How to Blend Concepts in Diffusion Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909321816702976 |
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| author | Olearo, Lorenzo Longari, Giorgio Melzi, Simone Raganato, Alessandro Peñaloza, Rafael |
| author_facet | Olearo, Lorenzo Longari, Giorgio Melzi, Simone Raganato, Alessandro Peñaloza, Rafael |
| contents | For the last decade, there has been a push to use multi-dimensional (latent) spaces to represent concepts; and yet how to manipulate these concepts or reason with them remains largely unclear. Some recent methods exploit multiple latent representations and their connection, making this research question even more entangled. Our goal is to understand how operations in the latent space affect the underlying concepts. To that end, we explore the task of concept blending through diffusion models. Diffusion models are based on a connection between a latent representation of textual prompts and a latent space that enables image reconstruction and generation. This task allows us to try different text-based combination strategies, and evaluate easily through a visual analysis. Our conclusion is that concept blending through space manipulation is possible, although the best strategy depends on the context of the blend. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14280 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How to Blend Concepts in Diffusion Models Olearo, Lorenzo Longari, Giorgio Melzi, Simone Raganato, Alessandro Peñaloza, Rafael Computer Vision and Pattern Recognition Artificial Intelligence For the last decade, there has been a push to use multi-dimensional (latent) spaces to represent concepts; and yet how to manipulate these concepts or reason with them remains largely unclear. Some recent methods exploit multiple latent representations and their connection, making this research question even more entangled. Our goal is to understand how operations in the latent space affect the underlying concepts. To that end, we explore the task of concept blending through diffusion models. Diffusion models are based on a connection between a latent representation of textual prompts and a latent space that enables image reconstruction and generation. This task allows us to try different text-based combination strategies, and evaluate easily through a visual analysis. Our conclusion is that concept blending through space manipulation is possible, although the best strategy depends on the context of the blend. |
| title | How to Blend Concepts in Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.14280 |