  Generative text-to-image models have gained great popularity among the public
for their powerful capability to generate high-quality images based on natural
language prompts. However, developing effective prompts for desired images can
be challenging due to the complexity and ambiguity of natural language. This
research proposes PromptMagician, a visual analysis system that helps users
explore the image results and refine the input prompts. The backbone of our
system is a prompt recommendation model that takes user prompts as input,
retrieves similar prompt-image pairs from DiffusionDB, and identifies special
(important and relevant) prompt keywords. To facilitate interactive prompt
refinement, PromptMagician introduces a multi-level visualization for the
cross-modal embedding of the retrieved images and recommended keywords, and
supports users in specifying multiple criteria for personalized exploration.
Two usage scenarios, a user study, and expert interviews demonstrate the
effectiveness and usability of our system, suggesting it facilitates prompt
engineering and improves the creativity support of the generative text-to-image
model.
