Grounded Text-to-Image Synthesis with Attention Refocusing
Authors: name: Quynh Phung, name: Songwei Ge, name: Jia-Bin Huang

Driven by scalable diffusion models trained on large-scale paired text-image
datasets, text-to-image synthesis methods have shown compelling results.
However, these models still fail to precisely follow the text prompt when
multiple objects, attributes, and spatial compositions are involved in the
prompt. In this paper, we identify the potential reasons in both the
cross-attention and self-attention layers of the diffusion model. We propose
two novel losses to refocus the attention maps according to a given layout
during the sampling process. We perform comprehensive experiments on the
DrawBench and HRS benchmarks using layouts synthesized by Large Language
Models, showing that our proposed losses can be integrated easily and
effectively into existing text-to-image methods and consistently improve their
alignment between the generated images and the text prompts.