{"type":"ScholarlyArticle","title":"Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt","authors":[{"name":"Zhanjie Zhang","slug":"zhanjie-zhang","given":"Zhanjie","family":"Zhang","affiliation":"Zhejiang University"},{"name":"Quanwei Zhang","slug":"quanwei-zhang","given":"Quanwei","family":"Zhang","affiliation":"Zhejiang University"},{"name":"Huaizhong Lin","slug":"huaizhong-lin","given":"Huaizhong","family":"Lin","affiliation":"Zhejiang University"},{"name":"Wei Xing","slug":"wei-xing","given":"Wei","family":"Xing","affiliation":"Zhejiang University"},{"name":"Juncheng Mo","slug":"juncheng-mo","given":"Juncheng","family":"Mo","affiliation":"Zhejiang University"},{"name":"Shuaicheng Huang","slug":"shuaicheng-huang","given":"Shuaicheng","family":"Huang","affiliation":"Anhui University"},{"name":"Jinheng Xie","slug":"jinheng-xie","given":"Jinheng","family":"Xie","affiliation":"National University of Singapore"},{"name":"Guangyuan Li","slug":"guangyuan-li","given":"Guangyuan","family":"Li","affiliation":"Zhejiang University"},{"name":"Junsheng Luan","slug":"junsheng-luan","given":"Junsheng","family":"Luan","affiliation":"Zhejiang University"},{"name":"Lei Zhao","slug":"lei-zhao","given":"Lei","family":"Zhao","affiliation":"Zhejiang University"},{"name":"Dalong Zhang","slug":"dalong-zhang","given":"Dalong","family":"Zhang","affiliation":"Zhejiang University"},{"name":"Lixia Chen","slug":"lixia-chen","given":"Lixia","family":"Chen","affiliation":"Zhejiang Gongshang University {cszzj , cszqw ,"}],"abstract":"Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern.\r\n\tTo train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection.\r\n\tIn addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure.\r\n\tExtensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods. Code is available at https://github.com/Jamie-Cheung/LSAST.","doi":"10.24963/ijcai.2024/865","url":"https://ijcai.org/proceedings/2024/865","pdf":"https://ijcai.org/proceedings/2024/0865.pdf","markdown":"https://ijcai.org/proceedings/2024/865.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/865","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":865,"pages":{"first":7814,"last":7822},"keywords":["Application domains: Images, movies and visual arts","Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Methods and resources: AI methods for better understanding human creative processes"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2023] Aishwarya Agarwal, Srikrishna Karanam, Tripti Shukla, and Balaji Vasan Srinivasan. 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