title: Stable Flow: Vital Layers for Training-Free Image Editing
summary: 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
paper_authors._id: 674015d59881b3bf0a862577
paper_authors.name: Omri Avrahami
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paper_authors.fullname: Omri Avrahami
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paper_authors.name: Or Patashnik
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paper_authors.fullname: Or Patashnik
paper_authors.type: user
paper_authors.user: orpatashnik
paper_authors._id: 674015d59881b3bf0a862579
paper_authors.name: Ohad Fried
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paper_authors.fullname: Ohad Fried
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paper_authors.user: ohadf
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paper_authors.name: Egor Nemchinov
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paper_authors.name: Kfir Aberman
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paper_authors.fullname: Kfir Aberman
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paper_authors.user: kaberman
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paper_authors.name: Dani Lischinski
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paper_authors.fullname: Dani Lischinski
paper_authors.type: user
paper_authors.user: danix3d
paper_authors._id: 674015d59881b3bf0a86257d
paper_authors.name: Daniel Cohen-Or
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:18:48.976Z
paper_authors._id: 628507161949ebcae8e24ec3
paper_authors.avatarUrl: /avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
paper_authors.fullname: Daniel Cohen-Or
paper_authors.type: user
paper_authors.user: cohenor
paper_title: Stable Flow: Vital Layers for Training-Free Image Editing
paper_summary: 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
paper_ai_summary: The work presents an automatic method to identify crucial layers in Diffusion Transformer models for consistent image editing and demonstrates its effectiveness through qualitative and quantitative evaluations.
paper_ai_keywords: Diffusion Transformer (DiT) flow-matching attention features coarse-to-fine synthesis vital layers image editing stable edits non-rigid modifications object addition image inversion flow models