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Main Authors: Venkataramanan, Asvin Kumar, Shrestha, Sloke, Sriraman, Sundar Sripada Venugopalaswamy
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2312.03993
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author Venkataramanan, Asvin Kumar
Shrestha, Sloke
Sriraman, Sundar Sripada Venugopalaswamy
author_facet Venkataramanan, Asvin Kumar
Shrestha, Sloke
Sriraman, Sundar Sripada Venugopalaswamy
contents This project report summarizes our journey to perform stable diffusion fine-tuning on a dataset containing Calvin and Hobbes comics. The purpose is to convert any given input image into the comic style of Calvin and Hobbes, essentially performing style transfer. We train stable-diffusion-v1.5 using Low Rank Adaptation (LoRA) to efficiently speed up the fine-tuning process. The diffusion itself is handled by a Variational Autoencoder (VAE), which is a U-net. Our results were visually appealing for the amount of training time and the quality of input data that went into training.
format Preprint
id arxiv_https___arxiv_org_abs_2312_03993
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Style Transfer to Calvin and Hobbes comics using Stable Diffusion
Venkataramanan, Asvin Kumar
Shrestha, Sloke
Sriraman, Sundar Sripada Venugopalaswamy
Computer Vision and Pattern Recognition
Artificial Intelligence
This project report summarizes our journey to perform stable diffusion fine-tuning on a dataset containing Calvin and Hobbes comics. The purpose is to convert any given input image into the comic style of Calvin and Hobbes, essentially performing style transfer. We train stable-diffusion-v1.5 using Low Rank Adaptation (LoRA) to efficiently speed up the fine-tuning process. The diffusion itself is handled by a Variational Autoencoder (VAE), which is a U-net. Our results were visually appealing for the amount of training time and the quality of input data that went into training.
title Style Transfer to Calvin and Hobbes comics using Stable Diffusion
topic Computer Vision and Pattern Recognition
Artificial Intelligence
url https://arxiv.org/abs/2312.03993