@inproceedings{xiang-etal-2025-promptsculptor,
title = "{P}rompt{S}culptor: Multi-Agent Based Text-to-Image Prompt Optimization",
author = "Xiang, Dawei and
Xu, Wenyan and
Chu, Kexin and
Ding, Tianqi and
Shen, Zixu and
Zeng, Yiming and
Su, Jianchang and
Zhang, Wei",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.59/",
pages = "774--786",
ISBN = "979-8-89176-334-0",
abstract = "The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) 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 (CoT) 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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<abstract>The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) 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 (CoT) 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.</abstract>
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%0 Conference Proceedings
%T PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
%A Xiang, Dawei
%A Xu, Wenyan
%A Chu, Kexin
%A Ding, Tianqi
%A Shen, Zixu
%A Zeng, Yiming
%A Su, Jianchang
%A Zhang, Wei
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F xiang-etal-2025-promptsculptor
%X The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) 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 (CoT) 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.
%U https://aclanthology.org/2025.emnlp-demos.59/
%P 774-786
Markdown (Informal)
[PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization](https://aclanthology.org/2025.emnlp-demos.59/) (Xiang et al., EMNLP 2025)
ACL
- Dawei Xiang, Wenyan Xu, Kexin Chu, Tianqi Ding, Zixu Shen, Yiming Zeng, Jianchang Su, and Wei Zhang. 2025. PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 774–786, Suzhou, China. Association for Computational Linguistics.