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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22038 |
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| _version_ | 1866915516223848448 |
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| author | Zhong, Zhihua Huang, Xuanyang |
| author_facet | Zhong, Zhihua Huang, Xuanyang |
| contents | Latent space is one of the key concepts in generative AI, offering powerful means for creative exploration through vector manipulation. However, diffusion models like Stable Diffusion lack the intuitive latent vector control found in GANs, limiting their flexibility for artistic expression. This paper introduces \workname, a framework for integrating customizable latent space operations into the diffusion process. By enabling direct manipulation of conceptual and spatial representations, this approach expands creative possibilities in generative art. We demonstrate the potential of this framework through two artworks, \textit{Infinitepedia} and \textit{Latent Motion}, highlighting its use in conceptual blending and dynamic motion generation. Our findings reveal latent space structures with semantic and meaningless regions, offering insights into the geometry of diffusion models and paving the way for further explorations of latent space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22038 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Diffusion : Multi-Dimension Stable Diffusion Latent Space Explorer Zhong, Zhihua Huang, Xuanyang Machine Learning Artificial Intelligence Latent space is one of the key concepts in generative AI, offering powerful means for creative exploration through vector manipulation. However, diffusion models like Stable Diffusion lack the intuitive latent vector control found in GANs, limiting their flexibility for artistic expression. This paper introduces \workname, a framework for integrating customizable latent space operations into the diffusion process. By enabling direct manipulation of conceptual and spatial representations, this approach expands creative possibilities in generative art. We demonstrate the potential of this framework through two artworks, \textit{Infinitepedia} and \textit{Latent Motion}, highlighting its use in conceptual blending and dynamic motion generation. Our findings reveal latent space structures with semantic and meaningless regions, offering insights into the geometry of diffusion models and paving the way for further explorations of latent space. |
| title | Latent Diffusion : Multi-Dimension Stable Diffusion Latent Space Explorer |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.22038 |