Pith. sign in

REVIEW 3 cited by

Multiresolution Textual Inversion

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2211.17115 v1 pith:PCFTBJRT submitted 2022-11-30 cs.CV cs.AIcs.LG

Giannis Daras , Alexandros G. Dimakis This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords differentconceptgenerateimagesinversionresolutionstextualallows
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

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

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Concept Guided Co-salient Object Detection

    cs.CV 2024-12 conditional novelty 6.0 of 10

    ConceptCoSOD extracts a shared text embedding from an image group and guides co-salient object segmentation with it, outperforming five baselines on three clean and five corrupted datasets.

  2. Opt-In Art: Learning Art Styles Only from Few Examples

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A diffusion model pretrained exclusively on photographs can learn a painter's style from just a handful of examples, matching the style fidelity of models pretrained on large art-containing datasets.

  3. Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    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.

Pith tools