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Research on AI Painting Generation Technology Based on the [Stable Diffusion]
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chenghao Wang | - |
| dc.contributor.author | 정진헌 | - |
| dc.date.accessioned | 2024-08-08T08:01:31Z | - |
| dc.date.available | 2024-08-08T08:01:31Z | - |
| dc.date.issued | 2023-06 | - |
| dc.identifier.issn | 2288-2847 | - |
| dc.identifier.issn | 2288-2855 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/20174 | - |
| dc.description.abstract | With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design. | - |
| dc.format.extent | 6 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | 한국인터넷방송통신학회 | - |
| dc.title | Research on AI Painting Generation Technology Based on the [Stable Diffusion] | - |
| dc.title.alternative | Research on AI Painting Generation Technology Based on the [Stable Diffusion] | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.doi | 10.7236/IJASC.2023.12.2.90 | - |
| dc.identifier.bibliographicCitation | The International Journal of Advanced Smart Convergence, v.12, no.2, pp 90 - 95 | - |
| dc.citation.title | The International Journal of Advanced Smart Convergence | - |
| dc.citation.volume | 12 | - |
| dc.citation.number | 2 | - |
| dc.citation.startPage | 90 | - |
| dc.citation.endPage | 95 | - |
| dc.identifier.kciid | ART002969407 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | AI painting | - |
| dc.subject.keywordAuthor | Artificial Intelligence | - |
| dc.subject.keywordAuthor | Stable Diffusion | - |
| dc.subject.keywordAuthor | Checkpoint | - |
| dc.subject.keywordAuthor | LoAR | - |
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