AnimeGAN

A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

Randomly Generated Images

The images are generated from a DCGAN model trained on 143,000 anime character faces for 100 epochs.

Image Transition

Manipulating latent codes, enables the transition from images in the first row to the last row.

Original Images

The images are not clean, some outliers can be observed, which degrades the quality of the generated images.

Usage

To run the experiment,

$ python main.py --dataRoot path_to_dataset/ 

The pretrained model for DCGAN are also in this repo, play it inside the jupyter notebook.

AnimeFace Dataset

Anime-style images of 126 tags are collected from danbooru.donmai.us using the crawler tool gallery-dl. The images are then processed by a anime face detector python-animeface. The resulting dataset contains ~143,000 anime faces. Note that some of the tags may no longer meaningful after cropping, i.e. the cropped face images under 'uniform' tag may not contain visible parts of uniforms.

How to construct the dataset from scratch ?

Prequisites: gallery-dl, python-animeface

1. Download anime-style images

   # download 1000 images under the tag "misaka_mikoto"
   gallery-dl --images 1000 "https://danbooru.donmai.us/posts?tags=misaka_mikoto"
   
   # In a multi-processing manner
   cat tags.txt | \
   xargs -n 1 -P 12 -I 'tag' \ 
   bash -c ' gallery-dl --images 1000 "https://danbooru.donmai.us/posts?tags=$tag" '

2. Extract faces from the downloaded images

   import animeface
   from PIL import Image
   
   im = Image.open('images/anime_image_misaka_mikoto.png')
   faces = animeface.detect(im)
   x,y,w,h = faces[0].face.pos
   im = im.crop((x,y,x+w,y+h))
   im.show() # display

Dig into build_face_dataset.py to find more settings that I used.

The dataset can also be downloaded from here, https://pan.baidu.com/s/1pLVpgEJ (~400MB), non-commercial use please.

Others

1. This project is heavily influenced by chainer-DCGAN and IllustrationGAN, the codes are mostly borrowed from PyTorch DCGAN example, thanks the authors for the clean codes.
2. Dependencies: pytorch, torchvision
3. This is a toy project for me to learn PyTorch and GANs, most importantly, for fun! :) Any feedback is welcome.

@jayleicn