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

• URL: http://arxiv.org/abs/2602.01805v1
• Date: Mon, 02 Feb 2026 08:37:00 GMT
• Title: FlowBypass: Rectified Flow Trajectory Bypass for Training-Free Image Editing
• Authors: Menglin Han, Zhangkai Ni,
• Abstract summary: Training-free image editing has attracted increasing attention for its efficiency and independence from training data.<n>Previous attempts to address this issue typically employ backbone-specific feature manipulations, limiting general applicability.<n>We propose FlowBypass, a novel and analytical framework grounded in Rectified Flow that constructs a bypass directly connecting inversion and reconstruction trajectories.
• Score: 10.304374060580828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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