diff --git a/pasd/myutils/vaehook.py b/pasd/myutils/vaehook.py
index 0fc09e5..4bde630 100644
--- a/pasd/myutils/vaehook.py
+++ b/pasd/myutils/vaehook.py
@@ -171,7 +171,6 @@ def attn_forward_new(self, h_):
     return hidden_states
 
 def attn_forward_new_pt2_0(self, hidden_states,):
-    scale = 1
     attention_mask = None
     encoder_hidden_states = None
 
@@ -194,15 +193,15 @@ def attn_forward_new_pt2_0(self, hidden_states,):
     if self.group_norm is not None:
         hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-    query = self.to_q(hidden_states, scale=scale)
+    query = self.to_q(hidden_states)
 
     if encoder_hidden_states is None:
         encoder_hidden_states = hidden_states
     elif self.norm_cross:
         encoder_hidden_states = self.norm_encoder_hidden_states(encoder_hidden_states)
 
-    key = self.to_k(encoder_hidden_states, scale=scale)
-    value = self.to_v(encoder_hidden_states, scale=scale)
+    key = self.to_k(encoder_hidden_states)
+    value = self.to_v(encoder_hidden_states)
 
     inner_dim = key.shape[-1]
     head_dim = inner_dim // self.heads
@@ -222,7 +221,7 @@ def attn_forward_new_pt2_0(self, hidden_states,):
     hidden_states = hidden_states.to(query.dtype)
 
     # linear proj
-    hidden_states = self.to_out[0](hidden_states, scale=scale)
+    hidden_states = self.to_out[0](hidden_states)
     # dropout
     hidden_states = self.to_out[1](hidden_states)
 
@@ -232,7 +231,6 @@ def attn_forward_new_pt2_0(self, hidden_states,):
     return hidden_states
 
 def attn_forward_new_xformers(self, hidden_states):
-    scale = 1
     attention_op = None
     attention_mask = None
     encoder_hidden_states = None
@@ -261,15 +259,15 @@ def attn_forward_new_xformers(self, hidden_states):
     if self.group_norm is not None:
         hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-    query = self.to_q(hidden_states, scale=scale)
+    query = self.to_q(hidden_states)
 
     if encoder_hidden_states is None:
         encoder_hidden_states = hidden_states
     elif self.norm_cross:
         encoder_hidden_states = self.norm_encoder_hidden_states(encoder_hidden_states)
 
-    key = self.to_k(encoder_hidden_states, scale=scale)
-    value = self.to_v(encoder_hidden_states, scale=scale)
+    key = self.to_k(encoder_hidden_states)
+    value = self.to_v(encoder_hidden_states)
 
     query = self.head_to_batch_dim(query).contiguous()
     key = self.head_to_batch_dim(key).contiguous()
@@ -282,7 +280,7 @@ def attn_forward_new_xformers(self, hidden_states):
     hidden_states = self.batch_to_head_dim(hidden_states)
 
     # linear proj
-    hidden_states = self.to_out[0](hidden_states, scale=scale)
+    hidden_states = self.to_out[0](hidden_states)
     # dropout
     hidden_states = self.to_out[1](hidden_states)