TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization
This addresses the challenge of ensuring alignment between generated images and user prompts for users of text-to-image models, representing an incremental improvement through novel optimization techniques.
The paper tackles the problem of image-text inconsistency in text-to-image generation by introducing TextMatch, a framework that uses multimodal optimization to refine prompts, resulting in significantly improved consistency across benchmarks.
Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch, a novel framework that leverages multimodal optimization to address image-text discrepancies in text-to-image (T2I) generation and editing. TextMatch employs a scoring strategy powered by large language models (LLMs) and visual question-answering (VQA) models to evaluate semantic consistency between prompts and generated images. By integrating multimodal in-context learning and chain of thought reasoning, our method dynamically refines prompts through iterative optimization. This process ensures that the generated images better capture user intent of, resulting in higher fidelity and relevance. Extensive experiments demonstrate that TextMatch significantly improves text-image consistency across multiple benchmarks, establishing a reliable framework for advancing the capabilities of text-to-image generative models. Our code is available at https://anonymous.4open.science/r/TextMatch-F55C/.