EKFEdit: Extended Kalman Filter for Training-Free Flow-Based Image Editing

Abstract

Despite recent advances in training-free text-guided image editing using pretrained rectified flow models, achieving a balance between text adherence and background preservation remains a challenge. Existing methods typically treat image editing as an open-loop trajectory generation task, adopting either an inversion-based or inversion-free paradigm. However, neither can perfectly achieve this goal. Specifically, inversion-based approaches accumulate inversion errors without feedback, whereas inversion-free methods exhibit unstable trajectories due to the lack of constraints. To address this, we propose EKFEdit, a novel framework that formulates image editing as a sequential state estimation problem in a nonlinear dynamic system, which models inversion-free editing trajectories as the system’s prior dynamics and leverages an Extended Kalman Filter to adaptively fuse observations from external editing algorithms. EKFEdit enables adaptive trajectory correction, effectively combining the complementary strengths of the different paradigms. We instantiate this framework into two concrete approaches (EKFEdit-RF and EKFEdit-DNA) via different observation methods, respectively. Extensive experiments demonstrate that our approaches achieve new state-of-the-art performance compared to previous methods.

Code is available at https://github.com/baiyeweiguang/EKFEdit.