FlowBypass: Rectified Flow Trajectory Bypass for Training-Free Image Editing

TL;DR

FlowBypass introduces a training-free image editing method that constructs a direct trajectory bypass to improve prompt alignment and detail preservation without relying on feature manipulations.

Contribution

It proposes a novel Rectified Flow-based framework that constructs a direct bypass between inversion and reconstruction trajectories, enhancing editing performance.

Findings

Outperforms state-of-the-art methods in prompt alignment

Preserves high-fidelity details in irrelevant regions

Mitigates error accumulation in trajectory-based editing

Abstract

Training-free image editing has attracted increasing attention for its efficiency and independence from training data. However, existing approaches predominantly rely on inversion-reconstruction trajectories, which impose an inherent trade-off: longer trajectories accumulate errors and compromise fidelity, while shorter ones fail to ensure sufficient alignment with the edit prompt. Previous attempts to address this issue typically employ backbone-specific feature manipulations, limiting general applicability. To address these challenges, we propose FlowBypass, a novel and analytical framework grounded in Rectified Flow that constructs a bypass directly connecting inversion and reconstruction trajectories, thereby mitigating error accumulation without relying on feature manipulations. We provide a formal derivation of two trajectories, from which we obtain an approximate bypass formulation and its numerical solution, enabling seamless trajectory transitions. Extensive experiments demonstrate that FlowBypass consistently outperforms state-of-the-art image editing methods, achieving stronger prompt alignment while preserving high-fidelity details in irrelevant regions.