Rectified-flow-based diffusion transformers, such as FLUX and OpenSora, have demonstrated exceptional performance in the field of image and video generation. Despite their robust generative capabilities, these models often suffer from inaccurate inversion, which could further limit their effectiveness in downstream tasks such as image and video editing. To address this issue, we propose RF-Solver, a novel training-free sampler that enhances inversion precision by reducing errors in the process of solving rectified flow ODEs. Specifically, we derive the exact formulation of the rectified flow ODE and perform a high-order Taylor expansion to estimate its nonlinear components, significantly decreasing the approximation error at each timestep. Building upon RF-Solver, we further design RF-Edit, which comprises specialized sub-modules for image and video editing. By sharing self-attention layer features during the editing process, RF-Edit effectively preserves the structural information of the source image or video while achieving high-quality editing results. Our approach is compatible with any pre-trained rectified-flow-based models for image and video tasks, requiring no additional training or optimization. Extensive experiments on text-to-image generation, image & video inversion, and image & video editing demonstrate the robust performance and adaptability of our methods. Code is available at https://github.com/wangjiangshan0725/RF-Solver-Edit.

该研究解决了整流流扩散变换器在图像和视频生成中的反演精度不足问题，通过提出RF-Solver这一新方法，显著降低了整流流ODE求解过程中的误差。基于RF-Solver的RF-Edit进一步促进了图像和视频编辑，能够在进行编辑时保留源内容的结构信息，并且该方法兼容所有预训练的整流流模型，具有广泛应用潜力。

驯化整流流以进行反演和编辑