normalize conditioning tokens outside of cross attention blocks

README.md

@@ -1017,6 +1017,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] offer save / load methods on the trainer classes to automatically take care of state dicts for scalers / optimizers / saving versions and checking for breaking changes
 - [ ] bring in skip-layer excitatons (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training
 - [ ] decoder needs one day worth of refactor for tech debt
+- [ ] allow for unet to be able to condition non-cross attention style as well
 
 ## Citations
 

dalle2_pytorch/dalle2_pytorch.py

@@ -1163,6 +1163,7 @@ class CrossAttention(nn.Module):
         dim_head = 64,
         heads = 8,
         dropout = 0.,
+        norm_context = False
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -1172,7 +1173,7 @@ class CrossAttention(nn.Module):
         context_dim = default(context_dim, dim)
 
         self.norm = LayerNorm(dim)
-        self.norm_context = LayerNorm(context_dim)
+        self.norm_context = LayerNorm(context_dim) if norm_context else nn.Identity()
         self.dropout = nn.Dropout(dropout)
 
         self.null_kv = nn.Parameter(torch.randn(2, dim_head))
@@ -1378,6 +1379,9 @@ 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.norm_cond = nn.LayerNorm(cond_dim)
+        self.norm_mid_cond = nn.LayerNorm(cond_dim)
+
         # text encoding conditioning (optional)
 
         self.text_to_cond = None
@@ -1593,6 +1597,11 @@ class Unet(nn.Module):
 
         mid_c = c if not exists(text_tokens) else torch.cat((c, text_tokens), dim = -2)
 
+        # normalize conditioning tokens
+
+        c = self.norm_cond(c)
+        mid_c = self.norm_mid_cond(mid_c)
+
         # go through the layers of the unet, down and up
 
         hiddens = []

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.22',
+  version = '0.2.23',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',