From Pampas to Pixels: Fine-Tuning Diffusion Models for Ga\'ucho Heritage

TL;DR

This paper explores how fine-tuning Latent Diffusion Models can effectively generate culturally significant images, aiding in the preservation of regional heritage and identity through AI-generated visual content.

Contribution

It demonstrates a methodology for fine-tuning diffusion models to represent local cultural concepts, with a case study on Rio Grande do Sul's heritage.

Findings

Generated images successfully depict regional cultural elements.

Fine-tuning enhances the model's ability to capture local identity.

Challenges include dataset creation and concept representation.

Abstract

Generative AI has become pervasive in society, witnessing significant advancements in various domains. Particularly in the realm of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs), showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the cultural and historical identity of regions. The paper outlines the methodology, including subject selection, dataset creation, and the fine-tuning process. The results showcase the images generated, alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.