Probing the Generative Worldview: Uncovering Implicit Visual Priors in T2I Models

Text-to-image (T2I) models must resolve a fundamental challenge: natural language is radically underspecified compared to the visual world. A prompt like “a man waiting on the side of the road” only conveys a handful of visual attributes, yet the T2I model must commit to many specific choices across different visual dimensions such as weather, lighting, and clothing to depict the scene. To characterize how models fill in what is left unsaid, we introduce implicit visual priors, the systematic tendencies T2I models exhibit over unspecified visual attributes, and argue that previously studied biases are a subset of this broader concept. Consequently, we propose ProbeGen, a bottom-up framework that discovers these priors directly from generated images without pre-specified classes or hypotheses. Unlike top-down approaches that anticipate potential biases, our observation-driven method surfaces priors across diverse visual dimensions. Through extensive experiments, we find that implicit visual priors are highly pervasive, with models imposing multiple simultaneous defaults and resolving the same underspecification in measurably distinct ways.