FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Authors: name: Yingying Deng, name: Xiangyu He, name: Changwang Mei, name: Peisong Wang, name: Fan Tang

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 3times 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
https://github.com/HolmesShuan/FireFlow{this URL}.