Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models".

FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. See paper via this link.

Pretrained models

Download checkpoints from this link and this link. Put them under checkpoints\ema_diffusion_${dataset_name}_model\model.ckpt, where ${dataset_name} is cifar10, celeba64, lsun_bedroom, lsun_church, or lsun_cat.

Usage

General command: python generate.py -ema -name ${dataset_name} -approxdiff ${approximate_diffusion_process} -kappa ${kappa} -S ${FastDPM_length} -schedule ${noise_level_schedule} -n ${number_to_generate} -bs ${batchsize} -gpu ${gpu_index}

• ${dataset_name}: cifar10, celeba64, lsun_bedroom, lsun_church, or lsun_cat
• ${approximate_diffusion_process}: VAR or STEP
• ${kappa}: a real value between 0 and 1
• ${FastDPM_length}: an integer between 1 and 1000; 10, 20, 50, 100 used in paper.
• ${noise_level_schedule}: linear or quadratic

CIFAR-10

Below are commands to generate CIFAR-10 images.

• Standard DDPM generation: python generate.py -ema -name cifar10 -approxdiff STD -n 16 -bs 16
• FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name cifar10 -approxdiff STEP -kappa 1.0 -S 50 -schedule quadratic -n 16 -bs 16
• FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name cifar10 -approxdiff STEP -kappa 0.0 -S 50 -schedule quadratic -n 16 -bs 16
• FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name cifar10 -approxdiff VAR -kappa 1.0 -S 50 -schedule quadratic -n 16 -bs 16
• FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name cifar10 -approxdiff VAR -kappa 0.0 -S 50 -schedule quadratic -n 16 -bs 16

CelebA

Below are commands to generate CelebA images.

• Standard DDPM generation: python generate.py -ema -name celeba64 -approxdiff STD -n 16 -bs 16
• FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name celeba64 -approxdiff STEP -kappa 1.0 -S 50 -schedule linear -n 16 -bs 16
• FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name celeba64 -approxdiff STEP -kappa 0.0 -S 50 -schedule linear -n 16 -bs 16
• FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name celeba64 -approxdiff VAR -kappa 1.0 -S 50 -schedule linear -n 16 -bs 16
• FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name celeba64 -approxdiff VAR -kappa 0.0 -S 50 -schedule linear -n 16 -bs 16

LSUN_bedroom

Below are commands to generate LSUN bedroom images.

• Standard DDPM generation: python generate.py -ema -name lsun_bedroom -approxdiff STD -n 8 -bs 8
• FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name lsun_bedroom -approxdiff STEP -kappa 1.0 -S 50 -schedule linear -n 8 -bs 8
• FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name lsun_bedroom -approxdiff STEP -kappa 0.0 -S 50 -schedule linear -n 8 -bs 8
• FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name lsun_bedroom -approxdiff VAR -kappa 1.0 -S 50 -schedule linear -n 8 -bs 8
• FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name lsun_bedroom -approxdiff VAR -kappa 0.0 -S 50 -schedule linear -n 8 -bs 8

Note

To generate 50K samples, set -n 50000 and batchsize (-bs) divisible by 50K.

Compute FID

To compute FID of generated samples, first make sure there are 50K images, and then run

• python FID.py -ema -name cifar10 -approxdiff STEP -kappa 1.0 -S 50 -schedule quadratic

Code References

• DDPM TensorFlow official
• DDPM PyTorch
• DDPM CelebA-HQ
• DDIM PyTorch
• FID PyTorch
• DiffWave PyTorch 1
• DiffWave PyTorch 2
• DiffWave PyTorch 3