The researchers at Sun Yat-sen University's HCP Lab have introduced a simple yet effective parameter-efficient fine-tuning method called SUR-adapter, which enhances the semantic understanding and reasoning capabilities of text-to-image diffusion models. This method integrates multiple large language models and pre-trained diffusion models, alleviating the common semantic mismatch issues in text-to-image models while maintaining image generation quality. The application of this method will further propel the development of user-friendly text-to-image generation models, enhancing user experience.