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.

翻译：自动布局生成能够合成高质量布局，是许多应用场景中图形设计的重要工具。尽管基于生成对抗网络（GANs）和变分自编码器（VAEs）等生成模型的现有方法已取得进展，但在结果质量与多样性方面仍有较大提升空间。受扩散模型在高质量图像生成领域最新成功的启发，本文探索了其在条件式布局生成中的潜力，并提出基于Transformer的布局扩散模型（LayoutDM），该模型通过将条件去噪扩散概率模型（DDPM）实例化为纯Transformer架构实现。不同于使用卷积神经网络，本文提出基于Transformer的条件式布局去噪器，通过学习逆向扩散过程，从噪声布局数据中生成样本。得益于Transformer与DDPM的双重优势，与GANs和VAEs相比，我们的LayoutDM具备理想特性，包括高质量生成、强样本多样性、忠实分布覆盖以及稳定训练。定量与定性实验结果表明，本方法在质量和多样性方面均优于当前最先进的生成模型。