FireFlow实现图像语义编辑的快速逆向，8步完成高精度还原与编辑。
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
- 设计高效数值求解器，兼顾二阶精度与一阶效率。
- 相比现有方法提速3倍，重建误差更小，编辑效果更优。
- 零样本训练，适用于快速图像语义编辑场景。
尽管带蒸馏的修正流（ReFlow）提供了快速采样的潜力，但其快速逆向将图像还原为结构化噪声以实现恢复和后续编辑的问题仍未解决。本文提出FireFlow，一种简单而有效的零样本方法，继承了基于ReFlow模型（如FLUX）在生成方面的惊人能力，同时扩展至8步内实现精确逆向与编辑。我们首次证明，精心设计的数值求解器对ReFlow逆向至关重要：在保持一阶欧拉法实用效率的同时，达到二阶求解器的精度。该求解器相较当前最先进的ReFlow逆向与编辑技术实现3倍的运行时加速，同时具备更小的重建误差和更优的编辑结果，且无需训练。代码已开源。
原文摘要 · Abstract (English)
Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, a simple yet effective zero-shot approach that inherits the startling capacity of ReFlow-based models (such as FLUX) in generation while extending its capabilities to accurate inversion and editing in $8$ steps. We first demonstrate that a carefully designed numerical solver is pivotal for ReFlow inversion, enabling accurate inversion and reconstruction with the precision of a second-order solver while maintaining the practical efficiency of a first-order Euler method. This solver achieves a $3\times$ runtime speedup compared to state-of-the-art ReFlow inversion and editing techniques, while delivering smaller reconstruction errors and superior editing results in a training-free mode. The code is available at $\href{https://github.com/HolmesShuan/FireFlow}{this URL}$.
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