From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho Heritage
It addresses preserving cultural identity for regional communities, but is incremental as it applies existing methods to new data.
This paper tackled the problem of representing local cultural concepts like Gaúcho heritage using fine-tuned Latent Diffusion Models, resulting in generated images that demonstrate feasibility but with challenges in capturing specific concepts.
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.