用加速采样技术让结构设计生成快100倍，质量不变。
DDIM sampling for Generative AIBIM, a faster intelligent structural design framework
- 将DDIM采样引入物理条件扩散模型，改进生成流程。
- 生成速度提升100倍，结果视觉质量保持一致。
- 简化理论说明，适合非机器学习背景的研究者使用。
生成式AIBIM是一种成功的结构设计流程，能智能生成高质量、多样化且富有创意的剪力墙设计方案，适应特定物理条件。然而，其当前的设计生成模块——基于物理的条件扩散模型（PCDM）——依赖于去噪扩散概率模型（DDPM）的采样过程，每生成一次需1000次迭代，导致生成过程耗时且计算成本高。为解决此问题，本文引入去噪扩散隐式模型（DDIM），作为替代方案加速PCDM的采样过程。尽管原始DDIM针对的是DDPM设计，而PCDM的优化过程与之不同，本文提出了适配PCDM优化流程的“针对PCDM的DDIM采样”方法，对原始公式进行调整。实验表明，该方法使原PCDM的生成速度提升100倍，同时保持生成结果的视觉质量。研究有效验证了该方法在加速智能结构设计中的有效性。此外，本文重新组织了DDIM内容，聚焦于实际应用，特别便于不具备深厚机器学习理论基础但希望高效使用该工具的研究者。
原文摘要 · Abstract (English)
Generative AIBIM, a successful structural design pipeline, has proven its ability to intelligently generate high-quality, diverse, and creative shear wall designs that are tailored to specific physical conditions. However, the current module of Generative AIBIM that generates designs, known as the physics-based conditional diffusion model (PCDM), necessitates 1000 iterations for each generation due to its reliance on the denoising diffusion probabilistic model (DDPM) sampling process. This leads to a time-consuming and computationally demanding generation process. To address this issue, this study introduces the denoising diffusion implicit model (DDIM), an accelerated generation method that replaces the DDPM sampling process in PCDM. While the original DDIM was designed for DDPM and the optimization process of PCDM differs from that of DDPM, this paper designs "DDIM sampling for PCDM," which modifies the original DDIM formulations to adapt to the optimization process of PCDM. Experimental results demonstrate that DDIM sampling for PCDM can accelerate the generation process of the original PCDM by a factor of 100 while maintaining the same visual quality in the generated results. This study effectively showcases the effectiveness of DDIM sampling for PCDM in expediting intelligent structural design. Furthermore, this paper reorganizes the contents of DDIM, focusing on the practical usage of DDIM. This change is particularly meaningful for researchers who may not possess a strong background in machine learning theory but are interested in utilizing the tool effectively.
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