CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
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.
687d9e422e8db0930be6f0a3
Quang-Binh Nguyen
claimed_verified
2025-07-21T08:29:38.282Z
637ce55e43fec4c21633f9ad
https://cdn-avatars.huggingface.co/v1/production/uploads/637ce55e43fec4c21633f9ad/yZTBQbvTatUw2zuxpp0nq.jpeg
Quang-Binh Nguyen
user
nqbinh
687d9e422e8db0930be6f0a4
Minh Luu
687d9e422e8db0930be6f0a5
Quang Nguyen
687d9e422e8db0930be6f0a6
Anh Tran
687d9e422e8db0930be6f0a7
Khoi Nguyen
CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
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.
CSD-VAR, a Visual Autoregressive Modeling approach, enhances content-style decomposition by introducing scale-aware optimization, SVD-based rectification, and augmented K-V memory, outperforming diffusion models in content preservation and stylization.
content-style decomposition Visual Autoregressive Modeling scale-aware alternating optimization SVD-based rectification Augmented Key-Value memory CSD-100 dataset