无需反演和优化，直接用微分方程实现文本编辑
FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models
- 构建微分方程直接连接源与目标文本分布
- 运输成本低于传统反演方法，效果更优
- 兼容多种模型，无需调整即可使用
使用预训练的文本到图像扩散/流模型编辑真实图像时，通常需要将图像反演为对应的噪声图。然而，仅靠反演难以获得满意结果，因此许多方法还需干预采样过程。这些方法虽效果提升，但无法在不同模型架构间无缝迁移。本文提出FlowEdit，一种针对预训练T2I流模型的无反演、无优化、模型无关的文本编辑方法。该方法构建一个微分方程，直接映射源分布（对应源文本提示）与目标分布（对应目标文本提示），其运输成本低于传统反演方法，从而实现当前最佳性能。我们在Stable Diffusion 3和FLUX上验证了该方法的有效性。代码与示例已在项目主页提供。
原文摘要 · Abstract (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.
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