Conditional GAN

Conditional Generative Adversarial Networks for anime generation (AnimeGAN).

Training results dump every 500 min-batch in 25 epoch(26000th min-batch) for the following tags

• blue hair blue eyes
  
• gray hair green eyes
  
• green hair red eyes
  
• orange hair brown eyes
  
• blonde hair gray eyes
  
• pink hair aqua eyes
  

Sample training data

Environment

python3
tensorflow 1.0
scipy

Model structure

Data

source link
google drive link

Usage

1. Download hw3 data from data link, place the MLDS_HW3_dataset/ in the same directory and unzip the face.zip in MLDS_HW3_dataset/
2. Replace the tags in MLDS_HW3_dataset/sample_testing_text.txt to the right format.
3. Start training !

Train

First time use, you need to do the preprocessing

$ python3 main.py --prepro 1

If you already have done the preprocessing

$ python3 main.py --prepro 0

Model

• dcgan structure
• use one hot encoding for condition tags

Test

This code will automatically dump the results for the tags specified in MLDS_HW3_dataset/sample_testing_text.txt every dump_every batches to the test_img/ folder.

Testing tags format

1,<Color> hair <Color> eyes 
2,<Color> hair <Color> eyes
3,<Color> hair <Color> eyes
4,<Color> hair <Color> eyes
.
.
.

• Possible colors for eyes

['<UNK>', 'yellow', 'gray', 'blue', 'brown', 'red', 'green', 'purple', 'orange',
 'black', 'aqua', 'pink', 'bicolored']

• Possible colors for hair

['<UNK>', 'gray', 'blue', 'brown', 'red', 'blonde', 'green', 'purple', 'orange',
 'black', 'aqua', 'pink', 'white']