Content-aware graphic layout generation aims to automatically arrange visual
elements along with a given content, such as an e-commerce product image. In
this paper, we argue that the current layout generation approaches suffer from
the limited training data for the high-dimensional layout structure. We show
that a simple retrieval augmentation can significantly improve the generation
quality. Our model, which is named Retrieval-Augmented Layout Transformer
(RALF), retrieves nearest neighbor layout examples based on an input image and
feeds these results into an autoregressive generator. Our model can apply
retrieval augmentation to various controllable generation tasks and yield
high-quality layouts within a unified architecture. Our extensive experiments
show that RALF successfully generates content-aware layouts in both constrained
and unconstrained settings and significantly outperforms the baselines.

本文介绍了一种名为 RALF 的模型，它通过检索相似的布局示例来提高内容感知图形布局的生成质量，并成功地在不受限制和受限制的环境中生成高质量的布局。

检索增强式布局变换器用于内容感知布局生成

Retrieval-Augmented Layout Transformer for Content-Aware Layout  Generation

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。