  Spatial control is a core capability in controllable image generation.
Advancements in layout-guided image generation have shown promising results on
in-distribution (ID) datasets with similar spatial configurations. However, it
is unclear how these models perform when facing out-of-distribution (OOD)
samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench,
a diagnostic benchmark for layout-guided image generation that examines four
categories of spatial control skills: number, position, size, and shape. We
benchmark two recent representative layout-guided image generation methods and
observe that the good ID layout control may not generalize well to arbitrary
layouts in the wild (e.g., objects at the boundary). Next, we propose
IterInpaint, a new baseline that generates foreground and background regions
step-by-step via inpainting, demonstrating stronger generalizability than
existing models on OOD layouts in LayoutBench. We perform quantitative and
qualitative evaluation and fine-grained analysis on the four LayoutBench skills
to pinpoint the weaknesses of existing models. We show comprehensive ablation
studies on IterInpaint, including training task ratio, crop&paste vs. repaint,
and generation order. Lastly, we evaluate the zero-shot performance of
different pretrained layout-guided image generation models on LayoutBench-COCO,
our new benchmark for OOD layouts with real objects, where our IterInpaint
consistently outperforms SOTA baselines in all four splits. Project website:
https://layoutbench.github.io
