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On Calibrating Diffusion Probabilistic Models
The official code for the paper On Calibrating Diffusion Probabilistic Models.
We propose a straightforward method for calibrating diffusion probabilistic models that reduces the values of SM objectives and increases model likelihood lower bounds.
Acknowledgement
The codes are modifed based on the DPM-solver and EDM.
Reproducing CIFAR-10 results on image generation and FID
The command for computing the FID of baseline methods (without calibration):
python main.py --config cifar10.yml \
--exp=experiments/cifar10 \
--sample --fid \
--timesteps=20 \
--eta 0 --ni \
--skip_type=logSNR \
--sample_type=dpm_solver \
--start_time=1e-4 \
--dpm_solver_fast -i baseline
The command for computing the FID of our methods (with calibration):
python main.py --config cifar10.yml \
--exp=experiments/cifar10 \
--sample --fid \
--timesteps=20 \
--eta 0 --ni \
--skip_type=logSNR \
--sample_type=dpm_solver \
--start_time=1e-4 \
--dpm_solver_fast -i our --score_mean
Reproducing CelebA results on image generation and FID
The command for computing the FID of baseline methods (without calibration):
python main.py --config celeba.yml \
--exp=experiments/celeba \
--sample --fid \
--timesteps=50 \
--eta 0 --ni \
--skip_type=logSNR \
--sample_type=dpm_solver \
--start_time=1e-4 \
--dpm_solver_fast -i baseline
The command for computing the FID of our methods (with calibration):
python main.py --config celeba.yml \
--exp=experiments/celeba \
--sample --fid \
--timesteps=50 \
--eta 0 --ni \
--skip_type=logSNR \
--sample_type=dpm_solver \
--start_time=1e-4 \
--dpm_solver_fast -i our --score_mean
Estimating SDE likelihood
The command for running on CIFAR-10:
python main.py --config cifar10.yml \
--exp=experiments/cifar10 \
--sample --eta 0 \
--ni --start_time=1e-4 \
-i temp --likelihood sde
The command for running on CelebA:
python main.py --config celeba.yml \
--exp=experiments/celeba \
--sample --eta 0 \
--ni --start_time=1e-4 \
-i temp --likelihood sde
Estimating the average estimated score with EDM
cd edm/;
# CIFAR-10
python torch.distributed.run --master_port 12315 --nproc_per_node=1 generate.py --outdir=generations/cifar10/temp --seeds=0-49999 --subdirs --method our --network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-uncond-vp.pkl
# ImageNet
python torch.distributed.run --master_port 12311 --nproc_per_node=1 generate.py --outdir=generations/imagenet/temp --seeds=0-49999 --subdirs --steps=256 --S_churn=40 --S_min=0.05 --S_max=50 --S_noise=1.003 --method our --network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-imagenet-64x64-cond-adm.pkl
The commands for running on FFHQ
and AFHQv2
are similar.