Title: FlowBypass: General Training-Free Rectified Flow Image Editing via Trajectory Bypass
Primary area: generative models
Keywords: Training-free, Image Editing, Rectified Flow

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.