Controllable layout generation refers to the process of creating a plausible
visual arrangement of elements within a graphic design (e.g., document and web
designs) with constraints representing design intentions. Although recent
diffusion-based models have achieved state-of-the-art FID scores, they tend to
exhibit more pronounced misalignment compared to earlier transformer-based
models. In this work, we propose the $\textbf{LA}$yout $\textbf{C}$onstraint
diffusion mod$\textbf{E}$l (LACE), a unified model to handle a broad range of
layout generation tasks, such as arranging elements with specified attributes
and refining or completing a coarse layout design. The model is based on
continuous diffusion models. Compared with existing methods that use discrete
diffusion models, continuous state-space design can enable the incorporation of
differentiable aesthetic constraint functions in training. For conditional
generation, we introduce conditions via masked input. Extensive experiment
results show that LACE produces high-quality layouts and outperforms existing
state-of-the-art baselines.

提出了一种统一模型，用于处理广泛的布局生成任务，包括使用指定属性排列元素和完善粗糙布局设计。该模型基于连续扩散模型，并通过掩码输入引入条件。广泛的实验结果表明，该模型能够产生高质量的布局，并且优于现有的最先进基线。

通过具有美学约束的扩散模型实现对齐布局生成

Towards Aligned Layout Generation via Diffusion Model with Aesthetic  Constraints

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.

本研究旨在以离散状态空间扩散模型为基础，通过控制布局生成算法的可控布局生成方式，包括类型或特定元素的位置等来解决各种布局生成任务。最终我们提出的 “LayoutDM” 模型能够自然地处理离散表示中的结构化布局数据，并通过模态逐渐推断出无噪声的布局。对于条件生成，我们建议在推理期间注入掩蔽或对数调整的布局约束。我们在实验中证明，我们的 LayoutDM 成功地生成高质量的布局，并在几个布局任务上优于特定于任务和不特定于任务的基线模型。