  Content-aware graphic layout generation aims to automatically arrange visual
elements along with a given content, such as an e-commerce product image. In
this paper, we argue that the current layout generation approaches suffer from
the limited training data for the high-dimensional layout structure. We show
that a simple retrieval augmentation can significantly improve the generation
quality. Our model, which is named Retrieval-Augmented Layout Transformer
(RALF), retrieves nearest neighbor layout examples based on an input image and
feeds these results into an autoregressive generator. Our model can apply
retrieval augmentation to various controllable generation tasks and yield
high-quality layouts within a unified architecture. Our extensive experiments
show that RALF successfully generates content-aware layouts in both constrained
and unconstrained settings and significantly outperforms the baselines.
