How to generate new versions of an original character?: an application of LoRA and DreamBooth fine-tuning of diffusion models

Boudier, Maëlys; Beltrán, Natalia; Michelangelo, Arianna

How to generate new versions of an original character?: an application of LoRA and DreamBooth fine-tuning of diffusion models

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Boudier, M, Beltrán, N, Michelangelo, A. How to generate new versions of an original character?: an application of LoRA and DreamBooth fine-tuning of diffusion models. 2024. handle: http://hdl.handle.net/10230/68765

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Abstract

This research explores the use of LoRA (Low-Rank Adaptation) and DreamBooth fine-tuning techniques on Stable Diffusion models to generate new versions of an original comic book character. By addressing the challenge of data scarcity, these techniques enable the creation of varied character poses from limited images, significantly enhancing efficiency in the comic creation process. The study demonstrates that fine-tuning pre-trained models with minimal computational resources can produce high-quality, consistent character images, reducing repetitive tasks for artists. This research paves the way for integrating AI into comic book creation, allowing artists greater creative freedom and productivity.

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Treball fi de màster de: Master's Degree in Data Science: Data Science for Decision Making Program. Curs 2023-2024
Tutors: Jesús Cerquides i Hannes Mueller