PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914053422579712 |
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| author | Xiang, Dawei Xu, Wenyan Chu, Kexin Ding, Tianqi Shen, Zixu Zeng, Yiming Su, Jianchang Zhang, Wei |
| author_facet | Xiang, Dawei Xu, Wenyan Chu, Kexin Ding, Tianqi Shen, Zixu Zeng, Yiming Su, Jianchang Zhang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization Xiang, Dawei Xu, Wenyan Chu, Kexin Ding, Tianqi Shen, Zixu Zeng, Yiming Su, Jianchang Zhang, Wei Multiagent Systems Artificial Intelligence 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. |
| title | PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12446 |