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

• URL: http://arxiv.org/abs/2509.12446v2
• Date: Wed, 24 Sep 2025 03:51:42 GMT
• Title: PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
• Authors: Dawei Xiang, Wenyan Xu, Kexin Chu, Tianqi Ding, Zixu Shen, Yiming Zeng, Jianchang Su, Wei Zhang,
• Abstract summary: To generate high-quality images, users must craft detailed prompts specifying scene, style, and context.<n>We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process.<n>Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt.
• Score: 4.133498001057646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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