  We introduce the GANformer2 model, an iterative object-oriented transformer,
explored for the task of generative modeling. The network incorporates strong
and explicit structural priors, to reflect the compositional nature of visual
scenes, and synthesizes images through a sequential process. It operates in two
stages: a fast and lightweight planning phase, where we draft a high-level
scene layout, followed by an attention-based execution phase, where the layout
is being refined, evolving into a rich and detailed picture. Our model moves
away from conventional black-box GAN architectures that feature a flat and
monolithic latent space towards a transparent design that encourages
efficiency, controllability and interpretability. We demonstrate GANformer2's
strengths and qualities through a careful evaluation over a range of datasets,
from multi-object CLEVR scenes to the challenging COCO images, showing it
successfully achieves state-of-the-art performance in terms of visual quality,
diversity and consistency. Further experiments demonstrate the model's
disentanglement and provide a deeper insight into its generative process, as it
proceeds step-by-step from a rough initial sketch, to a detailed layout that
accounts for objects' depths and dependencies, and up to the final
high-resolution depiction of vibrant and intricate real-world scenes. See
https://github.com/dorarad/gansformer for model implementation.
