FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Yingying Deng, Xiangyu He, Changwang Mei, Peisong Wang, Fan Tang
2024-12-16
Summary
This paper talks about FireFlow, a new method that makes it faster and easier to edit images by quickly transforming them back from noise while maintaining high quality.
What's the problem?
When using Rectified Flows (ReFlows) for image editing, the process of turning an edited image back into a clear picture can be slow and often results in poor quality. This makes it hard to achieve realistic edits without spending a lot of time and effort.
What's the solution?
FireFlow introduces a simple approach that speeds up the inversion process (turning noise back into images) while ensuring the images remain high-quality. It uses a specially designed numerical solver that combines the speed of simpler methods with the accuracy of more complex ones. This allows FireFlow to perform image edits in just 8 steps, significantly improving efficiency and reducing errors compared to previous methods.
Why it matters?
This research is important because it enhances the ability of AI to edit images quickly and effectively. By making image editing faster and maintaining high quality, FireFlow can benefit various applications such as graphic design, video production, and social media content creation, where quick and realistic edits are essential.
Abstract
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}.