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Multiresolution Textual Inversion
Giannis Daras · Alex Dimakis
Event URL: https://openreview.net/forum?id=3JCa_cqKaLy »
We extend Textual Inversion to learn pseudo-words that represent a concept at different resolutions. This allows us to generate images that use the concept at different resolutions and also to manipulate different resolutions using language.Once learned, the user can generate images that agree with the original concept at different levels of detail; ``A photo of $S^*(0)$'' produces the exact object while the prompt ``A photo of $S^*(0.8)$'' only matches the rough outlines and colors. Our framework allows us to generate images that use different resolutions of an image (e.g. details, textures, styles) as separate pseudo-words that can be composed in various ways.
We extend Textual Inversion to learn pseudo-words that represent a concept at different resolutions. This allows us to generate images that use the concept at different resolutions and also to manipulate different resolutions using language.Once learned, the user can generate images that agree with the original concept at different levels of detail; ``A photo of $S^*(0)$'' produces the exact object while the prompt ``A photo of $S^*(0.8)$'' only matches the rough outlines and colors. Our framework allows us to generate images that use different resolutions of an image (e.g. details, textures, styles) as separate pseudo-words that can be composed in various ways.
Author Information
Giannis Daras (University of Texas, Austin)
Alex Dimakis (University of Texas, Austin)
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