RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models

Recent advances in text-to-image (T2I) models have revolu-tionized the field of image generation and editing. However, identifyingsemantics that a T2I model can successfully edit in an image contin-ues to be a challenging task. Most existing approaches require usersto manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In thispaper, we present RankT2I, a novel, training-free, and model-agnosticframework that automates the discovery of editable semantics in diffu-sion and FLUX-based models. Given a visual domain, we first utilizea multimodal vision-language model to gather a broad set of candidatesemantics. We then frame semantic discovery as a set selection problemand use a submodular objective to identify semantics that are relevant,editable, and diverse. Our method helps users efficiently identify a widerange of semantics for text-to-image editing models across several do-mains while outperforming existing methods.