CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
Authors: name: Quang-Binh Nguyen, name: Minh Luu, name: Quang Nguyen, name: Anh Tran, name: Khoi Nguyen

Disentangling content and style from a single image, known as content-style
decomposition (CSD), enables recontextualization of extracted content and
stylization of extracted styles, offering greater creative flexibility in
visual synthesis. While recent personalization methods have explored the
decomposition of explicit content style, they remain tailored for diffusion
models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a
promising alternative with a next-scale prediction paradigm, achieving
performance comparable to that of diffusion models. In this paper, we explore
VAR as a generative framework for CSD, leveraging its scale-wise generation
process for improved disentanglement. To this end, we propose CSD-VAR, a novel
method that introduces three key innovations: (1) a scale-aware alternating
optimization strategy that aligns content and style representation with their
respective scales to enhance separation, (2) an SVD-based rectification method
to mitigate content leakage into style representations, and (3) an Augmented
Key-Value (K-V) memory enhancing content identity preservation. To benchmark
this task, we introduce CSD-100, a dataset specifically designed for
content-style decomposition, featuring diverse subjects rendered in various
artistic styles. Experiments demonstrate that CSD-VAR outperforms prior
approaches, achieving superior content preservation and stylization fidelity.