From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho Heritage
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912466747785216 |
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| author | Amadeus, Marcellus Castañeda, William Alberto Cruz Zanella, André Felipe Mahlow, Felipe Rodrigues Perche |
| author_facet | Amadeus, Marcellus Castañeda, William Alberto Cruz Zanella, André Felipe Mahlow, Felipe Rodrigues Perche |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho Heritage Amadeus, Marcellus Castañeda, William Alberto Cruz Zanella, André Felipe Mahlow, Felipe Rodrigues Perche Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language 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. |
| title | From Pampas to Pixels: Fine-Tuning Diffusion Models for Gaúcho Heritage |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2401.05520 |