groupnorm groups should be actually configurable

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -128,8 +128,8 @@ class ResnetBlock(nn.Module):
             nn.Linear(time_emb_dim, dim_out)
         ) if exists(time_emb_dim) else None
 
-        self.block1 = Block(dim, dim_out)
-        self.block2 = Block(dim_out, dim_out)
+        self.block1 = Block(dim, dim_out, groups = groups)
+        self.block2 = Block(dim_out, dim_out, groups = groups)
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
     def forward(self, x, time_emb = None):
@@ -238,7 +238,8 @@ class Unet(nn.Module):
         channels = 3,
         with_time_emb = True,
         use_convnext = False,
-        resnet_block_groups = 8
+        resnet_block_groups = 8,
+        convnext_mult = 2
     ):
         super().__init__()
 
@@ -255,7 +256,7 @@ class Unet(nn.Module):
         # resnet or convnext
 
         if use_convnext:
-            block_klass = ConvNextBlock
+            block_klass = partial(ConvNextBlock, mult = convnext_mult)
         else:
             block_klass = partial(ResnetBlock, groups = resnet_block_groups)
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.11.0',
+  version = '0.11.2',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',