FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
  Models
Authors: name: Vladimir Kulikov; name: Matan Kleiner; name: Inbar Huberman-Spiegelglas; name: Tomer Michaeli
Published: 2024-12-11 18:50:29

Abstract: 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.