DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer
This addresses layout generation for graphic design applications, enabling user interaction through flexible conditioning, but it is incremental as it adapts diffusion models to a mixed representation.
The paper tackles the problem of generating visual layouts conditioned on partial attributes by introducing DLT, a joint discrete-continuous diffusion model, which outperforms state-of-the-art methods on various datasets with improved metrics.
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.