Adams-Bashforth-Moulton 解决方案用于 rectified 流中的反向和编辑
Adams Bashforth Moulton Solver for Inversion and Editing in Rectified Flow
AI总结：
本文提出ABM求解器，通过多步预测校正和自适应步长调整提升rectified流模型的求解精度和编辑质量，无需额外训练。
摘要：
rectified 流模型在图像和视频生成任务中取得了显著的性能。然而，现有的数值求解器在快速采样和高精度解之间面临权衡，限制了它们在下游应用如重建和编辑中的有效性。为了解决这一挑战，我们提出利用Adams-Bashforth-Moulton（ABM）预测校正方法来提高rectified流模型中ODE求解的准确性。具体来说，我们引入了ABM求解器，它整合了多步预测校正方法以减少局部截断误差，并采用自适应步长调整来提高采样速度。此外，为了有效保留未编辑区域并促进语义修改，我们引入了掩码引导的特征注入模块。我们估计自相似性以生成空间掩码，以区分保留区域和可用于编辑的区域。在多个高分辨率图像数据集上的广泛实验验证了ABM求解器在提高反向精度和编辑质量方面的显著改进，优于现有求解器，而无需额外训练或优化。
英文摘要：
Rectified flow models have achieved remarkable performance in image and video generation tasks. However, existing numerical solvers face a trade-off between fast sampling and high accuracy solutions, limiting their effectiveness in downstream applications such as reconstruction and editing. To address this challenge, we propose leveraging the Adams Bashforth Moulton (ABM) predictor corrector method to enhance the accuracy of ODE solving in rectified flow models. Specifically, we introduce ABM Solver, which integrates a multi step predictor corrector approach to reduce local truncation errors and employs Adaptive Step Size Adjustment to improve sampling speed. Furthermore, to effectively preserve non edited regions while facilitating semantic modifications, we introduce a Mask Guided Feature Injection module. We estimate self-similarity to generate a spatial mask that differentiates preserved regions from those available for editing. Extensive experiments on multiple high resolution image datasets validate that ABM Solver significantly improves inversion precision and editing quality, outperforming existing solvers without requiring additional training or optimization.
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