make it so even if text mask is omitted, it will be derived based on whether text encodings are all 0s or not, simplify dataloading

dalle2_pytorch/dalle2_pytorch.py

@@ -220,6 +220,7 @@ class XClipAdapter(BaseClipAdapter):
         encoder_output = self.clip.text_transformer(text)
         text_cls, text_encodings = encoder_output[:, 0], encoder_output[:, 1:]
         text_embed = self.clip.to_text_latent(text_cls)
+        text_encodings = text_encodings.masked_fill(~text_mask[..., None], 0.)
         return EmbeddedText(l2norm(text_embed), text_encodings, text_mask)
 
     @torch.no_grad()
@@ -255,6 +256,7 @@ class CoCaAdapter(BaseClipAdapter):
         text = text[..., :self.max_text_len]
         text_mask = text != 0
         text_embed, text_encodings = self.clip.embed_text(text)
+        text_encodings = text_encodings.masked_fill(~text_mask[..., None], 0.)
         return EmbeddedText(text_embed, text_encodings, text_mask)
 
     @torch.no_grad()
@@ -314,6 +316,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
 
         text_embed = self.clip.encode_text(text)
         text_encodings = self.text_encodings
+        text_encodings = text_encodings.masked_fill(~text_mask[..., None], 0.)
         del self.text_encodings
         return EmbeddedText(l2norm(text_embed.float()), text_encodings.float(), text_mask)
 
@@ -1197,6 +1200,7 @@ class DiffusionPrior(nn.Module):
 
         if self.condition_on_text_encodings:
             assert exists(text_encodings), 'text encodings must be present for diffusion prior if specified'
+            text_mask = default(text_mask, lambda: torch.any(text_encodings != 0., dim = -1))
             text_cond = {**text_cond, 'text_encodings': text_encodings, 'mask': text_mask}
 
         # timestep conditioning from ddpm
@@ -2410,6 +2414,9 @@ class Decoder(nn.Module):
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
+        if self.condition_on_text_encodings:
+            text_mask = default(text_mask, lambda: torch.any(text_encodings != 0., dim = -1))
+
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
@@ -2493,6 +2500,9 @@ class Decoder(nn.Module):
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
+        if self.condition_on_text_encodings:
+            text_mask = default(text_mask, lambda: torch.any(text_encodings != 0., dim = -1))
+
         lowres_cond_img = self.to_lowres_cond(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if unet_number > 1 else None
         image = resize_image_to(image, target_image_size)
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.19.6'
+__version__ = '0.20.0'