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A Research on Aesthetic Aspects of Checkpoint Models in [Stable Diffusion]

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dc.contributor.author마카-
dc.contributor.author정진헌-
dc.date.accessioned2024-08-08T13:01:04Z-
dc.date.available2024-08-08T13:01:04Z-
dc.date.issued2024-06-
dc.identifier.issn2288-2847-
dc.identifier.issn2288-2855-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/22386-
dc.description.abstractThe Stable diffsuion AI tool is popular among designers because of its flexible and powerful image generation capabilities. However, due to the diversity of its AI models, it needs to spend a lot of time testing different AI models in the face of different design plans, so choosing a suitable general AI model has become a big problem at present. In this paper, by comparing the AI images generated by two different Stable diffsuion models, the advantages and disadvantages of each model are analyzed from the aspects of the matching degree of the AI image and the prompt, the color composition and light composition of the image, and the general AI model that the generated AI image has an aesthetic sense is analyzed, and the designer does not need to take cumbersome steps. A satisfactory AI image can be obtained. The results show that Playground V2.5 model can be used as a general AI model, which has both aesthetic and design sense in various style design requirements. As a result, content designers can focus more on creative content development, and expect more groundbreaking technologies to merge generative AI with content design.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisher한국인터넷방송통신학회-
dc.titleA Research on Aesthetic Aspects of Checkpoint Models in [Stable Diffusion]-
dc.title.alternativeA Research on Aesthetic Aspects of Checkpoint Models in [Stable Diffusion]-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.7236/IJASC.2024.13.2.130-
dc.identifier.bibliographicCitationInternational Journal of Advanced Smart Convergence, v.13, no.2, pp 130 - 135-
dc.citation.titleInternational Journal of Advanced Smart Convergence-
dc.citation.volume13-
dc.citation.number2-
dc.citation.startPage130-
dc.citation.endPage135-
dc.identifier.kciidART003096160-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorAI Generated Technology-
dc.subject.keywordAuthorAI Painting-
dc.subject.keywordAuthorGenerative Artificial Intelligence-
dc.subject.keywordAuthor3D Content Design, Stable Diffsuion,Checkpoint Models-
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