  Controllable layout generation refers to the process of creating a plausible
visual arrangement of elements within a graphic design (e.g., document and web
designs) with constraints representing design intentions. Although recent
diffusion-based models have achieved state-of-the-art FID scores, they tend to
exhibit more pronounced misalignment compared to earlier transformer-based
models. In this work, we propose the $\textbf{LA}$yout $\textbf{C}$onstraint
diffusion mod$\textbf{E}$l (LACE), a unified model to handle a broad range of
layout generation tasks, such as arranging elements with specified attributes
and refining or completing a coarse layout design. The model is based on
continuous diffusion models. Compared with existing methods that use discrete
diffusion models, continuous state-space design can enable the incorporation of
differentiable aesthetic constraint functions in training. For conditional
generation, we introduce conditions via masked input. Extensive experiment
results show that LACE produces high-quality layouts and outperforms existing
state-of-the-art baselines.
