Recent text-to-image diffusion models have reached an unprecedented level in
generating high-quality images. However, their exclusive reliance on textual
prompts often falls short in accurately conveying fine-grained spatial
compositions. In this paper, we propose LoCo, a training-free approach for
layout-to-image synthesis that excels in producing high-quality images aligned
with both textual prompts and spatial layouts. Our method introduces a
Localized Attention Constraint to refine cross-attention for individual
objects, ensuring their precise placement in designated regions. We further
propose a Padding Token Constraint to leverage the semantic information
embedded in previously neglected padding tokens, thereby preventing the
undesired fusion of synthesized objects. LoCo seamlessly integrates into
existing text-to-image and layout-to-image models, significantly amplifying
their performance and effectively addressing semantic failures observed in
prior methods. Through extensive experiments, we showcase the superiority of
our approach, surpassing existing state-of-the-art training-free
layout-to-image methods both qualitatively and quantitatively across multiple
benchmarks.

本文中，我们提出了一种名为 LoCo 的无需训练的布局到图像合成方法，能够通过引入局部化注意约束和填充令牌约束，对个体对象进行精确定位并防止合成对象的不良融合，并成功地集成到现有的文本到图像和布局到图像模型中，在多个基准测试中定性和定量地超越了既有的最先进的无需训练的布局到图像方法，展示了我们方法的优越性。