Document Type
Conference Proceeding
Publication Date
1-1-2024
Journal / Book Title
Lecture Notes in Networks and Systems
Abstract
Stable Diffusion is a captivating text-to-image model that generates images based on text input. However, a major challenge is that it is pretrained on a specific dataset, limiting its ability to generate images outside of the given data. In this paper, we propose to harness two models based on neural networks, Hypernetworks and DreamBooth, to allow the introduction of any image into Stable Diffusion, addressing versatility with minimal additional training data. This work targets AI applications such as augmenting next-generation multipurpose robots, enhancing human-robot collaboration, feeding intelligent tutoring systems, training autonomous cars, injecting subjects for photo personalization, producing high quality movie animations etc. It can contribute to AI in smart cities: facets such as smart living and smart mobility.
DOI
10.1007/978-3-031-47721-8_44
Journal ISSN / Book ISBN
85182514364 (Scopus)
Montclair State University Digital Commons Citation
Hidalgo, Rafael; Salah, Nesreen; Chandra Jetty, Rajiv; Jetty, Anupama; and Varde, Aparna S., "Personalizing Text-to-Image Diffusion Models by Fine-Tuning Classification for AI Applications" (2024). School of Computing Faculty Scholarship and Creative Works. 30.
https://digitalcommons.montclair.edu/computing-facpubs/30