  Layout generation aims to synthesize realistic graphic scenes consisting of
elements with different attributes including category, size, position, and
between-element relation. It is a crucial task for reducing the burden on
heavy-duty graphic design works for formatted scenes, e.g., publications,
documents, and user interfaces (UIs). Diverse application scenarios impose a
big challenge in unifying various layout generation subtasks, including
conditional and unconditional generation. In this paper, we propose a Layout
Diffusion Generative Model (LDGM) to achieve such unification with a single
decoupled diffusion model. LDGM views a layout of arbitrary missing or coarse
element attributes as an intermediate diffusion status from a completed layout.
Since different attributes have their individual semantics and characteristics,
we propose to decouple the diffusion processes for them to improve the
diversity of training samples and learn the reverse process jointly to exploit
global-scope contexts for facilitating generation. As a result, our LDGM can
generate layouts either from scratch or conditional on arbitrary available
attributes. Extensive qualitative and quantitative experiments demonstrate our
proposed LDGM outperforms existing layout generation models in both
functionality and performance.
