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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01926 |
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| _version_ | 1866918152053456896 |
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| author | Li, Yaowei Li, Xiaoyu Zhang, Zhaoyang Bian, Yuxuan Liu, Gan Li, Xinyuan Xu, Jiale Hu, Wenbo Liu, Yating Li, Lingen Cai, Jing Zou, Yuexian He, Yancheng Shan, Ying |
| author_facet | Li, Yaowei Li, Xiaoyu Zhang, Zhaoyang Bian, Yuxuan Liu, Gan Li, Xinyuan Xu, Jiale Hu, Wenbo Liu, Yating Li, Lingen Cai, Jing Zou, Yuexian He, Yancheng Shan, Ying |
| contents | Image customization, a crucial technique for industrial media production, aims to generate content that is consistent with reference images. However, current approaches conventionally separate image customization into position-aware and position-free customization paradigms and lack a universal framework for diverse customization, limiting their applications across various scenarios. To overcome these limitations, we propose IC-Custom, a unified framework that seamlessly integrates position-aware and position-free image customization through in-context learning. IC-Custom concatenates reference images with target images to a polyptych, leveraging DiT's multi-modal attention mechanism for fine-grained token-level interactions. We propose the In-context Multi-Modal Attention (ICMA) mechanism, which employs learnable task-oriented register tokens and boundary-aware positional embeddings to enable the model to effectively handle diverse tasks and distinguish between inputs in polyptych configurations. To address the data gap, we curated a 12K identity-consistent dataset with 8K real-world and 4K high-quality synthetic samples, avoiding the overly glossy, oversaturated look typical of synthetic data. IC-Custom supports various industrial applications, including try-on, image insertion, and creative IP customization. Extensive evaluations on our proposed ProductBench and the publicly available DreamBench demonstrate that IC-Custom significantly outperforms community workflows, closed-source models, and state-of-the-art open-source approaches. IC-Custom achieves about 73\% higher human preference across identity consistency, harmony, and text alignment metrics, while training only 0.4\% of the original model parameters. Project page: https://liyaowei-stu.github.io/project/IC_Custom |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IC-Custom: Diverse Image Customization via In-Context Learning Li, Yaowei Li, Xiaoyu Zhang, Zhaoyang Bian, Yuxuan Liu, Gan Li, Xinyuan Xu, Jiale Hu, Wenbo Liu, Yating Li, Lingen Cai, Jing Zou, Yuexian He, Yancheng Shan, Ying Computer Vision and Pattern Recognition Image customization, a crucial technique for industrial media production, aims to generate content that is consistent with reference images. However, current approaches conventionally separate image customization into position-aware and position-free customization paradigms and lack a universal framework for diverse customization, limiting their applications across various scenarios. To overcome these limitations, we propose IC-Custom, a unified framework that seamlessly integrates position-aware and position-free image customization through in-context learning. IC-Custom concatenates reference images with target images to a polyptych, leveraging DiT's multi-modal attention mechanism for fine-grained token-level interactions. We propose the In-context Multi-Modal Attention (ICMA) mechanism, which employs learnable task-oriented register tokens and boundary-aware positional embeddings to enable the model to effectively handle diverse tasks and distinguish between inputs in polyptych configurations. To address the data gap, we curated a 12K identity-consistent dataset with 8K real-world and 4K high-quality synthetic samples, avoiding the overly glossy, oversaturated look typical of synthetic data. IC-Custom supports various industrial applications, including try-on, image insertion, and creative IP customization. Extensive evaluations on our proposed ProductBench and the publicly available DreamBench demonstrate that IC-Custom significantly outperforms community workflows, closed-source models, and state-of-the-art open-source approaches. IC-Custom achieves about 73\% higher human preference across identity consistency, harmony, and text alignment metrics, while training only 0.4\% of the original model parameters. Project page: https://liyaowei-stu.github.io/project/IC_Custom |
| title | IC-Custom: Diverse Image Customization via In-Context Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.01926 |