  Personalized generation paradigms empower designers to customize visual
intellectual properties with the help of textual descriptions by tuning or
adapting pre-trained text-to-image models on a few images. Recent works explore
approaches for concurrently customizing both content and detailed visual style
appearance. However, these existing approaches often generate images where the
content and style are entangled. In this study, we reconsider the customization
of content and style concepts from the perspective of parameter space
construction. Unlike existing methods that utilize a shared parameter space for
content and style, we propose a learning framework that separates the parameter
space to facilitate individual learning of content and style, thereby enabling
disentangled content and style. To achieve this goal, we introduce "partly
learnable projection" (PLP) matrices to separate the original adapters into
divided sub-parameter spaces. We propose "break-for-make" customization
learning pipeline based on PLP, which is simple yet effective. We break the
original adapters into "up projection" and "down projection", train content and
style PLPs individually with the guidance of corresponding textual prompts in
the separate adapters, and maintain generalization by employing a
multi-correspondence projection learning strategy. Based on the adapters broken
apart for separate training content and style, we then make the entity
parameter space by reconstructing the content and style PLPs matrices, followed
by fine-tuning the combined adapter to generate the target object with the
desired appearance. Experiments on various styles, including textures,
materials, and artistic style, show that our method outperforms
state-of-the-art single/multiple concept learning pipelines in terms of
content-style-prompt alignment.
