  Recent work has identified substantial disparities in generated images of
different geographic regions, including stereotypical depictions of everyday
objects like houses and cars. However, existing measures for these disparities
have been limited to either human evaluations, which are time-consuming and
costly, or automatic metrics evaluating full images, which are unable to
attribute these disparities to specific parts of the generated images. In this
work, we introduce a new set of metrics, Decomposed Indicators of Disparities
in Image Generation (Decomposed-DIG), that allows us to separately measure
geographic disparities in the depiction of objects and backgrounds in generated
images. Using Decomposed-DIG, we audit a widely used latent diffusion model and
find that generated images depict objects with better realism than backgrounds
and that backgrounds in generated images tend to contain larger regional
disparities than objects. We use Decomposed-DIG to pinpoint specific examples
of disparities, such as stereotypical background generation in Africa,
struggling to generate modern vehicles in Africa, and unrealistically placing
some objects in outdoor settings. Informed by our metric, we use a new
prompting structure that enables a 52% worst-region improvement and a 20%
average improvement in generated background diversity.
