Attaining a high degree of user controllability in visual generation often
requires intricate, fine-grained inputs like layouts. However, such inputs
impose a substantial burden on users when compared to simple text inputs. To
address the issue, we study how Large Language Models (LLMs) can serve as
visual planners by generating layouts from text conditions, and thus
collaborate with visual generative models. We propose LayoutGPT, a method to
compose in-context visual demonstrations in style sheet language to enhance the
visual planning skills of LLMs. LayoutGPT can generate plausible layouts in
multiple domains, ranging from 2D images to 3D indoor scenes. LayoutGPT also
shows superior performance in converting challenging language concepts like
numerical and spatial relations to layout arrangements for faithful
text-to-image generation. When combined with a downstream image generation
model, LayoutGPT outperforms text-to-image models/systems by 20-40% and
achieves comparable performance as human users in designing visual layouts for
numerical and spatial correctness. Lastly, LayoutGPT achieves comparable
performance to supervised methods in 3D indoor scene synthesis, demonstrating
its effectiveness and potential in multiple visual domains.

LayoutGPT 是一种利用 LLMS 生成样式表语言的方法，能够生成多个视觉域中的可信布局，包括三维室内场景；当与下游图像生成模型相结合时，比文本到图像模型系统表现更好，并可在正确性方面与人类用户进行比较，同时在 3D 室内场景合成方面也与监督方法实现相当的性能。