Abstract
Diffusion models have emerged as the leading approach for style transfer, yet they struggle with photo-realistic transfers, often producing painting-like results or missing detailed stylistic elements. Current methods inadequately address unwanted influence from original content styles and style reference content features. We introduce SCAdapter, a novel technique leveraging CLIP image space to effectively separate and integrate content and style features. Our key innovation systematically extracts pure content from content images and style elements from style references, ensuring authentic transfers. This approach is enhanced through three components: Controllable Style Adaptive Instance Normalization (CSAdaIN) for precise multi-style blending, KVS Injection for targeted style integration, and a style transfer consistency objective maintaining process coherence. Comprehensive experiments demonstrate SCAdapter significantly outperforms state-of-the-art methods in both conventional and diffusion-based baselines. By eliminating DDIM inversion and inference-stage optimization, our method achieves at least $2\times$ faster inference than other diffusion-based approaches, making it both more effective and efficient for practical applications.
Abstract (translated)
扩散模型已经成为风格转换的领先方法，但它们在进行照片级写实的风格转换时存在困难，常常产生类似绘画的结果或遗漏细微的风格元素。目前的方法未能充分解决原始内容风格和风格参考内容特征之间不必要的相互影响问题。我们引入了SCAdapter，这是一种新颖的技术，利用CLIP图像空间有效地分离并整合内容和风格特征。我们的关键技术创新系统地从内容图片中提取纯净的内容，并从风格参考中提取风格元素，确保进行真实的转换。这种技术通过三个组件得到增强：可控风格自适应实例归一化（CSAdaIN）用于精确的多风格融合、KVS注入以实现目标风格集成以及一种保持过程一致性的风格传输一致性目标。全面的实验表明，在传统方法和基于扩散的方法基线上，SCAdapter显著优于现有的最先进的方法。通过消除DDIM反转和推理阶段优化，我们的方法至少比其他基于扩散的方法快两倍的推断速度，使其在实际应用中既更有效又更高效。
URL
https://arxiv.org/abs/2512.12963
