Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
<|reference_start|>Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints: 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.<|reference_end|>
@article{chen2024towards,
    title={Towards Aligned Layout Generation via Diffusion Model with Aesthetic
  Constraints},
    author={Jian Chen, Ruiyi Zhang, Yufan Zhou, Rajiv Jain, Zhiqiang Xu, Ryan
  Rossi, Changyou Chen},
    journal={arXiv preprint arXiv:2402.04754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04754},
    primaryClass={cs.CV cs.LG}
}