Automatic layout generation that can synthesize high-quality layouts is an important tool for graphic design in many applications. Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the results. Inspired by the recent success of diffusion models in generating high-quality images, this paper explores their potential for conditional layout generation and proposes Transformer-based Layout Diffusion Model (LayoutDM) by instantiating the conditional denoising diffusion probabilistic model (DDPM) with a purely transformer-based architecture. Instead of using convolutional neural networks, a transformer-based conditional Layout Denoiser is proposed to learn the reverse diffusion process to generate samples from noised layout data. Benefitting from both transformer and DDPM, our LayoutDM is of desired properties such as high-quality generation, strong sample diversity, faithful distribution coverage, and stationary training in comparison to GANs and VAEs. Quantitative and qualitative experimental results show that our method outperforms state-of-the-art generative models in terms of quality and diversity.

Automatic layout generation models can generate numerous design layouts in a few seconds, which significantly reduces the amount of repetitive work for designers. However, most of these models consider the layout generation task as arranging layout elements with different attributes on a blank canvas, thus struggle to handle the case when an image is used as the layout background. Additionally, existing layout generation models often fail to incorporate explicit aesthetic principles such as alignment and non-overlap, and neglect implicit aesthetic principles which are hard to model. To address these issues, this paper proposes a two-stage content-aware layout generation framework for poster layout generation. Our framework consists of an aesthetics-conditioned layout generation module and a layout ranking module. The diffusion model based layout generation module utilizes an aesthetics-guided layout denoising process to sample layout proposals that meet explicit aesthetic constraints. The Auto-Encoder based layout ranking module then measures the distance between those proposals and real designs to determine the layout that best meets implicit aesthetic principles. Quantitative and qualitative experiments demonstrate that our method outperforms state-of-the-art content-aware layout generation models.