PGGAN Tensorflow

This repo is the TF2.0 implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Note that this implementation is not totally the same as the paper. There might be some differences.

Official implementation

Google Colab

Dataset

The notebook trains models using CelebAMask-HQ dataset.

Their paper

Environments

• Python 3
• jupyter or jupyterlab
• numpy
• matplotlib
• tensorflow 2.0

How to Run

1. Download the dataset you want.

2. Clone this repo, then use Juypter Notebook or Lab to open the PGGAN-Tensorflow.ipynb file, and modify the dataset path in the Prepare dataset section.

It takes me about 4~5 days to train from 4 x 4 to 128 x 128 resolution on single 1080ti GPU. (Not continuous training)

Training steps

The default setting is running on Tensorflow eager mode, this setting can support growing network, but the downside is the training speed. On the other hand, adding tf function decorator can almost double up the speed, however this setting doesn't work with auto growing network. Which means that when you want to train the next resolution model, you need to restart the notebook and adjust image_size and CURRENT_EPOCH.

For example, if the training of 64 x 64 model is finished and saved, and I want to train 128 x 128 model. I have to restart the notebook, adjust the image_size to 128, and CURRENT_EPOCH to 201 (By default, 40 epochs for one resolution in this notebook)

Trained Models

The model's code has been fixed, so the old 128x128 trained model wouldn't work.

4 x 4

8 x 8

16 x 16

32 x 32

Results

4 x 4 resolution

8 x 8 resolution

16 x 16 resolution

32 x 32 resolution

64 x 64 resolution

128 x 128 resolution

Key Modules

Please check modules.md.

Acknowledges

TKarras's PGGAN repository, https://github.com/tkarras/progressive_growing_of_gans