Multi-Subject Personalization

Fuente: arXiv
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Hauptverfasser: Jain, Arushi, Paliwal, Shubham, Sharma, Monika, Jamwal, Vikram, Vig, Lovekesh
Format: Preprint
Veröffentlicht: 2024
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author Jain, Arushi
Paliwal, Shubham
Sharma, Monika
Jamwal, Vikram
Vig, Lovekesh
author_facet Jain, Arushi
Paliwal, Shubham
Sharma, Monika
Jamwal, Vikram
Vig, Lovekesh
contents Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render coherent subject interactions. We present Multi-Subject Personalization (MSP) to alleviate some of these challenges. We implement MSP using Stable Diffusion and assess our approach against other text-to-image models, showcasing its consistent generation of good-quality images representing intended subjects and interactions.
format Preprint
id arxiv_https___arxiv_org_abs_2405_12742
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Multi-Subject Personalization
Jain, Arushi
Paliwal, Shubham
Sharma, Monika
Jamwal, Vikram
Vig, Lovekesh
Computer Vision and Pattern Recognition
Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render coherent subject interactions. We present Multi-Subject Personalization (MSP) to alleviate some of these challenges. We implement MSP using Stable Diffusion and assess our approach against other text-to-image models, showcasing its consistent generation of good-quality images representing intended subjects and interactions.
title Multi-Subject Personalization
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2405.12742