be able to turn off warning for use of LazyLinear by passing in text embedding dimension for unet

dalle2_pytorch/dalle2_pytorch.py

@@ -906,6 +906,7 @@ class Unet(nn.Module):
         dim,
         *,
         image_embed_dim,
+        text_embed_dim = None,
         cond_dim = None,
         num_image_tokens = 4,
         num_time_tokens = 2,
@@ -959,7 +960,7 @@ class Unet(nn.Module):
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
         ) if image_embed_dim != cond_dim else nn.Identity()
 
-        self.text_to_cond = nn.LazyLinear(cond_dim)
+        self.text_to_cond = nn.LazyLinear(cond_dim) if not exists(text_embed_dim) else nn.Linear(text_embed_dim, cond_dim)
 
         # finer control over whether to condition on image embeddings and text encodings
         # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.51',
+  version = '0.0.52',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',