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arXiv · 2509.12446

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization

Dawei Xiang·Wenyan Xu·Kexin Chu·Tianqi Ding·Zixu Shen·Yiming Zeng·Jianchang Su·Wei Zhang
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Abstract

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.

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Dawei Xiang, Wenyan Xu, Kexin Chu, Tianqi Ding, Zixu Shen, Yiming Zeng, Jianchang Su, Wei Zhang. 2025-09-15. PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization. https://arxiv.org/abs/2509.12446

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