save samples and models to ./results path

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -503,7 +503,7 @@ class Trainer(object):
         torch.save(data, str(RESULTS_FOLDER / f'model-{milestone}.pt'))
 
     def load(self, milestone):
-        data = torch.load(str(RESULTS_FOLDER / 'model-{milestone}.pt'))
+        data = torch.load(str(RESULTS_FOLDER / f'model-{milestone}.pt'))
 
         self.step = data['step']
         self.model.load_state_dict(data['model'])
@@ -530,7 +530,7 @@ 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 / 'sample-{milestone}.png'), nrow=6)
+                utils.save_image(all_images, str(RESULTS_FOLDER / 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 = '0.5.1',
+  version = '0.5.2',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',