  The emergence of various adapters, including Low-Rank Adaptation (LoRA)
applied from the field of natural language processing, has allowed diffusion
models to personalize image generation at a low cost. However, due to the
various challenges including limited datasets and shortage of regularization
and computation resources, adapter training often results in unsatisfactory
outcomes, leading to the corruption of the backbone model's prior knowledge.
One of the well known phenomena is the loss of diversity in object generation,
especially within the same class which leads to generating almost identical
objects with minor variations. This poses challenges in generation
capabilities. To solve this issue, we present Contrastive Adapter Training
(CAT), a simple yet effective strategy to enhance adapter training through the
application of CAT loss. Our approach facilitates the preservation of the base
model's original knowledge when the model initiates adapters. Furthermore, we
introduce the Knowledge Preservation Score (KPS) to evaluate CAT's ability to
keep the former information. We qualitatively and quantitatively compare CAT's
improvement. Finally, we mention the possibility of CAT in the aspects of
multi-concept adapter and optimization.
