Stable Flow: Vital Layers for Training-Free Image Editing
Authors: name: Omri Avrahami, name: Or Patashnik, name: Ohad Fried, name: Egor Nemchinov, name: Kfir Aberman, name: Dani Lischinski, name: Daniel Cohen-Or

Diffusion models have revolutionized the field of content synthesis and
editing. Recent models have replaced the traditional UNet architecture with the
Diffusion Transformer (DiT), and employed flow-matching for improved training
and sampling. However, they exhibit limited generation diversity. In this work,
we leverage this limitation to perform consistent image edits via selective
injection of attention features. The main challenge is that, unlike the
UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it
unclear in which layers to perform the injection. Therefore, we propose an
automatic method to identify "vital layers" within DiT, crucial for image
formation, and demonstrate how these layers facilitate a range of controlled
stable edits, from non-rigid modifications to object addition, using the same
mechanism. Next, to enable real-image editing, we introduce an improved image
inversion method for flow models. Finally, we evaluate our approach through
qualitative and quantitative comparisons, along with a user study, and
demonstrate its effectiveness across multiple applications. The project page is
available at https://omriavrahami.com/stable-flow