Inversion-Free Style Transfer with Dual Rectified Flows

TL;DR

This paper introduces an inversion-free style transfer method using dual rectified flows that predicts content and style trajectories in parallel, enabling efficient and high-quality image stylization without inversion processes.

Contribution

The proposed framework is the first to perform style transfer solely with forward passes, jointly modeling content, style, and stylized distributions for improved robustness and efficiency.

Findings

Achieves high visual fidelity and content preservation.

Demonstrates generalization across diverse styles and content.

Offers computational efficiency over inversion-based methods.

Abstract

Style transfer, a pivotal task in image processing, synthesizes visually compelling images by seamlessly blending realistic content with artistic styles, enabling applications in photo editing and creative design. While mainstream training-free diffusion-based methods have greatly advanced style transfer in recent years, their reliance on computationally inversion processes compromises efficiency and introduces visual distortions when inversion is inaccurate. To address these limitations, we propose a novel \textit{inversion-free} style transfer framework based on dual rectified flows, which tackles the challenge of finding an unknown stylized distribution from two distinct inputs (content and style images), \textit{only with forward pass}. Our approach predicts content and style trajectories in parallel, then fuses them through a dynamic midpoint interpolation that integrates velocities from both paths while adapting to the evolving stylized image. By jointly modeling the content, style, and stylized distributions, our velocity field design achieves robust fusion and avoids the shortcomings of naive overlays. Attention injection further guides style integration, enhancing visual fidelity, content preservation, and computational efficiency. Extensive experiments demonstrate generalization across diverse styles and content, providing an effective and efficient pipeline for style transfer.