This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapte to achieve unprecedented fidelity in style transfer, ensuring close alignment with textual descriptions. Additionally, the incorporation of an Auxiliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.

翻译：本文提出ArtAdapter，一种突破传统颜色、笔触及物体形态局限的变革性文本到图像风格迁移框架，能够捕捉构图与独特艺术表现等高阶风格元素。通过将多层级风格编码器与我们提出的显式适配机制相融合，ArtAdapter在风格迁移中实现了前所未有的保真度，确保与文本描述的高度一致性。此外，辅助内容适配器（ACA）的引入有效分离内容与风格，缓解了从风格参考中借用内容的问题。进一步地，我们创新的快速微调方法能在抑制过拟合风险的同时增强零样本风格表征能力。综合评估表明，ArtAdapter超越了当前最先进方法。