  Recent breakthroughs in text-to-image diffusion models have significantly
advanced the generation of high-fidelity, photo-realistic images from textual
descriptions. Yet, these models often struggle with interpreting spatial
arrangements from text, hindering their ability to produce images with precise
spatial configurations. To bridge this gap, layout-to-image generation has
emerged as a promising direction. However, training-based approaches are
limited by the need for extensively annotated datasets, leading to high data
acquisition costs and a constrained conceptual scope. Conversely, training-free
methods face challenges in accurately locating and generating semantically
similar objects within complex compositions. This paper introduces a novel
training-free approach designed to overcome adversarial semantic intersections
during the diffusion conditioning phase. By refining intra-token loss with
selective sampling and enhancing the diffusion process with attention
redistribution, we propose two innovative constraints: 1) an inter-token
constraint that resolves token conflicts to ensure accurate concept synthesis;
and 2) a self-attention constraint that improves pixel-to-pixel relationships.
Our evaluations confirm the effectiveness of leveraging layout information for
guiding the diffusion process, generating content-rich images with enhanced
fidelity and complexity. Code is available at
https://github.com/Papple-F/csg.git.
