  Diffusion-based generative models have significantly advanced text-to-image
generation but encounter challenges when processing lengthy and intricate text
prompts describing complex scenes with multiple objects. While excelling in
generating images from short, single-object descriptions, these models often
struggle to faithfully capture all the nuanced details within longer and more
elaborate textual inputs. In response, we present a novel approach leveraging
Large Language Models (LLMs) to extract critical components from text prompts,
including bounding box coordinates for foreground objects, detailed textual
descriptions for individual objects, and a succinct background context. These
components form the foundation of our layout-to-image generation model, which
operates in two phases. The initial Global Scene Generation utilizes object
layouts and background context to create an initial scene but often falls short
in faithfully representing object characteristics as specified in the prompts.
To address this limitation, we introduce an Iterative Refinement Scheme that
iteratively evaluates and refines box-level content to align them with their
textual descriptions, recomposing objects as needed to ensure consistency. Our
evaluation on complex prompts featuring multiple objects demonstrates a
substantial improvement in recall compared to baseline diffusion models. This
is further validated by a user study, underscoring the efficacy of our approach
in generating coherent and detailed scenes from intricate textual inputs.
