FAGStyle: Feature Augmentation on Geodesic Surface for Zero-shot Text-guided Diffusion Image Style Transfer

TL;DR

FAGStyle is a zero-shot, text-guided diffusion method for image style transfer that improves style consistency and content preservation by augmenting features on a geodesic surface and using innovative loss functions.

Contribution

The paper introduces FAGStyle, which enhances style transfer by incorporating feature augmentation on geodesic surfaces and novel loss functions for better style and content retention.

Findings

Outperforms existing style transfer methods in style consistency

Maintains high content fidelity across diverse images

Effective with both imagined and common styles

Abstract

The goal of image style transfer is to render an image guided by a style reference while maintaining the original content. Existing image-guided methods rely on specific style reference images, restricting their wider application and potentially compromising result quality. As a flexible alternative, text-guided methods allow users to describe the desired style using text prompts. Despite their versatility, these methods often struggle with maintaining style consistency, reflecting the described style accurately, and preserving the content of the target image. To address these challenges, we introduce FAGStyle, a zero-shot text-guided diffusion image style transfer method. Our approach enhances inter-patch information interaction by incorporating the Sliding Window Crop technique and Feature Augmentation on Geodesic Surface into our style control loss. Furthermore, we integrate a Pre-Shape self-correlation consistency loss to ensure content consistency. FAGStyle demonstrates superior performance over existing methods, consistently achieving stylization that retains the semantic content of the source image. Experimental results confirms the efficacy of FAGStyle across a diverse range of source contents and styles, both imagined and common.