BRAT: Bonus oRthogonAl Token for Architecture Agnostic Textual Inversion

Fuente: arXiv
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1. Verfasser: Baker, James
Format: Preprint
Veröffentlicht: 2024
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author Baker, James
author_facet Baker, James
contents Textual Inversion remains a popular method for personalizing diffusion models, in order to teach models new subjects and styles. We note that textual inversion has been underexplored using alternatives to the UNet, and experiment with textual inversion with a vision transformer. We also seek to optimize textual inversion using a strategy that does not require explicit use of the UNet and its idiosyncratic layers, so we add bonus tokens and enforce orthogonality. We find the use of the bonus token improves adherence to the source images and the use of the vision transformer improves adherence to the prompt. Code is available at https://github.com/jamesBaker361/tex_inv_plus.
format Preprint
id arxiv_https___arxiv_org_abs_2408_04785
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle BRAT: Bonus oRthogonAl Token for Architecture Agnostic Textual Inversion
Baker, James
Computer Vision and Pattern Recognition
Textual Inversion remains a popular method for personalizing diffusion models, in order to teach models new subjects and styles. We note that textual inversion has been underexplored using alternatives to the UNet, and experiment with textual inversion with a vision transformer. We also seek to optimize textual inversion using a strategy that does not require explicit use of the UNet and its idiosyncratic layers, so we add bonus tokens and enforce orthogonality. We find the use of the bonus token improves adherence to the source images and the use of the vision transformer improves adherence to the prompt. Code is available at https://github.com/jamesBaker361/tex_inv_plus.
title BRAT: Bonus oRthogonAl Token for Architecture Agnostic Textual Inversion
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2408.04785