Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste. These personalization techniques can grasp the essence of a concept through a collection of images, or adjust a pre-trained text-to-image model with a specific image input for subject-driven or attribute-aware guidance. Yet, accurately capturing the distinct visual attributes of an individual image poses a challenge for these methods. To address this issue, we introduce OSTAF, a novel parameter-efficient one-shot fine-tuning method which only utilizes one reference image for T2I personalization. A novel hypernetwork-powered attribute-focused fine-tuning mechanism is employed to achieve the precise learning of various attribute features (e.g., appearance, shape or drawing style) from the reference image. Comparing to existing image customization methods, our method shows significant superiority in attribute identification and application, as well as achieves a good balance between efficiency and output quality.

个性化文本到图像模型不仅能够产生逼真且多样化的视觉效果，而且还允许用户根据个人口味定制图像。该研究引入了一种参数高效的一次性微调方法OSTAF，该方法仅利用一个参考图像进行T2I个性化。通过一种新颖的超网络驱动的属性聚焦微调机制，实现对参考图像中各种属性特征（如外观、形状或绘画风格）的精确学习。与现有的图像定制方法相比，我们的方法在属性识别和应用方面具有显著优势，同时在效率和输出质量之间实现了良好的平衡。

OSTAF：一种用于改进基于属性的T2I个性化的一次调整方法