | Your Name & Weathering with you | 1445 | BD | |
| Kon Satoshi | Paprika | 1284 | BDRip |
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.
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2. It is easy to train and directly achieve the effects in the paper.
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3. Further reduce the number of parameters of the generator network. (generator size: 8.17 Mb), The lite version has a smaller generator model.
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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 edge_smooth.py --dataset Hayao --img_size 256
python data_mean.py --dataset Hayao
python main.py --phase train --dataset Hayao --data_mean [13.1360,-8.6698,-4.4661] --epoch 101 --init_epoch 10
For light version:python main.py --phase train --dataset Hayao --data_mean [13.1360,-8.6698,-4.4661] --light --epoch 101 --init_epoch 10
python get_generator_ckpt.py --checkpoint_dir ../checkpoint/AnimeGAN_Hayao_lsgan_300_300_1_2_10_1 --style_name Hayao
python test.py --checkpoint_dir checkpoint/generator_Hayao_weight --test_dir dataset/test/HR_photo --style_name Hayao/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
This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGANv2 given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.
Xin Chen