Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder

TL;DR

Ada-Adapter enables rapid, high-quality style personalization of diffusion models using minimal data and computation, outperforming existing methods in diverse artistic styles.

Contribution

We introduce Ada-Adapter, a framework that leverages pre-trained encoders and diffusion models for efficient few-shot style transfer with minimal data and training.

Findings

Outperforms existing stylization methods in quality and diversity.

Requires only 3-5 images and a few minutes of fine-tuning.

Effective across various artistic styles including flat art and logo design.

Abstract

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.