Diffusion Self-Guidance for Controllable Image Generation
Large-scale generative models are capable of producing high-quality images
from detailed text descriptions. However, many aspects of an image are
difficult or impossible to convey through text. We introduce self-guidance, a
method that provides greater control over generated images by guiding the
internal representations of diffusion models. We demonstrate that properties
such as the shape, location, and appearance of objects can be extracted from
these representations and used to steer sampling. Self-guidance works similarly
to classifier guidance, but uses signals present in the pretrained model
itself, requiring no additional models or training. We show how a simple set of
properties can be composed to perform challenging image manipulations, such as
modifying the position or size of objects, merging the appearance of objects in
one image with the layout of another, composing objects from many images into
one, and more. We also show that self-guidance can be used to edit real images.
For results and an interactive demo, see our project page at
https://dave.ml/selfguidance/
64794aaca68454566355bbc8
Dave Epstein
64794aaca68454566355bbc9
Allan Jabri
64794aaca68454566355bbca
Ben Poole
64794aaca68454566355bbcb
Alexei A. Efros
64794aaca68454566355bbcc
Aleksander Holynski
admin_assigned
2023-06-07T12:56:22.480Z
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/avatars/85d94b4022577747b8d2d10a82c2f3c7.svg
Aleksander Holynski
user
holynski
Diffusion Self-Guidance for Controllable Image Generation
Large-scale generative models are capable of producing high-quality images
from detailed text descriptions. However, many aspects of an image are
difficult or impossible to convey through text. We introduce self-guidance, a
method that provides greater control over generated images by guiding the
internal representations of diffusion models. We demonstrate that properties
such as the shape, location, and appearance of objects can be extracted from
these representations and used to steer sampling. Self-guidance works similarly
to classifier guidance, but uses signals present in the pretrained model
itself, requiring no additional models or training. We show how a simple set of
properties can be composed to perform challenging image manipulations, such as
modifying the position or size of objects, merging the appearance of objects in
one image with the layout of another, composing objects from many images into
one, and more. We also show that self-guidance can be used to edit real images.
For results and an interactive demo, see our project page at
https://dave.ml/selfguidance/
Self-guidance enhances control over image generation by manipulating internal representations of diffusion models, enabling complex image manipulations and real-image editing using signals inherent to the pretrained model.
self-guidance diffusion models internal representations image generation classifier guidance image manipulations real-image editing