From Text to Pixels: Enhancing User Understanding through Text-to-Image Model Explanations

Recent progress in Text-to-Image (T2I) models promises transformative applications in art, design, education, medicine, and entertainment. These models, exemplified by Dall-e, Imagen, and Stable Diffusion, have the potential to revolutionize various industries. However, a primary concern is their operation as a 'black-box' for many users. Without understanding the underlying mechanics, users are unable to harness the full potential of these models. This study focuses on bridging this gap by developing and evaluating explanation techniques for T2I models, targeting inexperienced end users. While prior works have delved into Explainable AI (XAI) methods for classification or regression tasks, T2I generation poses distinct challenges. Through formative studies with experts, we identified unique explanation goals and subsequently designed tailored explanation strategies. We then empirically evaluated these methods with a cohort of 473 participants from Amazon Mechanical Turk (AMT) across three tasks. Our results highlight users' ability to learn new keywords through explanations, a preference for example-based explanations, and challenges in comprehending explanations that significantly shift the image's theme. Moreover, findings suggest users benefit from a limited set of concurrent explanations. Our main contributions include a curated dataset for evaluating T2I explainability techniques, insights from a comprehensive AMT user study, and observations critical for future T2I model explainability research.