Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Code for the paper Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
News (Apr 22, 2022): Analytic-DPM received an Outstanding Paper Award at ICLR 2022!
pytorch=1.9.0
You can change the phase
variable in the code to determine the specific experiment you run.
For example, setting phase = "sample_analytic_ddpm"
will run sampling using the Analytic-DDPM.
You can find all available phases in run_xxx.py
.
$ cd cifar_imagenet_codes
$ python run_cifar10.py
$ cd celeba_lsun_codes
$ python run_celeba.py
$ cd cifar_imagenet_codes
$ python run_imagenet64.py
$ cd celeba_lsun_codes
$ python run_lsun_bedroom.py
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CIFAR10 model: [checkpoint] trained by ourselves
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CelebA 64x64 model: [checkpoint] from https://github.com/ermongroup/ddim
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Imagenet 64x64 model: [checkpoint] from https://github.com/openai/improved-diffusion
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LSUN Bedroom model: [checkpoint] from https://github.com/pesser/pytorch_diffusion
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Precalculated Gamma vectors: [link]