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Graph."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Personalized generation paradigms empower designers to customize visual intellectual property with the help of textual descriptions by adapting pre-trained text-to-image models on a few images. Recent studies focus on simultaneously customizing content and detailed visual style in images but often struggle with entangling the two. In this study, we reconsider the customization of content and style concepts from the perspective of parameter space construction. Unlike existing methods that utilize a shared parameter space for content and style learning, we propose a novel framework that separates the parameter space to facilitate individual learning of content and style by introducing \u201cpartly learnable projection\u201d (PLP) matrices to separate the original adapters into divided sub-parameter spaces. A \u201c\n            <jats:bold>break-for-make<\/jats:bold>\n            \u201d customization learning pipeline based on PLP is proposed: we first\n            <jats:bold>break<\/jats:bold>\n            the original adapters into \u201cup projection\u201d and \u201cdown projection\u201d for content and style concept under orthogonal prior and then\n            <jats:bold>make<\/jats:bold>\n            the entity parameter space by reconstructing the content and style PLP matrices by using Riemannian preconditioning to adaptively balance content and style learning. Experiments on various styles, including textures, materials, and artistic style, show that our method outperforms state-of-the-art single\/multiple concept learning pipelines regarding content-style-prompt alignment. Code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/ICTMCG\/Break-for-make\">https:\/\/github.com\/ICTMCG\/Break-for-make<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3728461","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T10:55:16Z","timestamp":1743850516000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["B4M: Breaking Low-Rank Adapter for Making Content-Style Customization"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3459-5455","authenticated-orcid":false,"given":"Yu","family":"Xu","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of the Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-2483","authenticated-orcid":false,"given":"Fan","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7857-1546","authenticated-orcid":false,"given":"Juan","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6433-2678","authenticated-orcid":false,"given":"Yuxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5803-2185","authenticated-orcid":false,"given":"Oliver","family":"Deussen","sequence":"additional","affiliation":[{"name":"University of Konstanz, Konstanz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7399-7011","authenticated-orcid":false,"given":"Weiming","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-8534","authenticated-orcid":false,"given":"Jintao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6699-2944","authenticated-orcid":false,"given":"Tong-Yee","family":"Lee","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618322"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618154"},{"key":"e_1_3_3_4_1","first-page":"3","article-title":"Improving image generation with better captions","volume":"2","author":"Betker James","year":"2023","unstructured":"James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et\u00a0al. 2023. Improving image generation with better captions. Computer Science. https:\/\/cdn. openai. com\/papers\/dall-e-3. pdf 2 (2023), 3.","journal-title":"Computer Science. https:\/\/cdn. openai. com\/papers\/dall-e-3. pdf"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"e_1_3_3_6_1","first-page":"4055","volume-title":"Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"202","author":"Chang Huiwen","year":"2023","unstructured":"Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, et al.2023. Muse: Text-to-image generation via masked generative transformers. 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