Free Lunch for Stabilizing Rectified Flow Inversion

• URL: http://arxiv.org/abs/2602.11850v2
• Date: Fri, 13 Feb 2026 02:39:35 GMT
• Title: Free Lunch for Stabilizing Rectified Flow Inversion
• Authors: Chenru Wang, Beier Zhu, Chi Zhang,
• Abstract summary: Rectified-Flow (RF)-based generative models have emerged as strong alternatives to traditional diffusion models.<n>We propose Proximal-Mean Inversion (PMI), a training-free gradient correction method.<n>We also introduce mimic-CFG, a lightweight velocity correction scheme for editing tasks.
• Score: 11.80912018629953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rectified-Flow (RF)-based generative models have recently emerged as strong alternatives to traditional diffusion models, demonstrating state-of-the-art performance across various tasks. By learning a continuous velocity field that transforms simple noise into complex data, RF-based models not only enable high-quality generation, but also support training-free inversion, which facilitates downstream tasks such as reconstruction and editing. However, existing inversion methods, such as vanilla RF-based inversion, suffer from approximation errors that accumulate across timesteps, leading to unstable velocity fields and degraded reconstruction and editing quality. To address this challenge, we propose Proximal-Mean Inversion (PMI), a training-free gradient correction method that stabilizes the velocity field by guiding it toward a running average of past velocities, constrained within a theoretically derived spherical Gaussian. Furthermore, we introduce mimic-CFG, a lightweight velocity correction scheme for editing tasks, which interpolates between the current velocity and its projection onto the historical average, balancing editing effectiveness and structural consistency. Extensive experiments on PIE-Bench demonstrate that our methods significantly improve inversion stability, image reconstruction quality, and editing fidelity, while reducing the required number of neural function evaluations. Our approach achieves state-of-the-art performance on the PIE-Bench with enhanced efficiency and theoretical soundness.

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