The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context-often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.
@article{arxiv.2509.12446,
title = {PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization},
author = {Dawei Xiang and Wenyan Xu and Kexin Chu and Tianqi Ding and Zixu Shen and Yiming Zeng and Jianchang Su and Wei Zhang},
journal= {arXiv preprint arXiv:2509.12446},
year = {2025}
}