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}
${dataset_name}:cifar10,celeba64,lsun_bedroom,lsun_church, orlsun_cat${approximate_diffusion_process}:VARorSTEP${kappa}: a real value between 0 and 1${FastDPM_length}: an integer between 1 and 1000; 10, 20, 50, 100 used in paper.${noise_level_schedule}:linearorquadratic
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
172 Dec 23, 2022
2.9k Jan 4, 2023
11 Oct 14, 2022
123 Jan 1, 2023
1 May 18, 2022
3k Dec 29, 2022
22 Sep 8, 2022
152 Jan 2, 2023
16 Oct 6, 2022
55 Jan 1, 2023
103 Dec 23, 2022
7 Dec 15, 2022
225 Dec 28, 2022
3.7k Jan 3, 2023
4 Sep 13, 2022
38 Oct 11, 2022
242 Dec 23, 2022