SteerFlow: Steering Rectified Flows for Faithful Inversion-Based Image Editing
Thinh Dao, Zhen Wang, Kien T.Pham, Long Chen

TL;DR
SteerFlow is a novel, model-agnostic image editing framework that enhances fidelity and control in inversion-based editing by using velocity consistency and adaptive masking.
Contribution
It introduces the Amortized Fixed-Point Solver, Trajectory Interpolation, and Adaptive Masking to improve source fidelity and editing quality in flow-based image editing.
Findings
Achieves better editing quality than existing methods on benchmark datasets.
Maintains high source fidelity with fewer inferences and constraints.
Extends to multi-turn editing without drift.
Abstract
Recent advances in flow-based generative models have enabled training-free, text-guided image editing by inverting an image into its latent noise and regenerating it under a new target conditional guidance. However, existing methods struggle to preserve source fidelity: higher-order solvers incur additional model inferences, truncated inversion constrains editability, and feature injection methods lack architectural transferability. To address these limitations, we propose SteerFlow, a model-agnostic editing framework with strong theoretical guarantees on source fidelity. In the forward process, we introduce an Amortized Fixed-Point Solver that implicitly straightens the forward trajectory by enforcing velocity consistency across consecutive timesteps, yielding a high-fidelity inverted latent. In the backward process, we introduce Trajectory Interpolation, which adaptively blends…
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