Add more prints to function

app.py

@@ -97,6 +97,7 @@ pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",
 def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
   loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
   
+  _old_token = token
   # separate token and the embeds
   trained_token = list(loaded_learned_embeds.keys())[0]
   embeds = loaded_learned_embeds[trained_token]
@@ -108,10 +109,15 @@ def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, tok
   token = token if token is not None else trained_token
   num_added_tokens = tokenizer.add_tokens(token)
   i = 1
+  print("start while loop **************")
   while(num_added_tokens == 0):
     token = f"{token[:-1]}-{i}>"
     num_added_tokens = tokenizer.add_tokens(token)
+    print("i --> ", i)
+    print("token --> ", token)
+    print("num_added_tokens --> ", num_added_tokens)
     i+=1
+  print("end while loop **************")
   
   # resize the token embeddings
   text_encoder.resize_token_embeddings(len(tokenizer))
@@ -119,9 +125,18 @@ def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, tok
   # get the id for the token and assign the embeds
   token_id = tokenizer.convert_tokens_to_ids(token)
   print("&&&&&&&&&&&&&&&&")
+  print("learned_embeds_path --> ", learned_embeds_path)
+  print("text_encoder --> ", text_encoder)
+  print("tokenizer --> ", tokenizer)
+  print("_old_token --> ", _old_token)
+  print("token --> ", token)
+  print("trained_token --> ", trained_token)
+  print("dtype --> ", dtype)
+  print("num_added_tokens --> ", num_added_tokens)
   print("text_encoder --> ", text_encoder)
   print("token_id --> ", token_id)
   print("embeds --> ", embeds)
+  print("&&&&&&&&&&&&&&&&")
   text_encoder.get_input_embeddings().weight.data[token_id] = embeds # <------ POINT OF FAILURE
   return token