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