  Generative text-to-image (TTI) models produce high-quality images from short
textual descriptions and are widely used in academic and creative domains. Like
humans, TTI models have a worldview, a conception of the world learned from
their training data and task that influences the images they generate for a
given prompt. However, the worldviews of TTI models are often hidden from
users, making it challenging for users to build intuition about TTI outputs,
and they are often misaligned with users' worldviews, resulting in output
images that do not match user expectations. In response, we introduce
DiffusionWorldViewer, an interactive interface that exposes a TTI model's
worldview across output demographics and provides editing tools for aligning
output images with user perspectives. In a user study with 18 diverse TTI
users, we find that DiffusionWorldViewer helps users represent their varied
viewpoints in generated images and challenge the limited worldview reflected in
current TTI models.
