  Generating visual layouts is an essential ingredient of graphic design. The
ability to condition layout generation on a partial subset of component
attributes is critical to real-world applications that involve user
interaction. Recently, diffusion models have demonstrated high-quality
generative performances in various domains. However, it is unclear how to apply
diffusion models to the natural representation of layouts which consists of a
mix of discrete (class) and continuous (location, size) attributes. To address
the conditioning layout generation problem, we introduce DLT, a joint
discrete-continuous diffusion model. DLT is a transformer-based model which has
a flexible conditioning mechanism that allows for conditioning on any given
subset of all the layout component classes, locations, and sizes. Our method
outperforms state-of-the-art generative models on various layout generation
datasets with respect to different metrics and conditioning settings.
Additionally, we validate the effectiveness of our proposed conditioning
mechanism and the joint continuous-diffusion process. This joint process can be
incorporated into a wide range of mixed discrete-continuous generative tasks.
