DivCon: Divide and Conquer for Progressive Text-to-Image Generation
Yuhao Jia Wenhan Tan
Diffusion-driven text-to-image (T2I) generation has achieved remarkable
advancements. To further improve T2I models' capability in numerical and
spatial reasoning, the layout is employed as an intermedium to bridge large
language models and layout-based diffusion models. However, these methods still
struggle with generating images from textural prompts with multiple objects and
complicated spatial relationships. To tackle this challenge, we introduce a
divide-and-conquer approach which decouples the T2I generation task into simple
subtasks. Our approach divides the layout prediction stage into numerical \&
spatial reasoning and bounding box prediction. Then, the layout-to-image
generation stage is conducted in an iterative manner to reconstruct objects
from easy ones to difficult ones. We conduct experiments on the HRS and NSR-1K
benchmarks and our approach outperforms previous state-of-the-art models with
notable margins. In addition, visual results demonstrate that our approach
significantly improves the controllability and consistency in generating
multiple objects from complex textural prompts.