Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.

翻译：摘要：可控布局生成旨在合成符合指定约束（如特定元素的类型或位置）的包围框排列方案。本文提出基于离散状态空间扩散模型的统一框架，以解决多种布局生成任务。所提出的LayoutDM模型能够自然处理离散表示的结构化布局数据，通过模态离散扩散过程对布局退化过程建模，并逐步从初始输入推断出无噪声布局。在条件生成场景中，我们提出在推理阶段通过掩码或逻辑调整注入布局约束。实验表明，我们的LayoutDM模型能成功生成高质量布局，并在多项布局任务中优于任务特定型与任务无关型基线方法。