update with new and improved cosine noise scheduler

README.md

@@ -28,7 +28,7 @@ model = Unet(
 diffusion = GaussianDiffusion(
     model,
     timesteps = 1000,   # number of steps
-    loss_type = 'l1'    # L1 or L2
+    loss_type = 'l1'                  # L1 or L2
 )
 
 training_images = torch.randn(8, 3, 128, 128)

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -28,9 +28,6 @@ SAVE_AND_SAMPLE_EVERY = 1000
 UPDATE_EMA_EVERY = 10
 EXTS = ['jpg', 'jpeg', 'png']
 
-RESULTS_FOLDER = Path('./results')
-RESULTS_FOLDER.mkdir(exist_ok = True)
-
 # helpers functions
 
 def exists(x):
@@ -500,10 +497,10 @@ class Trainer(object):
             'model': self.model.state_dict(),
             'ema': self.ema_model.state_dict()
         }
-        torch.save(data, str(RESULTS_FOLDER / f'model-{milestone}.pt'))
+        torch.save(data, f'./model-{milestone}.pt')
 
     def load(self, milestone):
-        data = torch.load(str(RESULTS_FOLDER / f'model-{milestone}.pt'))
+        data = torch.load(f'./model-{milestone}.pt')
 
         self.step = data['step']
         self.model.load_state_dict(data['model'])
@@ -530,7 +527,7 @@ class Trainer(object):
                 batches = num_to_groups(36, self.batch_size)
                 all_images_list = list(map(lambda n: self.ema_model.sample(self.image_size, batch_size=n), batches))
                 all_images = torch.cat(all_images_list, dim=0)
-                utils.save_image(all_images, str(RESULTS_FOLDER / f'sample-{milestone}.png'), nrow=6)
+                utils.save_image(all_images, f'./sample-{milestone}.png', nrow=6)
                 self.save(milestone)
 
             self.step += 1

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.5.2',
+  version = '0.5.0',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',