Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866917667547381760 |
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| author | Chen, Jian Zhang, Ruiyi Zhou, Yufan Jain, Rajiv Xu, Zhiqiang Rossi, Ryan Chen, Changyou |
| author_facet | Chen, Jian Zhang, Ruiyi Zhou, Yufan Jain, Rajiv Xu, Zhiqiang Rossi, Ryan Chen, Changyou |
| contents | Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraints representing design intentions. Although recent diffusion-based models have achieved state-of-the-art FID scores, they tend to exhibit more pronounced misalignment compared to earlier transformer-based models. In this work, we propose the $\textbf{LA}$yout $\textbf{C}$onstraint diffusion mod$\textbf{E}$l (LACE), a unified model to handle a broad range of layout generation tasks, such as arranging elements with specified attributes and refining or completing a coarse layout design. The model is based on continuous diffusion models. Compared with existing methods that use discrete diffusion models, continuous state-space design can enable the incorporation of differentiable aesthetic constraint functions in training. For conditional generation, we introduce conditions via masked input. Extensive experiment results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04754 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints Chen, Jian Zhang, Ruiyi Zhou, Yufan Jain, Rajiv Xu, Zhiqiang Rossi, Ryan Chen, Changyou Computer Vision and Pattern Recognition Machine Learning Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraints representing design intentions. Although recent diffusion-based models have achieved state-of-the-art FID scores, they tend to exhibit more pronounced misalignment compared to earlier transformer-based models. In this work, we propose the $\textbf{LA}$yout $\textbf{C}$onstraint diffusion mod$\textbf{E}$l (LACE), a unified model to handle a broad range of layout generation tasks, such as arranging elements with specified attributes and refining or completing a coarse layout design. The model is based on continuous diffusion models. Compared with existing methods that use discrete diffusion models, continuous state-space design can enable the incorporation of differentiable aesthetic constraint functions in training. For conditional generation, we introduce conditions via masked input. Extensive experiment results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines. |
| title | Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.04754 |