Conditional-GAN

by m516825

Anime Generation

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Conditional GAN

Conditional Generative Adversarial Networks for anime generation (AnimeGAN).

image
Training results dump every 500 min-batch in 25 epoch(26000th min-batch) for the following tags - blue hair blue eyes
image - gray hair green eyes
image - green hair red eyes
image - orange hair brown eyes
image - blonde hair gray eyes
image - pink hair aqua eyes
image

Sample training data

image

Environment

python3
tensorflow 1.0
scipy

Model structure

image

Data

source link
google drive link

Usage

  1. Download hw3 data from data link, place the MLDSHW3dataset/ in the same directory and unzip the face.zip in MLDSHW3dataset/
  2. Replace the tags in MLDSHW3dataset/sampletestingtext.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 MLDSHW3dataset/sampletestingtext.txt every dumpevery batches to the testimg/ folder.

Testing tags format

1, hair  eyes 
2, hair  eyes
3, hair  eyes
4, hair  eyes
.
.
.
  • Possible colors for eyes
    ['', 'yellow', 'gray', 'blue', 'brown', 'red', 'green', 'purple', 'orange',
    'black', 'aqua', 'pink', 'bicolored']
    
  • Possible colors for hair
    ['', 'gray', 'blue', 'brown', 'red', 'blonde', 'green', 'purple', 'orange',
    'black', 'aqua', 'pink', 'white']
    

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