Reason out Your Layout: Evoking the Layout Master from Large Language
  Models for Text-to-Image Synthesis
Xiaohui Chen Yongfei Liu Yingxiang Yang Jianbo Yuan Quanzeng You Li-Ping Liu Hongxia Yang
Recent advancements in text-to-image (T2I) generative models have shown
remarkable capabilities in producing diverse and imaginative visuals based on
text prompts. Despite the advancement, these diffusion models sometimes
struggle to translate the semantic content from the text into images entirely.
While conditioning on the layout has shown to be effective in improving the
compositional ability of T2I diffusion models, they typically require manual
layout input. In this work, we introduce a novel approach to improving T2I
diffusion models using Large Language Models (LLMs) as layout generators. Our
method leverages the Chain-of-Thought prompting of LLMs to interpret text and
generate spatially reasonable object layouts. The generated layout is then used
to enhance the generated images' composition and spatial accuracy. Moreover, we
propose an efficient adapter based on a cross-attention mechanism, which
explicitly integrates the layout information into the stable diffusion models.
Our experiments demonstrate significant improvements in image quality and
layout accuracy, showcasing the potential of LLMs in augmenting generative
image models.