Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts.

翻译：扩散模型已成为流行的文本到图像生成模型，可在文本提示引导下生成高质量、内容丰富的图像。然而，当输入提示为简洁叙述时，现有模型在语义理解和常识推理方面存在局限性，导致图像生成质量较低。为提升对叙述性提示的处理能力，我们提出了一种简单而有效的参数高效微调方法——语义理解与推理适配器（SUR-adapter），用于预训练扩散模型。为此，我们首先收集并标注了一个新数据集SURD，包含超过57,000个语义校正的多模态样本，每个样本包含一个简单的叙述性提示、一个基于关键词的复杂提示以及一张高质量图像。随后，我们将叙述性提示的语义表示与复杂提示对齐，并通过知识蒸馏将大型语言模型（LLMs）的知识迁移到SUR-adapter中，使其获得强大的语义理解与推理能力，从而构建高质量文本语义表示用于文本到图像生成。我们通过集成多种LLMs与流行预训练扩散模型进行实验，证明了该方法能使扩散模型在理解与推理简洁自然语言时保持图像质量不下降。我们的方法可提升文本到图像扩散模型的易用性，带来更佳用户体验，这表明该方法通过弥合简单叙述性提示与复杂关键词提示之间的语义鸿沟，有望进一步推动用户友好的文本到图像生成模型的发展。