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.

翻译：生成式人工智能已渗透社会各领域，并在多个方向上取得显著进展。尤其在文本到图像（TTI）模型中，潜扩散模型（LDMs）展现出基于文本提示生成视觉内容的卓越能力。本文探讨了LDMs在表征地方文化概念、历史人物及濒危物种方面的潜力。研究以巴西南里奥格兰德州（RS）的文化遗产为典型案例，旨在深化对生成模型如何助力捕捉与存续区域文化与历史身份的认知。论文阐述了完整方法论，包括对象选择、数据集构建及微调流程。成果展示了生成图像，并分析了各概念的实现挑战与可行性。最终，本研究揭示了该类模型在表征与存续不同区域及社区独特特质方面的强大能力。