Generative Adversarial Networks (GANs) are a very promising category of generative models used to approximate unknown data distributions for sampling purposes. Nevertheless, their training instability problems have hindered the possibility of experimenting with a wide variety of different GANs architectures. The introduction of Wasserstein GAN and Wasserstein GAN-GP overcomes such limitation providing the possibility to successfully train a broader class of architectures without instability or convergence issues. Among the possible model architectures for image generation task, convolutional neural networks (CNN) excels as for many other subfields in deep learning. Notwithstanding the nice properties of convolutional layers, which are the building blocks of CNNs, the convolution is a local operator and for this reason lacks to effectively capture long-term dependencies, which are fundamental for reproducing plausible samples for image classes that present a well-determined structure.