  Advances in multimodal AI have presented people with powerful ways to create
images from text. Recent work has shown that text-to-image generations are able
to represent a broad range of subjects and artistic styles. However, finding
the right visual language for text prompts is difficult. In this paper, we
address this challenge with Opal, a system that produces text-to-image
generations for news illustration. Given an article, Opal guides users through
a structured search for visual concepts and provides a pipeline allowing users
to generate illustrations based on an article's tone, keywords, and related
artistic styles. Our evaluation shows that Opal efficiently generates diverse
sets of news illustrations, visual assets, and concept ideas. Users with Opal
generated two times more usable results than users without. We discuss how
structured exploration can help users better understand the capabilities of
human AI co-creative systems.
