# Prompt Performance Prediction for Image Generation

arXiv: https://arxiv.org/abs/2306.08915 · HTML: https://mlsanity.com/paper/2306.08915/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction](https://mlsanity.com/paper/2406.04746/) (2024)
- [VQPP: Video Query Performance Prediction Benchmark](https://mlsanity.com/paper/2602.17814/) (2026)

## Abstract (arXiv)

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.