  Creating graphic layouts is a fundamental step in graphic designs. In this
work, we present a novel generative model named LayoutDiffusion for automatic
layout generation. As layout is typically represented as a sequence of discrete
tokens, LayoutDiffusion models layout generation as a discrete denoising
diffusion process. It learns to reverse a mild forward process, in which
layouts become increasingly chaotic with the growth of forward steps and
layouts in the neighboring steps do not differ too much. Designing such a mild
forward process is however very challenging as layout has both categorical
attributes and ordinal attributes. To tackle the challenge, we summarize three
critical factors for achieving a mild forward process for the layout, i.e.,
legality, coordinate proximity and type disruption. Based on the factors, we
propose a block-wise transition matrix coupled with a piece-wise linear noise
schedule. Experiments on RICO and PubLayNet datasets show that LayoutDiffusion
outperforms state-of-the-art approaches significantly. Moreover, it enables two
conditional layout generation tasks in a plug-and-play manner without
re-training and achieves better performance than existing methods.
