StyleStudio：具有风格元素选择性控制的文本驱动风格迁移
StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
AI总结：
本文提出StyleStudio，通过跨模态AdaIN、基于风格的分类器自由引导和教师模型三种策略，解决文本驱动风格迁移中的过拟合、控制受限和文本不对齐问题，显著提升迁移质量与对齐度，且无需微调即可集成。
摘要：
文本驱动的风格迁移旨在将参考图像的风格与文本提示所描述的内容相融合。文本到图像模型的最新进展提升了风格转换的细腻度，但仍存在显著挑战，尤其是对参考风格的过拟合、风格控制受限以及与文本内容不对齐等问题。在本文中，我们提出了三种互补策略来解决这些问题。首先，我们引入了一种跨模态自适应实例归一化（AdaIN）机制，以更好地整合风格和文本特征，增强对齐效果。其次，我们开发了一种基于风格的分类器自由引导（SCFG）方法，能够对风格元素进行选择性控制，减少无关影响。最后，我们在早期生成阶段引入教师模型以稳定空间布局并减轻伪影。我们的广泛评估表明，该方法在风格迁移质量和与文本提示的对齐方面均有显著提升。此外，我们的方法无需微调即可集成到现有的风格迁移框架中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
