From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho
  Heritage
Marcellus Amadeus William Alberto Cruz Castañeda André Felipe Zanella Felipe Rodrigues Perche Mahlow
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.