Update pipeline.py

pipeline.py

@@ -340,13 +340,15 @@ def get_weighted_text_embeddings(
     # assign weights to the prompts and normalize in the sense of mean
     # TODO: should we normalize by chunk or in a whole (current implementation)?
     if (not skip_parsing) and (not skip_weighting):
-        previous_mean = text_embeddings.mean(axis=[-2, -1])
+        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
         text_embeddings *= prompt_weights.unsqueeze(-1)
-        text_embeddings *= (previous_mean / text_embeddings.mean(axis=[-2, -1])).unsqueeze(-1).unsqueeze(-1)
+        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
         if uncond_prompt is not None:
-            previous_mean = uncond_embeddings.mean(axis=[-2, -1])
+            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
             uncond_embeddings *= uncond_weights.unsqueeze(-1)
-            uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=[-2, -1])).unsqueeze(-1).unsqueeze(-1)
+            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
 
     if uncond_prompt is not None:
         return text_embeddings, uncond_embeddings