Improved Training of Wasserstein GANs

This is a project test Wasserstein GAN objectives on single image super-resolution. The code is built on a fork of the popular project under the same title.

We expand the original repo by another model gan_SR.py: GANs that generate 64x64-pixel images from 16x16-low-resolution inputs. The default dataset is the cropped aligned celebA dataset. Other dataset would require minor modifications.

Data preprocessing is required before training:

bash celebA_preprocess.sh

will download celebA dataset, crop and resize to 64x64. Note this might take a while.

To run, modify the path of data directory, summary directory (for tensorboard) and output directory in run_script.sh.

bash run_script.sh

Here is a sample output, using WGAN-GP objective and DCGAN architecture and 1 epoch of training. From left to right are low resolution input, bicubic interpolation, wgangp output and original image.

Description of the original repository

Code for reproducing experiments in "Improved Training of Wasserstein GANs".

Prerequisites

• Python, NumPy, TensorFlow, SciPy, Matplotlib
• A recent NVIDIA GPU

Models

Configuration for all models is specified in a list of constants at the top of the file. Two models should work "out of the box":

• python gan_toy.py: Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).
• python gan_mnist.py: MNIST

For the other models, edit the file to specify the path to the dataset in DATA_DIR before running. Each model's dataset is publicly available; the download URL is in the file.

• python gan_64x64.py: 64x64 architectures (this code trains on ImageNet instead of LSUN bedrooms in the paper)
• python gan_language.py: Character-level language model
• python gan_cifar.py: CIFAR-10