CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models

TL;DR

This paper introduces CSD-VAR, a novel content-style decomposition method for visual autoregressive models that improves disentanglement and stylization quality through innovative optimization and memory techniques.

Contribution

CSD-VAR is the first to adapt VAR models for content-style decomposition, introducing scale-aware optimization, SVD rectification, and augmented memory for better disentanglement.

Findings

Outperforms prior methods in content preservation.

Achieves higher stylization fidelity.

Demonstrates effectiveness on CSD-100 dataset.

Abstract

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.