TIPO: Text to Image with Text Presampling for Prompt Optimization

TL;DR

TIPO presents an efficient, scalable method for automatic prompt refinement in text-to-image generation, significantly improving visual quality and coherence without relying on large language models or reinforcement learning.

Contribution

Introduces a lightweight prompt expansion technique that enhances prompt detail and quality, outperforming resource-intensive methods in T2I tasks.

Findings

Achieves higher human preference rates

Reduces visual artifacts

Maintains competitive aesthetic quality

Abstract

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer and more detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO offers strong computational efficiency and scalability, opening new possibilities for effective automated prompt engineering in T2I tasks. Extensive experiments across multiple domains demonstrate that TIPO achieves stronger text alignment, reduced visual artifacts, and consistently higher human preference rates, while maintaining competitive aesthetic quality. These results highlight the effectiveness of distribution-aligned prompt engineering and point toward broader opportunities for scalable, automated refinement in text-to-image generation.