A Tensorflow implementation of AnimeGAN for fast photo animation ! This is the Open source of the paper <AnimeGAN: a novel lightweight GAN for photo animation>, which uses the GAN framwork to transform real-world photos into anime images.

AnimeGAN

A Tensorflow implementation of AnimeGAN for fast photo animation !!!

This is the Open source of the paper <AnimeGAN: a novel lightweight GAN for photo animation>, which uses the GAN framwork to transform real-world photos into anime images.

Some suggestions: since the real photos in the training set are all landscape photos, if you want to stylize the photos with people as the main body, you may as well add at least 3000 photos of people in the training set and retrain to obtain a new model.

Requirements

• python 3.6.8
• tensorflow-gpu 1.8
• opencv
• tqdm
• numpy
• glob
• argparse

Usage

1. Download vgg19 or Pretrained model

vgg19.npy

Pretrained model

2. Download dataset

Link

3. Do edge_smooth

eg. python edge_smooth.py --dataset Haoyao --img_size 256

3. Train

eg. python main.py --phase train --dataset Haoyao --epoch 101 --init_epoch 1

4. Test

eg. python main.py --phase test --dataset Hayao
or python test.py --checkpoint_dir checkpoint/AnimeGAN_Hayao_lsgan_300_300_1_3_10 --test_dir dataset/test/real --style_name H

Results

——> pictures from the paper ‘AnimeGAN: a novel lightweight GAN for photo animation’

——> Photo to Hayao Style

Acknowledgment

This code is based on the CartoonGAN-Tensorflow and Anime-Sketch-Coloring-with-Swish-Gated-Residual-UNet. Thanks to the contributors of this project.