LumiGen: An LVLM-Enhanced Iterative Framework for Fine-Grained Text-to-Image Generation

• URL: http://arxiv.org/abs/2508.04732v1
• Date: Tue, 05 Aug 2025 20:53:43 GMT
• Title: LumiGen: An LVLM-Enhanced Iterative Framework for Fine-Grained Text-to-Image Generation
• Authors: Xiaoqi Dong, Xiangyu Zhou, Nicholas Evans, Yujia Lin,
• Abstract summary: Vision-Language Models (LVLMs) have demonstrated powerful capabilities in cross-modal understanding and instruction following.<n>LumiGen is a novel LVLM-enhanced iterative framework designed to elevate T2I model performance.<n>LumiGen achieves a superior average score of 3.08, outperforming state-of-the-art baselines.
• Score: 1.124958340749622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-Image (T2I) generation has made significant advancements with diffusion models, yet challenges persist in handling complex instructions, ensuring fine-grained content control, and maintaining deep semantic consistency. Existing T2I models often struggle with tasks like accurate text rendering, precise pose generation, or intricate compositional coherence. Concurrently, Vision-Language Models (LVLMs) have demonstrated powerful capabilities in cross-modal understanding and instruction following. We propose LumiGen, a novel LVLM-enhanced iterative framework designed to elevate T2I model performance, particularly in areas requiring fine-grained control, through a closed-loop, LVLM-driven feedback mechanism. LumiGen comprises an Intelligent Prompt Parsing & Augmentation (IPPA) module for proactive prompt enhancement and an Iterative Visual Feedback & Refinement (IVFR) module, which acts as a "visual critic" to iteratively correct and optimize generated images. Evaluated on the challenging LongBench-T2I Benchmark, LumiGen achieves a superior average score of 3.08, outperforming state-of-the-art baselines. Notably, our framework demonstrates significant improvements in critical dimensions such as text rendering and pose expression, validating the effectiveness of LVLM integration for more controllable and higher-quality image generation.

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