move the inverse normalization function called before image embeddings are derived from clip to within the diffusion prior and decoder classes

dalle2_pytorch/dalle2_pytorch.py

@@ -278,7 +278,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
     def embed_image(self, image):
         assert not self.cleared
         image = resize_image_to(image, self.image_size)
-        image = self.clip_normalize(unnormalize_img(image))
+        image = self.clip_normalize(image)
         image_embed = self.clip.encode_image(image)
         return EmbeddedImage(l2norm(image_embed.float()), None)
 
@@ -1037,7 +1037,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         assert not (self.condition_on_text_encodings and (not exists(text_encodings) and not exists(text))), 'text encodings must be present if you specified you wish to condition on it on initialization'
 
         if exists(image):
-            image_embed, _ = self.clip.embed_image(image)
+            image_embed, _ = self.clip.embed_image(unnormalize_img(image))
 
         # calculate text conditionings, based on what is passed in
 
@@ -2011,7 +2011,7 @@ class Decoder(BaseGaussianDiffusion):
 
         if not exists(image_embed):
             assert exists(self.clip), 'if you want to derive CLIP image embeddings automatically, you must supply `clip` to the decoder on init'
-            image_embed, _ = self.clip.embed_image(image)
+            image_embed, _ = self.clip.embed_image(unnormalize_img(image))
 
         text_encodings = text_mask = None
         if exists(text) and not exists(text_encodings):

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.14',
+  version = '0.2.15',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',