Ada-Adapter：利用预训练图像编码器实现扩散模型的快速少样本风格个性化
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder
AI总结：
本文提出 Ada-Adapter 框架，结合现成扩散模型与预训练图像编码器，从少量参考图像学习紧凑风格表示，实现高效零样本/少样本风格化。
摘要：
为高质量图像风格化而微调先进扩散模型通常需要大型训练数据集和大量计算资源，这阻碍了它们的实际应用。我们提出 Ada-Adapter，这是一种用于扩散模型少样本风格个性化的新颖框架。Ada-Adapter 利用现成的扩散模型和预训练图像特征编码器，从有限的源图像集合中学习紧凑的风格表示。我们的方法能够利用单张参考图像实现高效的零样本风格迁移。此外，借助少量源图像（三到五张即已足够）和数分钟的微调，我们的方法可以捕捉复杂的风格细节和概念特征，生成与所提供文本提示高度一致的高保真风格化图像。我们在多种艺术风格上展示了该方法的有效性，包括扁平插画、3D 渲染和标志设计。实验结果表明，Ada-Adapter 在输出质量、多样性和训练效率方面优于现有的零样本和少样本风格化方法。
英文摘要：
Fine-tuning advanced diffusion models for high-quality image stylization usually requires large training datasets and substantial computational resources, hindering their practical applicability. We propose Ada-Adapter, a novel framework for few-shot style personalization of diffusion models. Ada-Adapter leverages off-the-shelf diffusion models and pre-trained image feature encoders to learn a compact style representation from a limited set of source images. Our method enables efficient zero-shot style transfer utilizing a single reference image. Furthermore, with a small number of source images (three to five are sufficient) and a few minutes of fine-tuning, our method can capture intricate style details and conceptual characteristics, generating high-fidelity stylized images that align well with the provided text prompts. We demonstrate the effectiveness of our approach on various artistic styles, including flat art, 3D rendering, and logo design. Our experimental results show that Ada-Adapter outperforms existing zero-shot and few-shot stylization methods in terms of output quality, diversity, and training efficiency.
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。
