TextCenGen: Attention-Guided Text-Centric Background Adaptation for
  Text-to-Image Generation
Tianyi Liang Jiangqi Liu Sicheng Song Shiqi Jiang Yifei Huang Changbo Wang Chenhui Li
Recent advancements in Text-to-image (T2I) generation have witnessed a shift
from adapting text to fixed backgrounds to creating images around text.
Traditional approaches are often limited to generate layouts within static
images for effective text placement. Our proposed approach, TextCenGen,
introduces a dynamic adaptation of the blank region for text-friendly image
generation, emphasizing text-centric design and visual harmony generation. Our
method employs force-directed attention guidance in T2I models to generate
images that strategically reserve whitespace for pre-defined text areas, even
for text or icons at the golden ratio. Observing how cross-attention maps
affect object placement, we detect and repel conflicting objects using a
force-directed graph approach, combined with a Spatial Excluding
Cross-Attention Constraint for smooth attention in whitespace areas. As a novel
task in graphic design, experiments indicate that TextCenGen outperforms
existing methods with more harmonious compositions. Furthermore, our method
significantly enhances T2I model outcomes on our specially collected prompt
datasets, catering to varied text positions. These results demonstrate the
efficacy of TextCenGen in creating more harmonious and integrated text-image
compositions.