  In spite of the rapidly evolving landscape of text-to-image generation, the
synthesis and manipulation of multiple entities while adhering to specific
relational constraints pose enduring challenges. This paper introduces an
innovative progressive synthesis and editing operation that systematically
incorporates entities into the target image, ensuring their adherence to
spatial and relational constraints at each sequential step. Our key insight
stems from the observation that while a pre-trained text-to-image diffusion
model adeptly handles one or two entities, it often falters when dealing with a
greater number. To address this limitation, we propose harnessing the
capabilities of a Large Language Model (LLM) to decompose intricate and
protracted text descriptions into coherent directives adhering to stringent
formats. To facilitate the execution of directives involving distinct semantic
operations-namely insertion, editing, and erasing-we formulate the Stimulus,
Response, and Fusion (SRF) framework. Within this framework, latent regions are
gently stimulated in alignment with each operation, followed by the fusion of
the responsive latent components to achieve cohesive entity manipulation. Our
proposed framework yields notable advancements in object synthesis,
particularly when confronted with intricate and lengthy textual inputs.
Consequently, it establishes a new benchmark for text-to-image generation
tasks, further elevating the field's performance standards.
