Minimal Diffusion

This is a multi-gpu PyTorch implementation of Diffusion Models with Classifier-Free Guidance (CFG).

This repo contains:

Training, sampling and FID evaluation code for diffusion models, including
- Frameworks:
- DDPM.py: Unconditional DDPM & DDIM
- DDPM_guide.py: Class-conditional DDPM & DDIM with CFG sampling
- EDM.py: Unconditional EDM
- EDM_guide.py: Class-conditional EDM with CFG sampling
- Networks:
- unet.py & unet_guide.py: Unconditional & conditional U-Net (DDPM & DDPM++)
- uvit.py: Unconditional & conditional Vision Transformer (U-ViT)
- Datasets:
- CIFAR-10

Requirements

In addition to PyTorch environments, please install:

conda install pyyaml
pip install pytorch-fid ema-pytorch timm einops

At least 4 3080ti GPUs are recommended to train diffusion models on CIFAR-10. With automatic mixed precision enabled and 4 GPUs, training a basic 35.7M UNet on CIFAR-10 takes ~14 hours.
The pytorch-fid requires image files to calculate the FID metric. Please refer to extract_cifar10_pngs.ipynb to unpack the CIFAR-10 training dataset into 50000 .png image files.

Usage

Training

To train a diffusion model with 4 GPUs and AMP enabled, for example, run:

python -m torch.distributed.launch --nproc_per_node=4
  # unconditional
  cifar.py       --config config/cifar_unconditional.yaml --use_amp

  # conditional
  cifar_guide.py --config config/cifar_conditional.yaml   --use_amp

Sampling

To generate 50000 image samples with a trained diffusion model, for example, run:

python -m torch.distributed.launch --nproc_per_node=4
  # unconditional
  # deterministic fast sampling (i.e. DDIM 100 steps / EDM 18 steps)
  sample.py       --config config/cifar_unconditional.yaml --use_amp
  # stochastic sampling (i.e. DDPM 1000 steps)
  sample.py       --config config/cifar_unconditional.yaml --use_amp --mode DDPM

  # conditional
  # deterministic fast sampling (i.e. DDIM 100 steps / EDM 18 steps)
  sample_guide.py --config config/cifar_conditional.yaml   --use_amp
  # stochastic sampling (i.e. DDPM 1000 steps)
  sample_guide.py --config config/cifar_conditional.yaml   --use_amp --mode DDPM

Compute FID

To calculate the FID metric on the training set, for example, run:

python -m pytorch_fid   data/cifar10-pngs/  2000_unconditional/EMAgenerated_ep1999_ddpm/pngs/

Results

Config	Model	Network	Cond	FID (best sampler/NFE)	FID (fast sampler/NFE)
`cifar_unconditional.yaml`	DDPM	35.7M UNet	no	3.03 (DDPM / 1000)	3.54 (DDIM / 100)
`cifar_unconditional_cosine.yaml`	DDPM (cosine)	35.7M UNet	no	3.18 (DDPM / 1000)	3.56 (DDIM / 100)
`cifar_conditional.yaml`	DDPM	38.6M UNet	yes	3.27 (DDPM / 2000)	3.46 (DDIM / 200)
`cifar_unconditional_vit.yaml`	DDPM	44.3M UViT	no	3.33 (DDPM / 1000)	4.51 (DDIM / 100)
`cifar_conditional_vit.yaml`	DDPM	44.3M UViT	yes	2.87 (DDPM / 2000)	3.19 (DDIM / 200)
`cifar_unconditional_EDM.yaml`	EDM	56.5M UNet	no	2.22 (EDM / 35)	2.22 (EDM / 35)
`cifar_conditional_EDM.yaml`	EDM	61.1M UNet	yes	2.06 (EDM / 53)	2.06 (EDM / 53)
N/A	EDM	44.3M UViT	no	NOT WORKING	NOT WORKING

Acknowledgments

This repository is built on numerous open-source codebases such as DDPM, DDPM-pytorch, DDIM, EDM, ADM, U-ViT, and CFG-MNIST.

FutureXiang / Diffusion

readme