Diffusion models have shown superior performance in image generation and
manipulation, but the inherent stochasticity presents challenges in preserving
and manipulating image content and identity. While previous approaches like
DreamBooth and Textual Inversion have proposed model or latent representation
personalization to maintain the content, their reliance on multiple reference
images and complex training limits their practicality. In this paper, we
present a simple yet highly effective approach to personalization using highly
personalized (HiPer) text embedding by decomposing the CLIP embedding space for
personalization and content manipulation. Our method does not require model
fine-tuning or identifiers, yet still enables manipulation of background,
texture, and motion with just a single image and target text. Through
experiments on diverse target texts, we demonstrate that our approach produces
highly personalized and complex semantic image edits across a wide range of
tasks. We believe that the novel understanding of the text embedding space
presented in this work has the potential to inspire further research across
various tasks.

本文介绍一种基于高度个性化文本嵌入的简单而高效的方法，通过分解 CLIP 嵌入空间来实现个性化和内容操作，并且仅需要一个图像和目标文本即可实现背景、纹理和动态的操作和编辑。