Wasserstein GAN (or WGAN) is a variant of Generative Adversarial Networks recently proposed by Martin Arjovsky, Soumith Chintala, and Léon Bottou, check out the paper here and the reference implementation here. Using some relatively simple algorithm changes (and less simple math), WGANs seem to greatly improve the notoriously unstable training of GANs for image generation.

• Tensorflow
• Your own dataset loader

• Create some boilerplate code to load your data into batches and then place that where the "YOURDATAHERE" placeholder is (line 93)
• Fiddle with the parameters at the top of the file (and/or add your own architectures for the generator and discriminator)
• Run it! python train_WGAN.py

The training can be stopped with Ctrl-C, which will cause it to save a checkpoint, so don't be surprised it doesn't shut down right away.

You can easily drop in any replacement architectures for the discriminator or generator networks (just modify the generator and discriminator functions). WGAN is solely a training algorithm trick and should work with any architecture. The default nets are both fully convolution DCGANs for 128x128x3 images and NOT identical to those used in the paper.