ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911621668929536 |
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| author | Hoang, Trong-Vu Nguyen, Quang-Binh Do, Thanh-Toan Nguyen, Tam V. Tran, Minh-Triet Le, Trung-Nghia |
| author_facet | Hoang, Trong-Vu Nguyen, Quang-Binh Do, Thanh-Toan Nguyen, Tam V. Tran, Minh-Triet Le, Trung-Nghia |
| contents | Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity preservation and prompt alignment is challenging. In multi-concept scenarios, relying solely on a prompt without additional conditions like layout boxes or semantic masks, often leads to identity loss and concept omission. In this paper, we introduce ShowFlow, a comprehensive framework designed to tackle these challenges. We propose ShowFlow-S for single-concept image generation, and ShowFlow-M for handling multiple concepts. ShowFlow-S introduces a KronA-WED adapter, which integrates a Kronecker adapter with weight and embedding decomposition, and together with a novel Semantic-Aware Attention Regularization (SAR) training objective to enhance single-concept generation. Building on this foundation, ShowFlow-M directly reuses robust models learned by ShowFlow-S to support multi-concept generation without extra conditions, incorporating a Subject-Adaptive Matching Attention (SAMA) and a Layout Consistency guidance as the plug-and-play module. Extensive experiments and user studies validate ShowFlow's effectiveness, highlighting its potential in real-world applications like advertising and virtual dressing. Our source code will be publicly available at: https://htrvu.github.io/showflow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation Hoang, Trong-Vu Nguyen, Quang-Binh Do, Thanh-Toan Nguyen, Tam V. Tran, Minh-Triet Le, Trung-Nghia Computer Vision and Pattern Recognition Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity preservation and prompt alignment is challenging. In multi-concept scenarios, relying solely on a prompt without additional conditions like layout boxes or semantic masks, often leads to identity loss and concept omission. In this paper, we introduce ShowFlow, a comprehensive framework designed to tackle these challenges. We propose ShowFlow-S for single-concept image generation, and ShowFlow-M for handling multiple concepts. ShowFlow-S introduces a KronA-WED adapter, which integrates a Kronecker adapter with weight and embedding decomposition, and together with a novel Semantic-Aware Attention Regularization (SAR) training objective to enhance single-concept generation. Building on this foundation, ShowFlow-M directly reuses robust models learned by ShowFlow-S to support multi-concept generation without extra conditions, incorporating a Subject-Adaptive Matching Attention (SAMA) and a Layout Consistency guidance as the plug-and-play module. Extensive experiments and user studies validate ShowFlow's effectiveness, highlighting its potential in real-world applications like advertising and virtual dressing. Our source code will be publicly available at: https://htrvu.github.io/showflow. |
| title | ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.18493 |