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. The code is released at https://github.com/Qrange-group/SUR-adapter.

翻译：扩散模型作为当前流行的文本到图像生成模型，能够根据文本提示生成高质量且内容丰富的图像。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，导致生成的图像质量较低。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个经过语义校正的多模态样本，每个样本包括一个简单叙述提示、一个基于关键词的复杂提示以及一张高质量图像。随后，我们将叙述提示的语义表示与复杂提示对齐，并通过知识蒸馏将大语言模型（LLM）的知识迁移至SUR-adapter，使其具备强大的语义理解和推理能力，从而为文本到图像生成构建高质量文本语义表示。通过集成多种LLM和主流预训练扩散模型，我们验证了该方法在不降低图像质量的情况下，使扩散模型能够理解并推理简洁自然语言的有效性。该方法使文本到图像扩散模型更易用且用户体验更佳，表明其通过弥合简单叙述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型文本到图像生成模型的发展。代码已发布至https://github.com/Qrange-group/SUR-adapter。