DCGAN for CelebA in PyTorch

This repository contains an example implementation of a DCGAN architecture written in PyTroch.

For the demonstration, I've used CelebA dataset.

How to run it?

Training and visualization should work without any modifications and default arguments will reproduce my results. Hyperparameters were taken from the papers but can be tuned by passing arguments to below scripts.

Download dataset:

1. Use this Google Drive to download images (URL points to original dataset shared by its authors).

2. Put it into dataset directory and unpack.

3. Wrap unpacked directory (img_align_celeba) into another one named celeba. This is a requirement set by PyTorch's implementation of ImageFolder.

Instruction:

$ virtualenv -p python3.7 venv
$ . venv/bin/activate
(venv) $ pip install -r requirements.txt
(venv) $ python train.py
(venv) $ python visualize.py --checkpoint={YOUR_CHECKPOINT}

Help:

(venv) $ python train.py -h
(venv) $ python visualize.py -h

Training

It takes about 50 epochs to train DCGAN that fools discriminator on ~90% of fake images. Feel free to run it for longer time! A single epoch takes about ~8:45 minutes on the GTX 1060 6GB.

Results

Visualization of example images

Visualization of latent space

Example 1	Example 2	Example 3	Example 4

DCGAN Architecture

Resources

• [arXiv] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio "Generative Adversarial Networks"

• [arXiv] Alec Radford, Luke Metz, Soumith Chintala "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"