  We present CUPID: a visualization method for the contextual understanding of
prompt-conditioned image distributions. CUPID targets the visual analysis of
distributions produced by modern text-to-image generative models, wherein a
user can specify a scene via natural language, and the model generates a set of
images, each intended to satisfy the user's description. CUPID is designed to
help understand the resulting distribution, using contextual cues to facilitate
analysis: objects mentioned in the prompt, novel, synthesized objects not
explicitly mentioned, and their potential relationships. Central to CUPID is a
novel method for visualizing high-dimensional distributions, wherein
contextualized embeddings of objects, those found within images, are mapped to
a low-dimensional space via density-based embeddings. We show how such
embeddings allows one to discover salient styles of objects within a
distribution, as well as identify anomalous, or rare, object styles. Moreover,
we introduce conditional density embeddings, whereby conditioning on a given
object allows one to compare object dependencies within the distribution. We
employ CUPID for analyzing image distributions produced by large-scale
diffusion models, where our experimental results offer insights on language
misunderstanding from such models and biases in object composition, while also
providing an interface for discovery of typical, or rare, synthesized scenes.
