Enhanced Text-to-Image Generation by Fine-grained Multimodal Reasoning
For researchers in text-to-image generation, FiMR addresses the bottleneck of fine-grained control by enabling MLLMs to self-refine at the attribute level, offering a novel method for improving compositional fidelity.
FiMR improves text-to-image generation by decomposing prompts into minimal semantic units and using VQA-based verification for fine-grained feedback, achieving better image-prompt alignment and outperforming baselines on compositional benchmarks.
With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced significantly. However, despite the inherent reasoning capabilities of unified MLLMs for self-reflection and self-refinement, their use in text-to-image generation remains largely underexplored. Meanwhile, existing multimodal reasoning-based image generation methods mostly rely on holistic image-text alignment judgments, without fine-grained reflection and refinement of detailed prompt attributes, leading to limited fine-grained control. Therefore, we propose Fine-grained Multimodal Reasoning (FiMR), a framework that leverages decomposed visual question answering (VQA) to break down an input prompt into minimal semantic units-such as entities and attributes-and verify each unit via VQA to generate explicit, fine-grained feedback. Based on this feedback, FiMR then applies targeted, localized refinements. This fine-grained self-reasoning and self-refinement enable MLLMs to achieve more precise improvements in image-prompt alignment and overall generation quality at test time. Extensive experiments demonstrate that FiMR consistently outperforms image generation baselines, including reasoning-based methods, particularly on compositional text-to-image benchmarks. The code and models are available at https://github.com/KU-AGI/FiMR