  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 precise control of image 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 layout instructions. Specifically, we introduce a Localized Attention
Constraint (LAC), leveraging semantic affinity between pixels in self-attention
maps to create precise representations of desired objects and effectively
ensure the accurate placement of objects in designated regions. We further
propose a Padding Token Constraint (PTC) to leverage the semantic information
embedded in previously neglected padding tokens, improving the consistency
between object appearance and layout instructions. LoCo seamlessly integrates
into existing text-to-image and layout-to-image models, enhancing their
performance in spatial control and addressing semantic failures observed in
prior methods. Extensive experiments 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.
