TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
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.
66ac449b0318a823f53b102d
Gilad Deutch
admin_assigned
2024-08-02T09:02:20.331Z
6471ead322016353ae3915c7
https://cdn-avatars.huggingface.co/v1/production/uploads/6471ead322016353ae3915c7/pR9CZBkEtWM1bAolwItdY.jpeg
Gilad Deutch
user
Gilad-Deutch
66ac449b0318a823f53b102e
Rinon Gal
admin_assigned
2024-08-02T09:02:25.674Z
627c1360f19c5eb46d55ba05
https://cdn-avatars.huggingface.co/v1/production/uploads/1652712871747-627c1360f19c5eb46d55ba05.jpeg
Rinon Gal
user
rinong
66ac449b0318a823f53b102f
Daniel Garibi
admin_assigned
2024-08-02T09:02:30.739Z
65fbf768980d4143b0d3ab52
/avatars/018a26ce944860e9ab97ca43765e7d76.svg
Daniel Garibi
user
garibida
66ac449b0318a823f53b1030
Or Patashnik
admin_assigned
2024-08-02T09:02:36.437Z
62853516e483e0d37b354ce1
https://cdn-avatars.huggingface.co/v1/production/uploads/62853516e483e0d37b354ce1/t5Tyd3E07w26B9Z3XpZWI.jpeg
Or Patashnik
user
orpatashnik
66ac449b0318a823f53b1031
Daniel Cohen-Or
admin_assigned
2024-08-02T09:02:42.040Z
628507161949ebcae8e24ec3
/avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
Daniel Cohen-Or
user
cohenor
TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
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.
Methods are proposed to enhance fast sampling in text-based diffusion models for image editing, addressing visual artifacts and insufficient editing strength.
diffusion models text-based image editing DDPM-noise inversion visual artifacts noise statistics noise schedule pseudo-guidance editing strength