zhangqianhui / Progressive_growing_of_gans_tensorflow
Licence: mit
Tensorflow implementation of PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
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PGGAN-tensorflow
the Tensorflow implementation of PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION.
The generative process of PG-GAN
Differences with the original paper.
- Recently, just generate 64x64 and 128x128 pixels samples.
Setup
Prerequisites
- TensorFlow >= 1.4
- python 2.7 or 3
Getting Started
- Clone this repo:
git clone https://github.com/zhangqianhui/progressive_growing_of_gans_tensorflow.git
cd progressive_growing_of_gans_tensorflow
- Download the CelebA dataset
You can download the CelebA dataset and unzip CelebA into a directory. Noted that this directory don't contain the sub-directory.
-
The method for creating CelebA-HQ can be found on Method
-
Train the model on CelebA dataset
python main.py --path=your celeba data-path --celeba=True
- Train the model on CelebA-HQ dataset
python main.py --path=your celeba-hq data-path --celeba=False
Results on celebA dataset
Here is the generated 64x64 results(Left: generated; Right: Real):
Here is the generated 128x128 results(Left: generated; Right: Real):
Results on CelebA-HQ dataset
Here is the generated 64x64 results(Left: Real; Right: Generated):
Here is the generated 128x128 results(Left: Real; Right: Generated):
Issue
If you find some bugs, Thanks for your issue to propose it.
Reference code
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