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denoising-diffusion-probabilistic-models

unofficial implementation. Pytorch version

diffusion-models

Deep generative models of all kinds have recently exhibited high quality samples in a wide variety of data modalities. Generative adversarial networks (GANs), autoregressive models, flows, and variational autoencoders (VAEs) have synthesized striking image and audio samples and there have been remarkable advances in energy-based modeling and score matching that have produced images comparable to those of GANs. This paper presents progress in diffusion probabilistic models. A diffusion probabilistic model (which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using variational inference to produce samples matching the data after finite time. Transitions of this chain are learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the data in the opposite direction of sampling until signal is destroyed. When the diffusion consists of small amounts of Gaussian noise, it is sufficient to set the sampling chain transitions to conditional Gaussians too, allowing for a particularly simple neural network parameterization. Diffusion models are straightforward to define and efficient to train, but to the best of our knowledge, there has been no demonstration that they are capable of generating high quality samples. We show that diffusion models actually are capable of generating high quality samples, sometimes better than the published results on other types of generative models This work was done with the help of an article on diffusion models. More information about this topic can be found here arxiv.org/pdf/2006.11239.pdf

diffusion-mri

Diffusion model for MRI image generation

diffusion_priors

Using pre-trained Diffusion models as priors for inference tasks

mindiffusion

Self-contained, minimalistic implementation of diffusion models with Pytorch.

papersreimplementations

Personal implementations of Machine Learning papers

scorediffusionmodel

The Pytorch Tutorial of Score-based and Diffusion Model

simlr_py

Python Implementation of SIMLR for single-cell visualization and analysis

stable-diffusion

tasks