CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917373785669632 |
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| author | Zhang, Hui Hong, Dexiang Yang, Maoke Cheng, Yutao Zhang, Zhao Chen, Weidong Shao, Jie Wu, Xinglong Wu, Zuxuan Jiang, Yu-Gang |
| author_facet | Zhang, Hui Hong, Dexiang Yang, Maoke Cheng, Yutao Zhang, Zhao Chen, Weidong Shao, Jie Wu, Xinglong Wu, Zuxuan Jiang, Yu-Gang |
| contents | Graphic design plays a vital role in visual communication across advertising, marketing, and multimedia entertainment. Prior work has explored automated graphic design generation using diffusion models, aiming to streamline creative workflows and democratize design capabilities. However, complex graphic design scenarios require accurately adhering to design intent specified by multiple heterogeneous user-provided elements (\eg images, layouts, and texts), which pose multi-condition control challenges for existing methods. Specifically, previous single-condition control models demonstrate effectiveness only within their specialized domains but fail to generalize to other conditions, while existing multi-condition methods often lack fine-grained control over each sub-condition and compromise overall compositional harmony. To address these limitations, we introduce CreatiDesign, a systematic solution for automated graphic design covering both model architecture and dataset construction. First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements with minimal architectural modifications to the base diffusion model. Furthermore, to ensure that each condition precisely controls its designated image region and to avoid interference between conditions, we propose a multimodal attention mask mechanism. Additionally, we develop a fully automated pipeline for constructing graphic design datasets, and introduce a new dataset with 400K samples featuring multi-condition annotations, along with a comprehensive benchmark. Experimental results show that CreatiDesign outperforms existing models by a clear margin in faithfully adhering to user intent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design Zhang, Hui Hong, Dexiang Yang, Maoke Cheng, Yutao Zhang, Zhao Chen, Weidong Shao, Jie Wu, Xinglong Wu, Zuxuan Jiang, Yu-Gang Computer Vision and Pattern Recognition Graphic design plays a vital role in visual communication across advertising, marketing, and multimedia entertainment. Prior work has explored automated graphic design generation using diffusion models, aiming to streamline creative workflows and democratize design capabilities. However, complex graphic design scenarios require accurately adhering to design intent specified by multiple heterogeneous user-provided elements (\eg images, layouts, and texts), which pose multi-condition control challenges for existing methods. Specifically, previous single-condition control models demonstrate effectiveness only within their specialized domains but fail to generalize to other conditions, while existing multi-condition methods often lack fine-grained control over each sub-condition and compromise overall compositional harmony. To address these limitations, we introduce CreatiDesign, a systematic solution for automated graphic design covering both model architecture and dataset construction. First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements with minimal architectural modifications to the base diffusion model. Furthermore, to ensure that each condition precisely controls its designated image region and to avoid interference between conditions, we propose a multimodal attention mask mechanism. Additionally, we develop a fully automated pipeline for constructing graphic design datasets, and introduce a new dataset with 400K samples featuring multi-condition annotations, along with a comprehensive benchmark. Experimental results show that CreatiDesign outperforms existing models by a clear margin in faithfully adhering to user intent. |
| title | CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.19114 |