In the evolving domain of text-to-image generation, diffusion models have emerged as powerful tools in content creation. Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leakage. To address these challenges, we propose a novel approach, \ours, to produce a diverse range of images while maintaining specific style elements and nuances. During the denoising process, we keep the query from original features while swapping the key and value with those from reference features in the late self-attention layers. This approach allows for the visual style prompting without any fine-tuning, ensuring that generated images maintain a faithful style. Through extensive evaluation across various styles and text prompts, our method demonstrates superiority over existing approaches, best reflecting the style of the references and ensuring that resulting images match the text prompts most accurately. Our project page is available \href{https://curryjung.github.io/VisualStylePrompt/}{here}.

我们提出了一种新颖的方法来产生各种不同风格的图像，并保持特定的风格元素和细微差别，这种方法通过在去噪过程中，在最后的自注意力层中将查询从原始特征保留，而将关键字和值与参考特征交换，实现了视觉风格提示而无需任何微调，确保生成的图像保持忠实的风格。通过对各种风格和文本提示进行广泛评估，我们的方法表现出超越现有方法的优势，在最准确地匹配文本提示的情况下最好地反映出参考的风格，我们的项目页面在这里提供。

利用交换自注意力进行视觉风格激活