prepare unet to be conditioned on image embedding, optionally text encodings, and reminder for self to build conditional dropout for classifier free guidance

dalle2_pytorch/dalle2_pytorch.py

@@ -284,6 +284,8 @@ class Unet(nn.Module):
     def __init__(
         self,
         dim,
+        *,
+        image_embed_dim,
         out_dim = None,
         dim_mults=(1, 2, 4, 8),
         channels = 3,
@@ -338,7 +340,15 @@ class Unet(nn.Module):
             nn.Conv2d(dim, out_dim, 1)
         )
 
-    def forward(self, x, time):
+    def forward(
+        self,
+        x,
+        *,
+        image_embed,
+        time,
+        text_encodings = None,
+        cond_prob_drop = 0.2
+    ):
         t = self.time_mlp(time) if exists(self.time_mlp) else None
 
         hiddens = []