Qwen-Image-PE: Learning Visual Design for Text-to-Image Prompt Enhancement

Zekai Zhang; Yanran Zhang; Shengming Yin; Yan Shu; Zhening Liu; Bingxuan Zhao; Chenyang Zhu; Donghao Zhou; Jiahao Li; Jiahao Wang; Jie Zhang; Kaiyuan Gao; Kun Yan; Lihan Jiang; Ningyuan Tang; Qihang Ai; Rui Song; Shuo Cao; Xiao Xu; Xiaoyue Chen; Yuxiang Chen; Zhendong Wang; Zhouyang Li; Zihao Liu; Zikai Zhou; Bo Zheng; Huishuai Zhang; Dongyan Zhao; Chenfei Wu

arXiv · 2610.33472

Qwen-Image-PE: Learning Visual Design for Text-to-Image Prompt Enhancement

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Abstract

Learning visual design is essential for prompt enhancement (PE) to turn underspecified user requests into coherent visual specifications for text-to-image (T2I) generation. Existing methods preserve user intent and improve overall visual appeal, with less explicit emphasis on fine-grained design dimensions. To address this gap, we introduce Qwen-Image-PE, which learns visual design through two-stage training. Supervised fine-tuning with Caption-Based Reverse Distillation (CRD) uses dense captions of well-designed images as design supervision. Reinforcement learning with Groupwise Design Reward (GDR) jointly compares all candidate images for the same request to refine lighting, composition, layout, and detail. On Qwen-Image-Bench with Qwen-Image-2.1, Qwen-Image-PE-397B improves over original requests by 7.81 points, while Qwen-Image-PE-9B outperforms all evaluated PE baselines. Further evaluations show gains in visual design, aesthetics, image quality, and human preference. We find that PE scales along three axes: training data, model size, and information density, with sufficient information density as a prerequisite for effective enhancement. Moreover, we demonstrate that Qwen-Image-PE generalizes to four additional T2I backbones, even surpassing native prompt enhancers on HunyuanImage-2.1 and FLUX 3 Image Klein. We release Qwen-Image-PE-9B at https://huggingface.co/Qwen/Qwen-Image-2.1-PE-T2I.

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Zekai Zhang, Yanran Zhang, Shengming Yin, Yan Shu, Zhening Liu, Bingxuan Zhao, Chenyang Zhu, Donghao Zhou, Jiahao Li, Jiahao Wang, Jie Zhang, Kaiyuan Gao, Kun Yan, Lihan Jiang, Ningyuan Tang, Qihang Ai, Rui Song, Shuo Cao, Xiao Xu, Xiaoyue Chen, Yuxiang Chen, Zhendong Wang, Zhouyang Li, Zihao Liu, Zikai Zhou, Bo Zheng, Huishuai Zhang, Dongyan Zhao, Chenfei Wu. 2026-10-24. Qwen-Image-PE: Learning Visual Design for Text-to-Image Prompt Enhancement. https://arxiv.org/abs/2610.33472

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