Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion

Linhao Huang

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arXiv · 2511.01355

Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion

Linhao Huang

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Abstract

Recent advancements in text-to-image diffusion models have significantly improved the personalization and stylization of generated images. However, previous studies have only assessed content similarity under a single style intensity. In our experiments, we observe that increasing style intensity leads to a significant loss of content features, resulting in a suboptimal content-style frontier. To address this, we propose a novel approach to expand the content-style frontier by leveraging Content-Style Subspace Blending and a Content-Style Balance loss. Our method improves content similarity across varying style intensities, significantly broadening the content-style frontier. Extensive experiments demonstrate that our approach outperforms existing techniques in both qualitative and quantitative evaluations, achieving superior content-style trade-off with significantly lower Inverted Generational Distance (IGD) and Generational Distance (GD) scores compared to current methods.

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Linhao Huang. 2025-11-03. Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion. https://arxiv.org/abs/2511.01355

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