Unofficial Implementation of Classifier-free Diffusion Guidance
The Pytorch implementation is adapted from openai/guided-diffusion with modifications for classifier-free conditioned generation. The dataset used for training is cifar-10. The platform I use is Ubuntu 20.04.
During the training porcess, I use the file Scheduler.py from zoubohao/DenoisingDiffusionProbabilityModel-ddpm-, which is different from my implementation mainly on one technical detail : How to represent the embeddings of null token of class identifier. According to another academic paper Video Diffusion Models by the same authors, I use the same method as theirs in this detail.
First, you need to do some preparations:
mkdir sample
mkdir model
ln -s absolute/path/to/cifar-10 ./cifar-10-batches-py
make train
NOTICE : hyperparameter settings are in the file train.py
make sample
NOTICE : hyperparameter settings are in the file sample.py