diff --git a/py/bnk_adv_encode.py b/py/bnk_adv_encode.py
index f44eecf..0e30a05 100644
--- a/py/bnk_adv_encode.py
+++ b/py/bnk_adv_encode.py
@@ -1,10 +1,10 @@
 import torch
 import numpy as np
 import itertools
-from math import gcd
+#from math import gcd
 
 from comfy import model_management
-from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
+from comfy.sdxl_clip import SDXLClipModel
 
 def _grouper(n, iterable):
     it = iter(iterable)
@@ -235,11 +235,10 @@ def prepareXL(embs_l, embs_g, pooled, clip_balance):
         return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled
     else:
         return embs_g, pooled
-  #=====================
 
 def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
     tokenized = clip.tokenize(text, return_word_ids=True)
-    if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
+    if isinstance(tokenized, dict):
         embs_l = None
         embs_g = None
         pooled = None
@@ -273,32 +272,6 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
                                            lambda x: (clip.encode_from_tokens(x), None),
                                            w_max=w_max)
 
-  #=====================        
-def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
-    tokenized1 = clip.tokenize(text1, return_word_ids=True)
-    tokenized2 = clip.tokenize(text2, return_word_ids=True)
-
-    embs_l, _ = advanced_encode_from_tokens(tokenized1['l'], 
-                                            token_normalization, 
-                                            weight_interpretation, 
-                                            lambda x: encode_token_weights(clip, x, encode_token_weights_l),
-                                            w_max=w_max, 
-                                            return_pooled=False)
-
-    embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'], 
-                                                 token_normalization, 
-                                                 weight_interpretation,
-                                                 lambda x: encode_token_weights(clip, x, encode_token_weights_g),
-                                                 w_max=w_max, 
-                                                 return_pooled=True,
-                                                 apply_to_pooled=apply_to_pooled)
-
-    gcd_num = gcd(embs_l.shape[1], embs_g.shape[1])
-    repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1])
-    repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1])
-
-    return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance)
-
 ########################################################################################################################
 from nodes import MAX_RESOLUTION