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This is keep-it-simple-and-stupid realization of Score-Based Generative Modeling through Stochastic Differential Equations.

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KISSDE

This is keep-it-simple-and-stupid realization of Score-Based Generative Modeling through Stochastic Differential Equations. The whole model is written in pure PyTorch and made as self-explanatory as possible.

Model parts

This realization contains a basic convolutional U-Net-like score approximation model and predictor-corrector as a sampler. The whole code is based on a different parts of the mentioned repo. Some major improvements like EMA of weights are implemented, leading to reproducing nearly SotA results on CIFAR10, while using tutorial-like architecture.

How to train

Firstly, build docker file with

docker build -t score_sde .

Then you can specify your wandb key in run.yaml and run training with crafting

crafting run.yaml

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This is keep-it-simple-and-stupid realization of Score-Based Generative Modeling through Stochastic Differential Equations.

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