Update denoising_diffusion_pytorch_1d.py

Shouldn't eta be 0 for DDIM sampling? In the DDIM paper in section 5.1 they mention the eta=0 is DDIM, and eta=1 approximates the normal DDPM. They also use [eta=0](https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py#L487) in the openai implementation of DDIM.

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -23,6 +23,8 @@ from ema_pytorch import EMA
 
 from accelerate import Accelerator
 
+from denoising_diffusion_pytorch.version import __version__
+
 # constants
 
 ModelPrediction =  namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
@@ -429,7 +431,7 @@ class GaussianDiffusion(nn.Module):
         beta_schedule = 'cosine',
         p2_loss_weight_gamma = 0., # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
         p2_loss_weight_k = 1,
-        ddim_sampling_eta = 1.
+        ddim_sampling_eta = 0.
     ):
         super().__init__()
         assert not (type(self) == GaussianDiffusion and model.channels != model.out_dim)
@@ -808,8 +810,8 @@ class Trainer(object):
         if self.accelerator.is_main_process:
             self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)
 
-            self.results_folder = Path(results_folder)
-            self.results_folder.mkdir(exist_ok = True)
+        self.results_folder = Path(results_folder)
+        self.results_folder.mkdir(exist_ok = True)
 
         # step counter state
 
@@ -828,7 +830,8 @@ class Trainer(object):
             'model': self.accelerator.get_state_dict(self.model),
             'opt': self.opt.state_dict(),
             'ema': self.ema.state_dict(),
-            'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None
+            'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None,
+            'version': __version__
         }
 
         torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
@@ -846,6 +849,9 @@ class Trainer(object):
         self.opt.load_state_dict(data['opt'])
         self.ema.load_state_dict(data['ema'])
 
+        if 'version' in data:
+            print(f"loading from version {data['version']}")
+
         if exists(self.accelerator.scaler) and exists(data['scaler']):
             self.accelerator.scaler.load_state_dict(data['scaler'])
 

denoising_diffusion_pytorch/denoising_diffusion_pytorch_1d.py

@@ -414,7 +414,7 @@ class GaussianDiffusion1D(nn.Module):
         beta_schedule = 'cosine',
         p2_loss_weight_gamma = 0.,
         p2_loss_weight_k = 1,
-        ddim_sampling_eta = 1.
+        ddim_sampling_eta = 0.
     ):
         super().__init__()
         self.model = model

denoising_diffusion_pytorch/version.py

@@ -0,0 +1 @@
+__version__ = '0.1.5'

setup.py

@@ -1,9 +1,11 @@
 from setuptools import setup, find_packages
 
+exec(open('denoising_diffusion_pytorch/version.py').read())
+
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.32.0',
+  version = __version__,
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',