For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model and mathematical physics. Despite previous efforts, progress has been limited to the pursuit of a single specialized method. In order to advance the interpretability of diffusion models and explore new research directions, it is essential to establish a unified ODE-style generative diffusion model. Such a model should draw inspiration from physical models and possess a clear geometric meaning. This paper aims to identify various physical models that are suitable for constructing ODE-style generative diffusion models accurately from a mathematical perspective. We then summarize these models into a unified method. Additionally, we perform a case study where we use the theoretical model identified by our method to develop a range of new diffusion model methods, and conduct experiments. Our experiments on CIFAR-10 demonstrate the effectiveness of our approach. We have constructed a computational framework that attains highly proficient results with regards to image generation speed, alongside an additional model that demonstrates exceptional performance in both Inception score and FID score. These results underscore the significance of our method in advancing the field of diffusion models.
翻译:相当长一段时间以来,研究人员一直着眼于建立生成性扩散模型和数学物理之间的深度联系的方法。尽管之前已经做出了努力,但进展受限于追求单一专业方法。为了提升扩散模型的可解释性并探索新的研究方向,建立一个统一的ODE风格生成性扩散模型至关重要。这样的模型应该从物理模型中汲取灵感并具有清晰的几何意义。本文旨在鉴定适用于从数学角度精确构建ODE风格生成性扩散模型的各种物理模型,并将这些模型归纳为统一的方法。此外,我们进行了一个案例研究,使用我们的方法确定的理论模型开发出一系列新的扩散模型方法,并进行实验。我们在CIFAR-10上的实验说明了我们方法的有效性。我们构建了一个计算框架,在图像生成速度方面取得了高度高效的结果,以及一个在Inception分数和FID分数方面表现卓越的额外模型。这些结果突显了我们方法在推进扩散模型领域方面的重要性。