Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916119244177408 |
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| author | Zhou, Aven-Le Wang, Yu-Ao Wu, Wei Zhang, Kang |
| author_facet | Zhou, Aven-Le Wang, Yu-Ao Wu, Wei Zhang, Kang |
| contents | With the advancement of neural generative capabilities, the art community has actively embraced GenAI (generative artificial intelligence) for creating painterly content. Large text-to-image models can quickly generate aesthetically pleasing outcomes. However, the process can be non-deterministic and often involves tedious trial-and-error, as users struggle with formulating effective prompts to achieve their desired results. This paper introduces a prompting-free generative approach that empowers users to automatically generate personalized painterly content that incorporates their aesthetic preferences in a customized artistic style. This approach involves utilizing ``semantic injection'' to customize an artist model in a specific artistic style, and further leveraging a genetic algorithm to optimize the prompt generation process through real-time iterative human feedback. By solely relying on the user's aesthetic evaluation and preference for the artist model-generated images, this approach creates the user a personalized model that encompasses their aesthetic preferences and the customized artistic style. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06389 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example Zhou, Aven-Le Wang, Yu-Ao Wu, Wei Zhang, Kang Artificial Intelligence Human-Computer Interaction Multimedia With the advancement of neural generative capabilities, the art community has actively embraced GenAI (generative artificial intelligence) for creating painterly content. Large text-to-image models can quickly generate aesthetically pleasing outcomes. However, the process can be non-deterministic and often involves tedious trial-and-error, as users struggle with formulating effective prompts to achieve their desired results. This paper introduces a prompting-free generative approach that empowers users to automatically generate personalized painterly content that incorporates their aesthetic preferences in a customized artistic style. This approach involves utilizing ``semantic injection'' to customize an artist model in a specific artistic style, and further leveraging a genetic algorithm to optimize the prompt generation process through real-time iterative human feedback. By solely relying on the user's aesthetic evaluation and preference for the artist model-generated images, this approach creates the user a personalized model that encompasses their aesthetic preferences and the customized artistic style. |
| title | Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example |
| topic | Artificial Intelligence Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2402.06389 |