Denoising Diffusion Probabilistic Models

Unofficial PyTorch implementation of Denoising Diffusion Probabilistic Models [1].

This implementation follows the most of details in official TensorFlow implementation [2]. I use PyTorch coding style to port [2] to PyTorch and hope that anyone who is familiar with PyTorch can easily understand every implementation details.

TODO

Datasets
- Support CIFAR10
- Support LSUN
- Support CelebA-HQ
Featurex
- Gradient accumulation
- Multi-GPU training
Reproducing Experiment
- CIFAR10

Requirements

Python 3.6

Packages Upgrade pip for installing latest tensorboard

pip install -U pip setuptools
pip install -r requirements.txt

Download precalculated statistic for dataset:

cifar10.train.npz

Create folder stats for cifar10.train.npz.
```
stats
└── cifar10.train.npz
```

Train From Scratch

Take CIFAR10 for example:

python main.py --train \
    --flagfile ./config/CIFAR10.txt

[Optional] Overwrite arguments

python main.py --train \
    --flagfile ./config/CIFAR10.txt \
    --batch_size 64 \
    --logdir ./path/to/logdir

[Optional] Select GPU IDs

CUDA_VISIBLE_DEVICES=1 python main.py --train \
    --flagfile ./config/CIFAR10.txt

[Optional] Multi-GPU training

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --train \
    --flagfile ./config/CIFAR10.txt \
    --parallel

Evaluate

A flagfile.txt is autosaved to your log directory. The default logdir for config/CIFAR10.txt is ./logs/DDPM_CIFAR10_EPS

Start evaluation

python main.py \
    --flagfile ./logs/DDPM_CIFAR10_EPS/flagfile.txt \
    --notrain \
    --eval

[Optional] Multi-GPU evaluation

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py \
    --flagfile ./logs/DDPM_CIFAR10_EPS/flagfile.txt \
    --notrain \
    --eval \
    --parallel

Reproducing Experiment

CIFAR10

FID: 3.249, Inception Score: 9.475(0.174)

The checkpoint can be downloaded from my drive.

Reference

[1] Denoising Diffusion Probabilistic Models

[2] Official TensorFlow implementation

w86763777/pytorch-ddpm