title: TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
summary: Diffusion models have opened the path to a wide range of text-based image
  editing frameworks. However, these typically build on the multi-step nature of
  the diffusion backwards process, and adapting them to distilled, fast-sampling
  methods has proven surprisingly challenging. Here, we focus on a popular line
  of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion
  approach. We analyze its application to fast sampling methods and categorize
  its failures into two classes: the appearance of visual artifacts, and
  insufficient editing strength. We trace the artifacts to mismatched noise
  statistics between inverted noises and the expected noise schedule, and suggest
  a shifted noise schedule which corrects for this offset. To increase editing
  strength, we propose a pseudo-guidance approach that efficiently increases the
  magnitude of edits without introducing new artifacts. All in all, our method
  enables text-based image editing with as few as three diffusion steps, while
  providing novel insights into the mechanisms behind popular text-based editing
  approaches.
paper_authors._id: 66ac449b0318a823f53b102d
paper_authors.name: Gilad Deutch
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T09:02:20.331Z
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paper_authors.fullname: Gilad Deutch
paper_authors.type: user
paper_authors.user: Gilad-Deutch
paper_authors._id: 66ac449b0318a823f53b102e
paper_authors.name: Rinon Gal
paper_authors.status: admin_assigned
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paper_authors.fullname: Rinon Gal
paper_authors.type: user
paper_authors.user: rinong
paper_authors._id: 66ac449b0318a823f53b102f
paper_authors.name: Daniel Garibi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T09:02:30.739Z
paper_authors._id: 65fbf768980d4143b0d3ab52
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paper_authors.fullname: Daniel Garibi
paper_authors.type: user
paper_authors.user: garibida
paper_authors._id: 66ac449b0318a823f53b1030
paper_authors.name: Or Patashnik
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T09:02:36.437Z
paper_authors._id: 62853516e483e0d37b354ce1
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paper_authors.fullname: Or Patashnik
paper_authors.type: user
paper_authors.user: orpatashnik
paper_authors._id: 66ac449b0318a823f53b1031
paper_authors.name: Daniel Cohen-Or
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T09:02:42.040Z
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: TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
paper_summary: Diffusion models have opened the path to a wide range of text-based image
  editing frameworks. However, these typically build on the multi-step nature of
  the diffusion backwards process, and adapting them to distilled, fast-sampling
  methods has proven surprisingly challenging. Here, we focus on a popular line
  of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion
  approach. We analyze its application to fast sampling methods and categorize
  its failures into two classes: the appearance of visual artifacts, and
  insufficient editing strength. We trace the artifacts to mismatched noise
  statistics between inverted noises and the expected noise schedule, and suggest
  a shifted noise schedule which corrects for this offset. To increase editing
  strength, we propose a pseudo-guidance approach that efficiently increases the
  magnitude of edits without introducing new artifacts. All in all, our method
  enables text-based image editing with as few as three diffusion steps, while
  providing novel insights into the mechanisms behind popular text-based editing
  approaches.
paper_ai_summary: Methods are proposed to enhance fast sampling in text-based diffusion models for image editing, addressing visual artifacts and insufficient editing strength.
paper_ai_keywords: diffusion models text-based image editing DDPM-noise inversion visual artifacts noise statistics noise schedule pseudo-guidance editing strength