Shypd.aiThe largest open index of AI toolsDiffusion-Probabilistic-Models is an open-source academic research tool that provides a reference implementation for deep unsupervised learning using nonequilibrium thermodynamics. It allows for the creation of generative models based on Gaussian diffusion processes.
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Diffusion-Probabilistic-Models offers a reference implementation for deep unsupervised learning, specifically focusing on methods described in the paper "Deep Unsupervised Learning using Nonequilibrium Thermodynamics." This tool enables the construction of generative models by training a Gaussian diffusion process to transform a noise distribution into a data distribution over a fixed number of time steps. The mean and covariance of this diffusion process are parameterized using deep supervised learning. The resulting models are designed to be tractable for training, easy to sample from, allow for efficient probability evaluation of data points, and facilitate straightforward computation of conditional and posterior distributions. It includes features for training on datasets like MNIST and CIFAR-10, outputting objective function values, and generating samples, inpaintings, and denoised images.
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