Although recent years have witnessed significant advancements in image
editing thanks to the remarkable progress of text-to-image diffusion models,
the problem of non-rigid image editing still presents its complexities and
challenges. Existing methods often fail to achieve consistent results due to
the absence of unique identity characteristics. Thus, learning a personalized
identity prior might help with consistency in the edited results. In this
paper, we explore a novel task: learning the personalized identity prior for
text-based non-rigid image editing. To address the problems in jointly learning
prior and editing the image, we present LIPE, a two-stage framework designed to
customize the generative model utilizing a limited set of images of the same
subject, and subsequently employ the model with learned prior for non-rigid
image editing. Experimental results demonstrate the advantages of our approach
in various editing scenarios over past related leading methods in qualitative
and quantitative ways.

该研究探讨了学习用于非刚性图像编辑的个性化身份先验的新任务，并提出了一个两阶段框架 LIPE，通过利用同一主题的有限图像集来定制生成模型，并利用学习到的先验进行非刚性图像编辑。实验结果在定性和定量方面证明了我们的方法在各种编辑场景中相对过去相关领先方法的优势。