  Inversion methods, such as Textual Inversion, generate personalized images by
incorporating concepts of interest provided by user images. However, existing
methods often suffer from overfitting issues, where the dominant presence of
inverted concepts leads to the absence of other desired concepts. It stems from
the fact that during inversion, the irrelevant semantics in the user images are
also encoded, forcing the inverted concepts to occupy locations far from the
core distribution in the embedding space. To address this issue, we propose a
method that guides the inversion process towards the core distribution for
compositional embeddings. Additionally, we introduce a spatial regularization
approach to balance the attention on the concepts being composed. Our method is
designed as a post-training approach and can be seamlessly integrated with
other inversion methods. Experimental results demonstrate the effectiveness of
our proposed approach in mitigating the overfitting problem and generating more
diverse and balanced compositions of concepts in the synthesized images. The
source code is available at
https://github.com/zhangxulu1996/Compositional-Inversion.
