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.

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

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

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