FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
This work addresses a bottleneck in image semantic editing for users of generative models, offering an incremental improvement in inversion efficiency.
The paper tackles the problem of fast inversion for Rectified Flow models to enable image editing, achieving a 3x runtime speedup and smaller reconstruction errors compared to state-of-the-art methods.
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 $\href{https://github.com/HolmesShuan/FireFlow}{this URL}$.