diff --git a/clipperpweight.py b/clipperpweight.py
index ab07e47..47c83d0 100644
--- a/clipperpweight.py
+++ b/clipperpweight.py
@@ -15,9 +15,7 @@ class CLIPTextEncodePerpWeight:
     def encode(self, clip, text):
         empty_tokens = clip.tokenize("")
 
-        sdxl_flag = False
-        if isinstance(empty_tokens, dict):
-            sdxl_flag = True
+        sdxl_flag = isinstance(empty_tokens, dict)
         
         if sdxl_flag:
             empty_cond, empty_cond_pooled = clip.encode_from_tokens(empty_tokens, return_pooled=True)
@@ -30,10 +28,10 @@ class CLIPTextEncodePerpWeight:
             cond = torch.clone(unweighted_cond)
             for i in range(unweighted_cond.shape[0]):
                 for j in range(unweighted_cond.shape[1]):
-                    weight_l = tokens["l"][i][j][1]
+                    weight_l = tokens["l"][(j//77)][(j%77)][1]
                     if weight_l != 1.0:
                         token_vector_l = unweighted_cond[i][j][:768]
-                        zero_vector_l = empty_cond[0][j][:768]
+                        zero_vector_l = empty_cond[0][(j%77)][:768]
                         perp_l = ((torch.mul(zero_vector_l, token_vector_l).sum())/(torch.norm(token_vector_l)**2)) * token_vector_l
                         if weight_l > 1.0:
                             cond[i][j][:768] = token_vector_l + (weight_l * perp_l)
@@ -42,12 +40,12 @@ class CLIPTextEncodePerpWeight:
                         elif weight_l < 0.0:
                             cond[i][j][:768] = token_vector_l + (weight_l * perp_l)
                         elif weight_l == 0.0:
-                            cond[i][j][:768] = empty_cond[0][j][:768]
+                            cond[i][j][:768] = empty_cond[0][(j%77)][:768]
                     
-                    weight_g = tokens["g"][i][j][1]
+                    weight_g = tokens["g"][(j//77)][(j%77)][1]
                     if weight_g != 1.0:
                         token_vector_g = unweighted_cond[i][j][768:]
-                        zero_vector_g = empty_cond[0][j][768:]
+                        zero_vector_g = empty_cond[0][(j%77)][768:]
                         perp_g = ((torch.mul(zero_vector_g, token_vector_g).sum())/(torch.norm(token_vector_g)**2)) * token_vector_g
                         if weight_g > 1.0:
                             cond[i][j][768:] = token_vector_g + (weight_g * perp_g)
@@ -56,7 +54,7 @@ class CLIPTextEncodePerpWeight:
                         elif (weight_g < 0.0):
                             cond[i][j][768:] = token_vector_g + (weight_g * perp_g)
                         elif weight_g == 0.0:
-                            cond[i][j][768:] = empty_cond[0][j][768:]
+                            cond[i][j][768:] = empty_cond[0][(j%77)][768:]
         else:
             empty_cond, empty_cond_pooled = clip.encode_from_tokens(empty_tokens, return_pooled=True)
             tokens = clip.tokenize(text)
@@ -66,10 +64,10 @@ class CLIPTextEncodePerpWeight:
             cond = torch.clone(unweighted_cond)
             for i in range(unweighted_cond.shape[0]):
                 for j in range(unweighted_cond.shape[1]):
-                    weight = tokens[i][j][1]
+                    weight = tokens[(j//77)][(j%77)][1]
                     if weight != 1.0:
                         token_vector = unweighted_cond[i][j]
-                        zero_vector = empty_cond[0][j]
+                        zero_vector = empty_cond[0][(j%77)]
                         perp = ((torch.mul(zero_vector, token_vector).sum())/(torch.norm(token_vector)**2)) * token_vector
                         if weight > 1.0:
                             cond[i][j] = token_vector + (weight * perp)
@@ -78,7 +76,7 @@ class CLIPTextEncodePerpWeight:
                         elif (weight < 0.0):
                             cond[i][j] = token_vector + (weight * perp)
                         elif weight == 0.0:
-                            cond[i][j] = empty_cond[0][j]
+                            cond[i][j] = empty_cond[0][(j%77)]
         
         return ([[cond, {"pooled_output": unweighted_pooled}]], )