From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho Heritage
William Alberto Cruz Castañeda, Marcellus Amadeus, André Felipe Zanella, Felipe Rodrigues Perche Mahlow · Journal of the Brazilian Computer Society · 2025
Generative Artificial Intelligence has become pervasive in society, witnessing significant advancements in various domains. Particularly in the domain 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 regional culture and historical identity. The article outlines the methodology, including subject selection, dataset creation, and fine-tuning process. The results showcase the picture generation 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.