  Text-to-Image Diffusion Models (T2I DMs) have garnered significant attention
for their ability to generate high-quality images from textual descriptions.
However, these models often produce images that do not fully align with the
input prompts, resulting in semantic inconsistencies. The most prominent issue
among these semantic inconsistencies is catastrophic-neglect, where the images
generated by T2I DMs miss key objects mentioned in the prompt. We first conduct
an empirical study on this issue, exploring the prevalence of
catastrophic-neglect, potential mitigation strategies with feature enhancement,
and the insights gained. Guided by the empirical findings, we propose an
automated repair approach named Patcher to address catastrophic-neglect in T2I
DMs. Specifically, Patcher first determines whether there are any neglected
objects in the prompt, and then applies attention-guided feature enhancement to
these neglected objects, resulting in a repaired prompt. Experimental results
on three versions of Stable Diffusion demonstrate that Patcher effectively
repairs the issue of catastrophic-neglect, achieving 10.1%-16.3% higher Correct
Rate in image generation compared to baselines.
