RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
  via Reinforcement Learning
Despite recent progress in text-to-image (T2I) generation, existing models
often struggle to faithfully capture user intentions from short and
under-specified prompts. While prior work has attempted to enhance prompts
using large language models (LLMs), these methods frequently generate stylistic
or unrealistic content due to insufficient grounding in visual semantics and
real-world composition. Inspired by recent advances in reasoning for language
model, we propose RePrompt, a novel reprompting framework that introduces
explicit reasoning into the prompt enhancement process via reinforcement
learning. Instead of relying on handcrafted rules or stylistic rewrites, our
method trains a language model to generate structured, self-reflective prompts
by optimizing for image-level outcomes. The tailored reward models assesse the
generated images in terms of human preference, semantic alignment, and visual
composition, providing indirect supervision to refine prompt generation. Our
approach enables end-to-end training without human-annotated data. Experiments
on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial
layout fidelity and compositional generalization across diverse T2I backbones,
establishing new state-of-the-art results.
683409de1869c47bd0c423a4
Mingrui Wu
683409de1869c47bd0c423a5
Lu Wang
683409de1869c47bd0c423a6
Pu Zhao
683409de1869c47bd0c423a7
Fangkai Yang
683409de1869c47bd0c423a8
Jianjin Zhang
683409de1869c47bd0c423a9
Jianfeng Liu
683409de1869c47bd0c423aa
Yuefeng Zhan
683409de1869c47bd0c423ab
Weihao Han
683409de1869c47bd0c423ac
Hao Sun
683409de1869c47bd0c423ad
Jiayi Ji
683409de1869c47bd0c423ae
Xiaoshuai Sun
683409de1869c47bd0c423af
Qingwei Lin
683409de1869c47bd0c423b0
Weiwei Deng
683409de1869c47bd0c423b1
Dongmei Zhang
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Feng Sun
683409de1869c47bd0c423b3
Qi Zhang
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Rongrong Ji
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
  via Reinforcement Learning
Despite recent progress in text-to-image (T2I) generation, existing models
often struggle to faithfully capture user intentions from short and
under-specified prompts. While prior work has attempted to enhance prompts
using large language models (LLMs), these methods frequently generate stylistic
or unrealistic content due to insufficient grounding in visual semantics and
real-world composition. Inspired by recent advances in reasoning for language
model, we propose RePrompt, a novel reprompting framework that introduces
explicit reasoning into the prompt enhancement process via reinforcement
learning. Instead of relying on handcrafted rules or stylistic rewrites, our
method trains a language model to generate structured, self-reflective prompts
by optimizing for image-level outcomes. The tailored reward models assesse the
generated images in terms of human preference, semantic alignment, and visual
composition, providing indirect supervision to refine prompt generation. Our
approach enables end-to-end training without human-annotated data. Experiments
on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial
layout fidelity and compositional generalization across diverse T2I backbones,
establishing new state-of-the-art results.
RePrompt, a reprompting framework using reinforcement learning, enhances text-to-image generation by optimizing for image-level outcomes, significantly improving spatial layout and compositional generalization.
text-to-image T2I large language models LLMs reinforcement learning structured prompts self-reflective prompts reward models human preference semantic alignment visual composition GenEval T2I-Compbench spatial layout fidelity compositional generalization