CDST：用于通用风格参考定制的颜色解缠风格迁移
CDST: Color Disentangled Style Transfer for Universal Style Reference Customization
AI总结：
介绍CDST这种新颖高效的双流风格迁移训练范式，能分离颜色与风格，推理时免调参实现通用风格迁移，首次免调参解决带风格和内容参考的特征保留风格迁移问题，实验证明效果达最优。
摘要：
我们引入了颜色解缠风格迁移（CDST），这是一种新颖且高效的双流风格迁移训练范式，它将颜色与风格完全分离，并迫使风格流无视颜色。在推理过程中，同一模型的CDST无需调整即可实现通用风格迁移能力。特别是，首次以无需调整的方式解决了具有风格和内容参考的特征保留风格迁移问题。CDST通过多特征图像嵌入压缩显著提高了风格相似度，并通过受扩散UNet解缠定律启发的新CDST风格定义保留了强大的编辑能力。通过进行全面的定性和定量实验以及人工评估，我们证明CDST在各种风格迁移任务上取得了领先成果。
英文摘要：
We introduce Color Disentangled Style Transfer (CDST), a novel and efficient two-stream style transfer training paradigm which completely isolates color from style and forces the style stream to be color-blinded. With one same model, CDST unlocks universal style transfer capabilities in a tuning-free manner during inference. Especially, the characteristics-preserved style transfer with style and content references is solved in the tuning-free way for the first time. CDST significantly improves the style similarity by multi-feature image embeddings compression and preserves strong editing capability via our new CDST style definition inspired by Diffusion UNet disentanglement law. By conducting thorough qualitative and quantitative experiments and human evaluations, we demonstrate that CDST achieves state-of-the-art results on various style transfer tasks.
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。
