@article{denoisingdiffusionprobabilisticmodels2020,
  title = {Denoising Diffusion Probabilistic Models (DDPM)},
  author = {Ho, J. and Jain, A. and Abbeel, P.},
  year = {2020},
  journal = {NeurIPS 2020},
  url = {https://arxiv.org/abs/2006.11239},
  abstract = {Ho, Jain and Abbeel (2020) demonstrated that a neural network trained to reverse a fixed Markov noising chain can generate high-quality images by iteratively denoising Gaussian noise. The denoising score-matching objective gives DDPM a stable training regime compared to GANs, and the resulting sample quality matched or exceeded the state of the art. DDPM is the pixel-space origin of the entire diffusion stack: Stable Diffusion, AnimateDiff, Video LDM and the Motion Diffusion Model all build on its mathematical foundation. It is the paper courses name when introducing diffusion models to creative-AI students.},
  keywords = {generative-ai, image-generation},
  note = {AI \& Animation Education Knowledge Base}
}