let researchers do the hyperparameter search

dalle2_pytorch/dalle2_pytorch.py

@@ -1072,6 +1072,8 @@ class Unet(nn.Module):
         cond_on_text_encodings = False,
         max_text_len = 256,
         cond_on_image_embeds = False,
+        init_dim = None,
+        init_conv_kernel_size = 7
     ):
         super().__init__()
         # save locals to take care of some hyperparameters for cascading DDPM
@@ -1089,9 +1091,10 @@ class Unet(nn.Module):
         self.channels = channels
 
         init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
-        init_dim = dim // 2
+        init_dim = default(init_dim, dim // 2)
 
-        self.init_conv = nn.Conv2d(init_channels, init_dim, 7, padding = 3)
+        assert (init_conv_kernel_size % 2) == 1
+        self.init_conv = nn.Conv2d(init_channels, init_dim, init_conv_kernel_size, padding = init_conv_kernel_size // 2)
 
         dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.84',
+  version = '0.0.85',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',