Progressive Growing of GANs implemented with chainer
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README.md
analogy.py
batch.sh
dataset.py
functions.py
network.py first commit Nov 7, 2017
train.py
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utils.py

README.md

chainer-PGGAN

Progressive Growing of GANs implemented with chainer

python 3.5.2 + chainer 3.0.0

Usage

Training

$ python3 ./train.py -g 0 --dir ./train_images/ --epoch 100 --depth 0 

You can train models with ./train.py.

When depth = n, generated images are 2^{n+2} x 2^{n+2} size.

$ ./batch.sh 100

batch.sh automatically trains models gradually (through 4 x 4 to 256 x 256).

You should tune delta and epoch when it changes too quickly or too slowly.

Generating

$ python3 ./analogy.py --gen results/gen --depth 0

You can generate images with analogy.py

$ wget https://www.dropbox.com/s/dvnxb4vur6fasei/gen_yui_model
$ python3 ./analogy.py --gen gen_yui_model --depth 6

You can use the pre-trained model.

It generates 256 x 256 size Ichii Yui's images.

Bibliography

[1] http://research.nvidia.com/publication/2017-10_Progressive-Growing-of

The original paper

[2] https://github.com/dhgrs/chainer-WGAN-GP

WGAN-GP implemented with chainer.

[3] http://joisino.hatenablog.com/entry/2017/11/07/200000

My Blog post related to this repository.