{"type":"ScholarlyArticle","title":"MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models","authors":[{"name":"Donghao Zhou","slug":"donghao-zhou","given":"Donghao","family":"Zhou","affiliation":""},{"name":"Jiancheng Huang","slug":"jiancheng-huang","given":"Jiancheng","family":"Huang","affiliation":""},{"name":"Jinbin Bai","slug":"jinbin-bai","given":"Jinbin","family":"Bai","affiliation":""},{"name":"Jiaze Wang","slug":"jiaze-wang","given":"Jiaze","family":"Wang","affiliation":""},{"name":"Hao Chen","slug":"hao-chen","given":"Hao","family":"Chen","affiliation":""},{"name":"Guangyong Chen","slug":"guangyong-chen","given":"Guangyong","family":"Chen","affiliation":""},{"name":"Xiaowei Hu","slug":"xiaowei-hu","given":"Xiaowei","family":"Hu","affiliation":""},{"name":"Pheng-Ann Heng","slug":"pheng-ann-heng","given":"Pheng-Ann","family":"Heng","affiliation":""}],"abstract":"Text-to-image diffusion models can generate high-quality images but lack fine-grained control of visual concepts, limiting their creativity. Thus, we introduce component-controllable personalization, a new task that enables users to customize and reconfigure individual components within concepts. This task faces two challenges: semantic pollution, where undesired elements disrupt the target concept, and semantic imbalance, which causes disproportionate learning of the target concept and component. To address these, we design MagicTailor, a framework that uses Dynamic Masked Degradation to adaptively perturb unwanted visual semantics and Dual-Stream Balancing for more balanced learning of desired visual semantics. The experimental results show that MagicTailor achieves superior performance in this task and enables more personalized and creative image generation.","doi":"10.24963/ijcai.2025/1136","url":"https://ijcai.org/proceedings/2025/1136","pdf":"https://ijcai.org/proceedings/2025/1136.pdf","markdown":"https://ijcai.org/proceedings/2025/1136.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1136","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":1136,"pages":{"first":10225,"last":10233},"keywords":["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","Application domains: Images, movies and visual arts","Theory and philosophy of arts and creativity in AI systems: Computational paradigms, architectures and models for creativity","Methods and resources: AI systems for ideation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}