A Training-Free Style-Personalization via Scale-wise Autoregressive Model

• URL: http://arxiv.org/abs/2507.04482v1
• Date: Sun, 06 Jul 2025 17:42:11 GMT
• Title: A Training-Free Style-Personalization via Scale-wise Autoregressive Model
• Authors: Kyoungmin Lee, Jihun Park, Jongmin Gim, Wonhyeok Choi, Kyumin Hwang, Jaeyeul Kim, Sunghoon Im,
• Abstract summary: We present a training-free framework for style-personalized image generation that controls content and style information during inference.<n>Our method employs a three-path design--content, style, and generation--each guided by a corresponding text prompt.
• Score: 11.918925320254534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a training-free framework for style-personalized image generation that controls content and style information during inference using a scale-wise autoregressive model. Our method employs a three-path design--content, style, and generation--each guided by a corresponding text prompt, enabling flexible and efficient control over image semantics without any additional training. A central contribution of this work is a step-wise and attention-wise intervention analysis. Through systematic prompt and feature injection, we find that early-to-middle generation steps play a pivotal role in shaping both content and style, and that query features predominantly encode content-specific information. Guided by these insights, we introduce two targeted mechanisms: Key Stage Attention Sharing, which aligns content and style during the semantically critical steps, and Adaptive Query Sharing, which reinforces content semantics in later steps through similarity-aware query blending. Extensive experiments demonstrate that our method achieves competitive style fidelity and prompt fidelity compared to fine-tuned baselines, while offering faster inference and greater deployment flexibility.

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