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× 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.
@article{arxiv.2412.07517,
title = {FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing},
author = {Yingying Deng and Xiangyu He and Changwang Mei and Peisong Wang and Fan Tang},
journal= {arXiv preprint arXiv:2412.07517},
year = {2024}
}