  Transformer models have recently attracted much interest from computer vision
researchers and have since been successfully employed for several problems
traditionally addressed with convolutional neural networks. At the same time,
image synthesis using generative adversarial networks (GANs) has drastically
improved over the last few years. The recently proposed TransGAN is the first
GAN using only transformer-based architectures and achieves competitive results
when compared to convolutional GANs. However, since transformers are
data-hungry architectures, TransGAN requires data augmentation, an auxiliary
super-resolution task during training, and a masking prior to guide the
self-attention mechanism. In this paper, we study the combination of a
transformer-based generator and convolutional discriminator and successfully
remove the need of the aforementioned required design choices. We evaluate our
approach by conducting a benchmark of well-known CNN discriminators, ablate the
size of the transformer-based generator, and show that combining both
architectural elements into a hybrid model leads to better results.
Furthermore, we investigate the frequency spectrum properties of generated
images and observe that our model retains the benefits of an attention based
generator.
