  The ability to predict the performance of a query before results are returned
has been a longstanding challenge in Information Retrieval (IR) systems.
Inspired by this task, we introduce, in this paper, a novel task called "Prompt
Performance Prediction" (PPP) that aims to predict the performance of a prompt,
before obtaining the actual generated images. We demonstrate the plausibility
of our task by measuring the correlation coefficient between predicted and
actual performance scores across: three datasets containing pairs of prompts
and generated images AND three art domain datasets of real images and real user
appreciation ratings. Our results show promising performance prediction
capabilities, suggesting potential applications for optimizing user prompts.
