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 ArtAdapter 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——一种变革性的文本到图像风格迁移框架，突破了传统方法在色彩、笔触和物体形状上的局限，能够捕捉构图及独特艺术表达等高层风格元素。通过将多层风格编码器与我们提出的显式适配机制相结合，ArtAdapter在风格迁移中实现了前所未有的保真度，确保与文本描述高度吻合。此外，辅助内容适配器（ACA）的引入有效分离了内容与风格，减轻了从风格参考中借用内容的倾向。同时，我们新颖的快速微调方法能在降低过拟合风险的前提下进一步增强零样本风格表征能力。全面评估证实，ArtAdapter超越了当前最先进方法。