fix condition_on_text_encodings in dalle2 orchestrator class, fix readme

README.md

@@ -581,7 +581,8 @@ unet1 = Unet(
     image_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    cond_on_text_encodings = True  # set to True for any unets that need to be conditioned on text encodings (ex. first unet in cascade)
 ).cuda()
 
 unet2 = Unet(
@@ -598,8 +599,7 @@ decoder = Decoder(
     clip = clip,
     timesteps = 100,
     image_cond_drop_prob = 0.1,
-    text_cond_drop_prob = 0.5,
-    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 for unet_number in (1, 2):

dalle2_pytorch/dalle2_pytorch.py

@@ -1930,10 +1930,6 @@ class Decoder(nn.Module):
             self.unets.append(one_unet)
             self.vaes.append(one_vae.copy_for_eval())
 
-        # determine from unets whether conditioning on text encoding is needed
-
-        self.condition_on_text_encodings = any([unet.cond_on_text_encodings for unet in self.unets])
-
         # create noise schedulers per unet
 
         if not exists(beta_schedule):
@@ -2012,6 +2008,10 @@ class Decoder(nn.Module):
     def device(self):
         return self._dummy.device
 
+    @property
+    def condition_on_text_encodings(self):
+        return any([unet.cond_on_text_encodings for unet in self.unets])
+
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
         index = unet_number - 1

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.16.14'
+__version__ = '0.16.15'