LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
<|reference_start|>LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation: Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving multiple objects, attribute binding, and long descriptions. In this paper, we propose a novel framework called \textbf{LLM4GEN}, which enhances the semantic understanding of text-to-image diffusion models by leveraging the representation of Large Language Models (LLMs). It can be seamlessly incorporated into various diffusion models as a plug-and-play component. A specially designed Cross-Adapter Module (CAM) integrates the original text features of text-to-image models with LLM features, thereby enhancing text-to-image generation. Additionally, to facilitate and correct entity-attribute relationships in text prompts, we develop an entity-guided regularization loss to further improve generation performance. We also introduce DensePrompts, which contains $7,000$ dense prompts to provide a comprehensive evaluation for the text-to-image generation task. Experiments indicate that LLM4GEN significantly improves the semantic alignment of SD1.5 and SDXL, demonstrating increases of 9.69\% and 12.90\% in color on T2I-CompBench, respectively. Moreover, it surpasses existing models in terms of sample quality, image-text alignment, and human evaluation.<|reference_end|>
@article{liu2024llm4gen:,
    title={LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image
  Generation},
    author={Mushui Liu, Yuhang Ma, Yang Zhen, Jun Dan, Yunlong Yu, Zeng Zhao,
  Zhipeng Hu, Bai Liu, Changjie Fan},
    journal={arXiv preprint arXiv:2407.00737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00737},
    primaryClass={cs.CV}
}