  We introduce the GANformer, a novel and efficient type of transformer, and
explore it for the task of visual generative modeling. The network employs a
bipartite structure that enables long-range interactions across the image,
while maintaining computation of linear efficiency, that can readily scale to
high-resolution synthesis. It iteratively propagates information from a set of
latent variables to the evolving visual features and vice versa, to support the
refinement of each in light of the other and encourage the emergence of
compositional representations of objects and scenes. In contrast to the classic
transformer architecture, it utilizes multiplicative integration that allows
flexible region-based modulation, and can thus be seen as a generalization of
the successful StyleGAN network. We demonstrate the model's strength and
robustness through a careful evaluation over a range of datasets, from
simulated multi-object environments to rich real-world indoor and outdoor
scenes, showing it achieves state-of-the-art results in terms of image quality
and diversity, while enjoying fast learning and better data-efficiency. Further
qualitative and quantitative experiments offer us an insight into the model's
inner workings, revealing improved interpretability and stronger
disentanglement, and illustrating the benefits and efficacy of our approach. An
implementation of the model is available at
https://github.com/dorarad/gansformer.
