A Training-Free Style-Personalization via SVD-Based Feature Decomposition
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915630443134976 |
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| author | Lee, Kyoungmin Park, Jihun Gim, Jongmin Choi, Wonhyeok Hwang, Kyumin Kim, Jaeyeul Im, Sunghoon |
| author_facet | Lee, Kyoungmin Park, Jihun Gim, Jongmin Choi, Wonhyeok Hwang, Kyumin Kim, Jaeyeul Im, Sunghoon |
| contents | We present a training-free framework for style-personalized image generation that operates during inference using a scale-wise autoregressive model. Our method generates a stylized image guided by a single reference style while preserving semantic consistency and mitigating content leakage. Through a detailed step-wise analysis of the generation process, we identify a pivotal step where the dominant singular values of the internal feature encode style-related components. Building upon this insight, we introduce two lightweight control modules: Principal Feature Blending, which enables precise modulation of style through SVD-based feature reconstruction, and Structural Attention Correction, which stabilizes structural consistency by leveraging content-guided attention correction across fine stages. Without any additional training, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Training-Free Style-Personalization via SVD-Based Feature Decomposition Lee, Kyoungmin Park, Jihun Gim, Jongmin Choi, Wonhyeok Hwang, Kyumin Kim, Jaeyeul Im, Sunghoon Computer Vision and Pattern Recognition We present a training-free framework for style-personalized image generation that operates during inference using a scale-wise autoregressive model. Our method generates a stylized image guided by a single reference style while preserving semantic consistency and mitigating content leakage. Through a detailed step-wise analysis of the generation process, we identify a pivotal step where the dominant singular values of the internal feature encode style-related components. Building upon this insight, we introduce two lightweight control modules: Principal Feature Blending, which enables precise modulation of style through SVD-based feature reconstruction, and Structural Attention Correction, which stabilizes structural consistency by leveraging content-guided attention correction across fine stages. Without any additional training, 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. |
| title | A Training-Free Style-Personalization via SVD-Based Feature Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.04482 |