Deep learning generative models have demonstrated great strengths in high quality synthetic images and various scientific tasks; diffusion models learn the target distribution via a forward Markov process by gradually adding Gaussian noise to the clean sample and reversing the Markov chain via a denoising process by a neural network-based noise estimation. In this study, we generate 3-dimensional cosmological dark matter simulation data and extract density fields using cloud-in-cell methods from various random seeds following the particle-mesh (PM) method, and subsequently use this dataset to train diffusion generative models of both unconditional and conditional nature on cosmological simulation snapshots taken from a certain redshift. We then compare their physical metrics such as power spectrum and density PDFs with ground truth to verify and benchmark the authenticity of such methods. The efficient …