allow text encodings and text mask to be passed in on forward and sampling for Decoder class

dalle2_pytorch/dalle2_pytorch.py

@@ -61,6 +61,9 @@ def default(val, d):
 def cast_tuple(val, length = 1):
     return val if isinstance(val, tuple) else ((val,) * length)
 
+def module_device(module):
+    return next(module.parameters()).device
+
 @contextmanager
 def null_context(*args, **kwargs):
     yield
@@ -1817,7 +1820,7 @@ class Decoder(BaseGaussianDiffusion):
 
         self.cuda()
 
-        devices = [next(unet.parameters()).device for unet in self.unets]
+        devices = [module_device(unet) for unet in self.unets]
         self.unets.cpu()
         unet.cuda()
 
@@ -1965,6 +1968,8 @@ class Decoder(BaseGaussianDiffusion):
         self,
         image_embed = None,
         text = None,
+        text_mask = None,
+        text_encodings = None,
         batch_size = 1,
         cond_scale = 1.,
         stop_at_unet_number = None
@@ -1974,8 +1979,8 @@ class Decoder(BaseGaussianDiffusion):
         if not self.unconditional:
             batch_size = image_embed.shape[0]
 
-        text_encodings = text_mask = None
+        if exists(text) and not exists(text_encodings) and not self.unconditional:
-        if exists(text):
+            assert exists(self.clip)
             _, text_encodings, text_mask = self.clip.embed_text(text)
 
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
@@ -2027,6 +2032,7 @@ class Decoder(BaseGaussianDiffusion):
         text = None,
         image_embed = None,
         text_encodings = None,
+        text_mask = None,
         unet_number = None
     ):
         assert not (len(self.unets) > 1 and not exists(unet_number)), f'you must specify which unet you want trained, from a range of 1 to {len(self.unets)}, if you are training cascading DDPM (multiple unets)'
@@ -2051,7 +2057,6 @@ class Decoder(BaseGaussianDiffusion):
             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)
 
-        text_encodings = text_mask = None
         if exists(text) and not exists(text_encodings) and not self.unconditional:
             assert exists(self.clip), 'if you are passing in raw text, you need to supply `clip` to the decoder'
             _, text_encodings, text_mask = self.clip.embed_text(text)
@@ -2107,7 +2112,7 @@ class DALLE2(nn.Module):
         prior_cond_scale = 1.,
         return_pil_images = False
     ):
-        device = next(self.parameters()).device
+        device = module_device(self)
         one_text = isinstance(text, str) or (not is_list_str(text) and text.shape[0] == 1)
 
         if isinstance(text, str) or is_list_str(text):

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.40',
+  version = '0.2.42',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',