/pytorch-ddpm

Unofficial PyTorch implementation of Denoising Diffusion Probabilistic Models

Primary LanguagePythonDo What The F*ck You Want To Public LicenseWTFPL

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