Examining the Text-to-Image Community of Practice: Why and How do People Prompt Generative AIs?

Image generation gained popularity with machine learning (ML) models generating images from text, fuelling new online communities of practices. This work explores the sociology, motivations, and usages of AI art hobbyists. We analyzed an online questionnaire answered by 64 practitioners and a dataset of user prompts sent to the Stable Diffusion generative model. Our findings suggest that TTI generation is a recreational activity mainly conducted by narrow socio-demographic groups who use auxiliary techniques across platforms and beyond request-response interactions. Inherent model limitations and finding suitable prompt formulation are the main obstacles practitioners face. A taxonomy and a corresponding ML model capable of recognizing the semantic content of unseen prompts were created to conduct the user prompt analysis. The prompt analysis revealed that artist names are the main specifier used beside the main subject, often in sequences. We finally discuss the design and socio-technical implications of our work for creativity support.