StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements

• URL: http://arxiv.org/abs/2412.08503v1
• Date: Wed, 11 Dec 2024 16:13:23 GMT
• Title: StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
• Authors: Mingkun Lei, Xue Song, Beier Zhu, Hao Wang, Chi Zhang,
• Abstract summary: Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the transformations of nuance style, yet significant challenges remain. We propose three complementary strategies to address these issues.
• Score: 10.752464085587267
• License:
• Abstract: Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain, particularly with overfitting to reference styles, limiting stylistic control, and misaligning with textual content. In this paper, we propose three complementary strategies to address these issues. First, we introduce a cross-modal Adaptive Instance Normalization (AdaIN) mechanism for better integration of style and text features, enhancing alignment. Second, we develop a Style-based Classifier-Free Guidance (SCFG) approach that enables selective control over stylistic elements, reducing irrelevant influences. Finally, we incorporate a teacher model during early generation stages to stabilize spatial layouts and mitigate artifacts. Our extensive evaluations demonstrate significant improvements in style transfer quality and alignment with textual prompts. Furthermore, our approach can be integrated into existing style transfer frameworks without fine-tuning.

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