make sure all versions of torch supported

denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py

@@ -1,3 +1,4 @@
+import math
 import torch
 from torch import sqrt
 from torch import nn, einsum
@@ -66,7 +67,7 @@ def beta_linear_log_snr(t):
     return -log(expm1(1e-4 + 10 * (t ** 2)))
 
 def alpha_cosine_log_snr(t, s = 0.008):
-    return -log((torch.cos((t + s) / (1 + s) * torch.pi * 0.5) ** -2) - 1, eps = 1e-5)
+    return -log((torch.cos((t + s) / (1 + s) * math.pi * 0.5) ** -2) - 1, eps = 1e-5)
 
 class learned_noise_schedule(nn.Module):
     """ described in section H and then I.2 of the supplementary material for variational ddpm paper """

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -355,7 +355,7 @@ def cosine_beta_schedule(timesteps, s = 0.008):
     """
     steps = timesteps + 1
     x = torch.linspace(0, timesteps, steps, dtype = torch.float64)
-    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
+    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
     alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
     betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
     return torch.clip(betas, 0, 0.999)
@@ -590,7 +590,7 @@ class Trainer(object):
         gradient_accumulate_every = 2,
         amp = False,
         step_start_ema = 2000,
-        update_ema_every = 10,
+        ema_update_every = 10,
         save_and_sample_every = 1000,
         results_folder = './results',
         augment_horizontal_flip = True
@@ -599,8 +599,7 @@ class Trainer(object):
         self.image_size = diffusion_model.image_size
 
         self.model = diffusion_model
-        self.ema = EMA(diffusion_model, beta = ema_decay)
-        self.update_ema_every = update_ema_every
+        self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)
 
         self.step_start_ema = step_start_ema
         self.save_and_sample_every = save_and_sample_every

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.21.0',
+  version = '0.21.2',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',