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.

翻译：摘要：在本次学术研究中，我们提出了“Diffusion-C”这一基础性方法，用于分析扩散模型（尤其是类似于GAN、DDPM和DDIM的模型）在生成方面的局限性。通过采用经过多种损坏模式与强度处理的输入视觉数据，我们阐明了这些扩散模型的性能特征。噪声成分在我们分析中占据核心地位，被假设为影响深度学习系统机制的关键要素。在利用Diffusion-C进行的严格探索中，我们观察到以下重要发现：（I）在扩散分类下的生成模型环境中，DDPM表现卓越，持续展现出更优的性能指标。（II）在众多损坏框架中，雾化和分形损坏显著削弱了DDPM和DDIM的功能鲁棒性。（III）扩散模型对这些特定损坏的脆弱性受到拓扑和统计相似性的显著影响，特别是关于均值与方差的一致性。本学术工作凸显了Diffusion-C对各种损坏影响的核心理解，为生成模型领域的未来研究奠定了基础。