Awesome

sdeflow-light

This is a minimalist codebase for training score-based diffusion models (supporting MNIST and CIFAR-10) used in the following paper

"A Variational Perspective on Diffusion-Based Generative Models and Score Matching" by Chin-Wei Huang, Jae Hyun Lim and Aaron Courville [arXiv]

Also see the concurrent work by Yang Song & Conor Durkan where they used the same idea to obtain state-of-the-art likelihood estimates.

Experiments on Swissroll

Here's a Colab notebook which contains an example for training a model on the Swissroll dataset.

In this notebook, you'll see how to train the model using score matching loss, how to evaluate the ELBO of the plug-in reverse SDE, and how to sample from it. It also includes a snippet to sample from a family of plug-in reverse SDEs (parameterized by λ) mentioned in Appendix C of the paper.

Below are the trajectories of λ=0 (the reverse SDE used in Song et al.) and λ=1 (equivalent ODE) when we plug in the learned score / drift function. This corresponds to Figure 5 of the paper. <img src="/assets/sdeflow_equivalent_sdes_lmbd0.png" alt="drawing" width="1000"/> <img src="/assets/sdeflow_equivalent_sdes_lmbd1.png" alt="drawing" width="1000"/>

Experiments on MNIST and CIFAR-10

This repository contains one main training loop (train_img.py). The model is trained to minimize the denoising score matching loss by calling the .dsm(x) loss function, and evaluated using the following ELBO, by calling .elbo_random_t_slice(x)

score-elbo

where the divergence (sum of the diagonal entries of the Jacobian) is estimated using the Hutchinson trace estimator.

It's a minimalist codebase in the sense that we do not use fancy optimizer (we only use Adam with the default setup) or learning rate scheduling. We use the modified U-net architecture from Denoising Diffusion Probabilistic Models by Jonathan Ho.

A key difference from Song et al. is that instead of parameterizing the score function s, here we parameterize the drift term a (where they are related by a=gs and g is the diffusion coefficient). That is, a is the U-net.

Parameterization: Our original generative & inference SDEs are

dX = mu dt + sigma dBt
dY = (-mu + sigma*a) ds + sigma dBs

We reparameterize it as

dX = (ga - f) dt + g dBt
dY = f ds + g dBs

by letting mu = ga - f, and sigma = g. (since f and g are fixed, we only have one degree of freedom, which is a). Alternatively, one can parameterize s (e.g. using the U-net), and just let a=gs.

How it works

Here's an example command line for running an experiment

python train_img.py --dataroot=[DATAROOT] --saveroot=[SAVEROOT] --expname=[EXPNAME] \
    --dataset=cifar --print_every=2000 --sample_every=2000 --checkpoint_every=2000 --num_steps=1000 \
    --batch_size=128 --lr=0.0001 --num_iterations=100000 --real=True --debias=False

Setting --debias to be False uses uniform sampling for the time variable, whereas setting it to be True uses a non-uniform sampling strategy to debias the gradient estimate described in the paper. Below are the bits-per-dim and the corresponding standard error of the test set recorded during training (<span style="color:orange;">orange</span> for --debias=True and <span style="color:blue;">blue</span> for --debias=False).

Here are some samples (debiased on the right)

It takes about 14 hrs to finish 100k iterations on a V100 GPU.