Text-to-image generative models are a new and powerful way to generate visual artwork. The free-form nature of text as interaction is double-edged; while users have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt components and model parameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style and investigate success and failure modes within these dimensions. Our evaluation of 5493 generations over the course of five experiments spans 49 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people find better outcomes from text-to-image generative models.
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