FlowEdit：使用预训练流模型进行无反转的基于文本的编辑

FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models

December 11, 2024

作者: Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli

cs.AI

摘要

使用预训练的文本到图像（T2I）扩散/流模型编辑真实图像通常涉及将图像反转为其相应的噪声图。然而，仅靠反转通常无法获得令人满意的结果，因此许多方法还会介入采样过程。这些方法可以实现改进的结果，但在不同模型架构之间并不是无缝转移的。在这里，我们介绍了FlowEdit，这是一种基于文本的编辑方法，适用于预训练的T2I流模型，它无需反转、无需优化，并且与模型无关。我们的方法构建了一个常微分方程（ODE），直接映射源分布和目标分布（对应源文本提示和目标文本提示），并实现比反转方法更低的传输成本。这导致了最先进的结果，我们以稳定扩散3和FLUX为例进行说明。代码和示例可在项目网页上找到。

English

Editing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory results, and therefore many methods additionally intervene in the sampling process. Such methods achieve improved results but are not seamlessly transferable between model architectures. Here, we introduce FlowEdit, a text-based editing method for pre-trained T2I flow models, which is inversion-free, optimization-free and model agnostic. Our method constructs an ODE that directly maps between the source and target distributions (corresponding to the source and target text prompts) and achieves a lower transport cost than the inversion approach. This leads to state-of-the-art results, as we illustrate with Stable Diffusion 3 and FLUX. Code and examples are available on the project's webpage.