LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation

• URL: http://arxiv.org/abs/2407.00737v1
• Date: Sun, 30 Jun 2024 15:50:32 GMT
• Title: LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
• Authors: Mushui Liu, Yuhang Ma, Xinfeng Zhang, Yang Zhen, Zeng Zhao, Zhipeng Hu, Bai Liu, Changjie Fan,
• Abstract summary: This paper proposes a framework called bfLLM4GEN, which enhances the semantic understanding ability of text-to-image diffusion models. LLM4GEN can be easily incorporated into various diffusion models as a plug-and-play component and enhances text-to-image generation.
• Score: 31.560663550775235
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Diffusion Models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts that involve multiple objects, attribute binding, and long descriptions. This paper proposes a framework called \textbf{LLM4GEN}, which enhances the semantic understanding ability of text-to-image diffusion models by leveraging the semantic representation of Large Language Models (LLMs). Through a specially designed Cross-Adapter Module (CAM) that combines the original text features of text-to-image models with LLM features, LLM4GEN can be easily incorporated into various diffusion models as a plug-and-play component and enhances text-to-image generation. Additionally, to facilitate the complex and dense prompts semantic understanding, we develop a LAION-refined dataset, consisting of 1 million (M) text-image pairs with improved image descriptions. We also introduce DensePrompts which contains 7,000 dense prompts to provide a comprehensive evaluation for the text-to-image generation task. With just 10\% of the training data required by recent ELLA, LLM4GEN significantly improves the semantic alignment of SD1.5 and SDXL, demonstrating increases of 7.69\% and 9.60\% in color on T2I-CompBench, respectively. The extensive experiments on DensePrompts also demonstrate that LLM4GEN surpasses existing state-of-the-art models in terms of sample quality, image-text alignment, and human evaluation. The project website is at: \textcolor{magenta}{\url{https://xiaobul.github.io/LLM4GEN/}}

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