DUSA adapts classifiers and segmenters at test time by matching their predictions to conditional noise estimates from a pre-trained diffusion model, using a single timestep and active class selection.
Multiresolution Textual Inversion
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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 with different levels of detail and also to manipulate different resolutions using language. Once learned, the user can generate images at different levels of agreement to the original concept; "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 open-soure our code in the following URL: https://github.com/giannisdaras/multires_textual_inversion
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Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation
DUSA adapts classifiers and segmenters at test time by matching their predictions to conditional noise estimates from a pre-trained diffusion model, using a single timestep and active class selection.