just force it so researcher can never pass in an image that is less than the size that is required for CLIP or CoCa

dalle2_pytorch/dalle2_pytorch.py

@@ -169,6 +169,11 @@ class BaseClipAdapter(nn.Module):
         self.clip = clip
         self.overrides = kwargs
 
+    def validate_and_resize_image(self, image):
+        image_size = image.shape[-1]
+        assert image_size >= self.image_size, f'you are passing in an image of size {image_size} but CLIP requires the image size to be at least {self.image_size}'
+        return resize_image_to(image, self.image_size)
+
     @property
     def dim_latent(self):
         raise NotImplementedError
@@ -219,7 +224,7 @@ class XClipAdapter(BaseClipAdapter):
 
     @torch.no_grad()
     def embed_image(self, image):
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         encoder_output = self.clip.visual_transformer(image)
         image_cls, image_encodings = encoder_output[:, 0], encoder_output[:, 1:]
         image_embed = self.clip.to_visual_latent(image_cls)
@@ -254,7 +259,7 @@ class CoCaAdapter(BaseClipAdapter):
 
     @torch.no_grad()
     def embed_image(self, image):
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         image_embed, image_encodings = self.clip.embed_image(image)
         return EmbeddedImage(image_embed, image_encodings)
 
@@ -315,7 +320,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
     @torch.no_grad()
     def embed_image(self, image):
         assert not self.cleared
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         image = self.clip_normalize(image)
         image_embed = self.clip.encode_image(image)
         return EmbeddedImage(l2norm(image_embed.float()), None)

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.17.0'
+__version__ = '0.17.1'