title: FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
    Models
summary: 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.
paper_authors._id: 675a8cf2c67a008f39d94c29
paper_authors.name: Vladimir Kulikov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:42:23.525Z
paper_authors._id: 63f6497eb29015adc33c1da7
paper_authors.avatarUrl: /avatars/9119dffcec465b42c490d0e893699a60.svg
paper_authors.fullname: Vladimir Kulikov
paper_authors.type: user
paper_authors.user: fallenshock
paper_authors._id: 675a8cf2c67a008f39d94c2a
paper_authors.name: Matan Kleiner
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:42:29.816Z
paper_authors._id: 63511e3aa8822aadf5720ff9
paper_authors.avatarUrl: /avatars/5798938f1c90d642b2d492b67c77aa4c.svg
paper_authors.fullname: Matan Kleiner
paper_authors.type: user
paper_authors.user: matankleiner
paper_authors._id: 675a8cf2c67a008f39d94c2b
paper_authors.name: Inbar Huberman-Spiegelglas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:42:36.697Z
paper_authors._id: 637f42eb069b3a40ffb24f2e
paper_authors.avatarUrl: /avatars/f9081fda9e2fb46884bc0e34027efb90.svg
paper_authors.fullname: Inbar Huberman-Spiegelglas
paper_authors.type: user
paper_authors.user: Inbarhub
paper_authors._id: 675a8cf2c67a008f39d94c2c
paper_authors.name: Tomer Michaeli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:42:46.188Z
paper_authors._id: 6457c3f50664b05a5b1d941d
paper_authors.avatarUrl: /avatars/0132be4d068d4b314f6b498b65411461.svg
paper_authors.fullname: Tomer Michaeli
paper_authors.type: user
paper_authors.user: Michato
paper_title: FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
    Models
paper_summary: 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.
paper_ai_keywords: text-to-image (T2I) diffusion/flow model noise map sampling process inversion-free optimization-free model agnostic ODE (Ordinary Differential Equation) source and target distributions transport cost State-of-the-art results Stable Diffusion 3 FLUX