  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.
