SlerpFlow: Spherical Trajectory Correction for Rectified Flow Inversion

Rectified-flow-based diffusion transformers, particularly FLUX, have demonstrated outstanding performance in high-quality image generation. However, achieving fast and accurate inversion—transforming images back to latent noise for faithful reconstruction and editing—remains a challenging bottleneck due to the discretization errors of linear solvers. This paper introduces SlerpFlow, a straightforward yet highly effective zero-shot approach that unlocks the full potential of FLUX for high-fidelity inversion and editing. Unlike existing approaches (e.g., RF-Solver) that rely on complex numerical approximations such as high-order Taylor expansions to correct trajectory errors, we present a geometric view based on the Manifold Hypothesis: the empirically observed trajectory curvature is not a numerical artifact, but rather serves as a necessary “centripetal force” that constrains the flow to remain on the data manifold. Guided by this insight, SlerpFlow integrates Spherical Linear Interpolation (Slerp) to rectify flow velocity directions on the hypersphere, strictly adhering to the intrinsic curvature of the latent space. Crucially, by caching the corrected velocity for subsequent steps, SlerpFlow achieves high-precision inversion while maintaining the computational efficiency of a first-order Euler solver. Extensive experiments on FLUX-based reconstruction and editing tasks demonstrate that SlerpFlow improves reconstruction fidelity and achieves stronger semantic alignment in editing without requiring additional training. Code is available at https://github.com/0answer0/SlerpFlow.

Modern image generation models can create high-quality images, but editing a real image often requires first converting the image back into the model's hidden starting representation. This conversion is difficult: small numerical errors can make the reconstructed or edited image lose details or change unintended regions. Our work proposes SlerpFlow, a simple method that improves this conversion process by guiding the model's trajectory in a more geometry-aware way. The method does not require extra training and keeps the computational cost comparable to existing fast solvers. Experiments show that SlerpFlow improves image reconstruction quality and helps image editing better follow the target text prompt while maintaining reasonable consistency with the original image.