Region-Aware Text-to-Image Generation via Hard Binding and Soft
  Refinement
Authors: name: Zhennan Chen, name: Yajie Li, name: Haofan Wang, name: Zhibo Chen, name: Zhengkai Jiang, name: Jun Li, name: Qian Wang, name: Jian Yang, name: Ying Tai

In this paper, we present RAG, a Regional-Aware text-to-image Generation
method conditioned on regional descriptions for precise layout composition.
Regional prompting, or compositional generation, which enables fine-grained
spatial control, has gained increasing attention for its practicality in
real-world applications. However, previous methods either introduce additional
trainable modules, thus only applicable to specific models, or manipulate on
score maps within cross-attention layers using attention masks, resulting in
limited control strength when the number of regions increases. To handle these
limitations, we decouple the multi-region generation into two sub-tasks, the
construction of individual region (Regional Hard Binding) that ensures the
regional prompt is properly executed, and the overall detail refinement
(Regional Soft Refinement) over regions that dismiss the visual boundaries and
enhance adjacent interactions. Furthermore, RAG novelly makes repainting
feasible, where users can modify specific unsatisfied regions in the last
generation while keeping all other regions unchanged, without relying on
additional inpainting models. Our approach is tuning-free and applicable to
other frameworks as an enhancement to the prompt following property.
Quantitative and qualitative experiments demonstrate that RAG achieves superior
performance over attribute binding and object relationship than previous
tuning-free methods.