FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
  Models
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.
675a8cf2c67a008f39d94c29
Vladimir Kulikov
admin_assigned
2024-12-16T10:42:23.525Z
63f6497eb29015adc33c1da7
/avatars/9119dffcec465b42c490d0e893699a60.svg
Vladimir Kulikov
user
fallenshock
675a8cf2c67a008f39d94c2a
Matan Kleiner
admin_assigned
2024-12-16T10:42:29.816Z
63511e3aa8822aadf5720ff9
/avatars/5798938f1c90d642b2d492b67c77aa4c.svg
Matan Kleiner
user
matankleiner
675a8cf2c67a008f39d94c2b
Inbar Huberman-Spiegelglas
admin_assigned
2024-12-16T10:42:36.697Z
637f42eb069b3a40ffb24f2e
/avatars/f9081fda9e2fb46884bc0e34027efb90.svg
Inbar Huberman-Spiegelglas
user
Inbarhub
675a8cf2c67a008f39d94c2c
Tomer Michaeli
admin_assigned
2024-12-16T10:42:46.188Z
6457c3f50664b05a5b1d941d
/avatars/0132be4d068d4b314f6b498b65411461.svg
Tomer Michaeli
user
Michato
FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
  Models
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.
FlowEdit is a text-based editing method for pre-trained text-to-image flow models that avoids inversion and optimization, yielding state-of-the-art results using ODEs.
text-to-image (T2I) diffusion/flow model noise map sampling process inversion-free optimization-free model agnostic ODE source and target distributions source and target text prompts Stable Diffusion 3 FLUX