SlerpFlow：用于整流流反演的球形轨迹校正
SlerpFlow: Spherical Trajectory Correction for Rectified Flow Inversion
AI总结：
研究针对基于整流流的图像生成中反演难题，提出SlerpFlow方法，基于流形假设，整合球形线性插值校正流速度方向，缓存校正速度，实现高精度反演，提升重建保真度与编辑语义对齐，无需额外训练。
AI中文摘要：
基于整流流的扩散变压器，特别是FLUX，在高质量图像生成中表现出色。然而，由于线性求解器的离散化误差，实现快速准确的反演（将图像转换回潜在噪声以进行忠实重建和编辑）仍然是一个具有挑战性的瓶颈。本文介绍了SlerpFlow，一种简单而高效的零样本方法，它释放了FLUX在高保真反演和编辑方面的全部潜力。与现有方法不同，SlerpFlow基于流形假设提出了一种几何观点，通过整合球形线性插值（Slerp）来校正超球面上的流速度方向，严格遵循潜在空间的内在曲率。通过缓存校正后的速度，SlerpFlow在保持一阶欧拉求解器计算效率的同时实现了高精度反演。大量实验表明，SlerpFlow提高了重建保真度，在编辑中实现了更强的语义对齐，且无需额外训练。
英文摘要：
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.
