In our contemporary academic inquiry, we present "Diffusion-C," a
foundational methodology to analyze the generative restrictions of Diffusion
Models, particularly those akin to GANs, DDPM, and DDIM. By employing input
visual data that has been subjected to a myriad of corruption modalities and
intensities, we elucidate the performance characteristics of those Diffusion
Models. The noise component takes center stage in our analysis, hypothesized to
be a pivotal element influencing the mechanics of deep learning systems. In our
rigorous expedition utilizing Diffusion-C, we have discerned the following
critical observations: (I) Within the milieu of generative models under the
Diffusion taxonomy, DDPM emerges as a paragon, consistently exhibiting superior
performance metrics. (II) Within the vast spectrum of corruption frameworks,
the fog and fractal corruptions notably undermine the functional robustness of
both DDPM and DDIM. (III) The vulnerability of Diffusion Models to these
particular corruptions is significantly influenced by topological and
statistical similarities, particularly concerning the alignment between mean
and variance. This scholarly work highlights Diffusion-C's core understandings
regarding the impacts of various corruptions, setting the stage for future
research endeavors in the realm of generative models.

使用 Diffusion-C 方法分析 Diffusion Models 的生成限制，通过对经过各种腐败方式和强度处理的输入视觉数据的使用，阐明了这些 Diffusion Models 的性能特征，并探讨了影响深度学习系统机制的噪声成分的重要性。