Taming Rectified Flow for Inversion and Editing (2411.04746v2)

Abstract: Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, 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 effectively enhances inversion precision by mitigating the errors in the ODE-solving process of rectified flow. Specifically, we derive the exact formulation of the rectified flow ODE and apply the high-order Taylor expansion to estimate its nonlinear components, significantly enhancing the precision of ODE solutions at each timestep. Building upon RF-Solver, we further propose RF-Edit, a general feature-sharing-based framework for image and video editing. By incorporating self-attention features from the inversion process into 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 across generation, inversion, and editing tasks in both image and video modalities demonstrate the superiority and versatility of our method. The source code is available at https://github.com/wangjiangshan0725/RF-Solver-Edit.

Insights into "Taming Rectified Flow for Inversion and Editing"

The paper "Taming Rectified Flow for Inversion and Editing" by Wang et al. provides a significant paper of rectified-flow-based generative models, specifically focusing on improving inversion accuracy and introducing versatile editing capabilities in image and video processing. The contributions are centered around two novel methods, RF-Solver and RF-Edit, each addressing critical challenges in existing generative modeling techniques.

Rectified-flow-based models, such as FLUX and OpenSora, have made remarkable advancements in generating high-quality images and videos. However, these models often exhibit limitations in inversion tasks, where reconstructing the original image or video with high fidelity poses substantial challenges. The inaccuracies during the inversion process, primarily due to coarse approximations in solving the ordinary differential equations (ODEs) that govern the rectified flow, impair their efficacy across downstream applications, including image and video editing.

The proposed RF-Solver is a pivotal solution designed to enhance inversion precision by refining the ODE-solving process of rectified flow models. By deriving the exact formulation of the rectified flow ODE and employing a high-order Taylor expansion, RF-Solver significantly increases the accuracy of ODE solutions at each discrete timestep. This more refined approximation method results in improved inversion and reconstruction outcomes, avoiding the cumulative errors that plague traditional solutions. Notably, RF-Solver is a training-free enhancement applicable to any pre-trained rectified-flow-based generative model, allowing immediate improvement without additional computational cost or training.

Building on the foundational improvements of RF-Solver, RF-Edit is introduced as a feature-sharing-based framework tailored for image and video editing tasks. The self-attention features captured during inversion play a critical role in this process by integrating structural information of the source data into the editing phase. This allows the preservation of core attributes of the source material while facilitating high-quality edits. RF-Edit extends the capability of rectified-flow models to handle complex editing scenarios, achieving superior performance over various state-of-the-art methods in both image and video domains.

The experimental validation presented in the paper shows substantial progress across several metrics, including MSE, SSIM, and LPIPS for reconstruction accuracy, and FID and CLIP scores for generative tasks. These results corroborate the efficacy of RF-Solver and RF-Edit in overcoming traditional limitations. Moreover, the novel framework proposed in RF-Edit demonstrates promising potential for real-world application in video editing, underscoring the growing importance of consistent and high-fidelity video processing.

The implications of this research extend to both practical applications and theoretical advancements in the field of generative modeling. By addressing inversion accuracy with RF-Solver and improving editing capabilities with RF-Edit, the work paves the way for more robust and versatile image and video generation systems. Future work could explore the integration of these methods with newer model architectures to further enhance compatibility and performance across a broader range of tasks. Additionally, the potential for extending RF-Solver and RF-Edit to other modalities or data types may yield intriguing directions for continued research in the generative model landscape.