diff --git a/core/leras/__init__.py b/core/leras/__init__.py
index 4b00a11..ba8c151 100644
--- a/core/leras/__init__.py
+++ b/core/leras/__init__.py
@@ -1,4 +1,5 @@
 import numpy as np
+from .nn import nn
 from core.leras import nn
 from .nn import nn
 tf = nn.tf
diff --git a/models/Model_AMP/Model.py b/models/Model_AMP/Model.py
index 2e95504..49afb89 100644
--- a/models/Model_AMP/Model.py
+++ b/models/Model_AMP/Model.py
@@ -29,6 +29,7 @@ class AMPModel(ModelBase):
         default_d_dims             = self.options['d_dims']             = self.options.get('d_dims', None)
         default_d_mask_dims        = self.options['d_mask_dims']        = self.options.get('d_mask_dims', None)
         default_morph_factor       = self.options['morph_factor']       = self.options.get('morph_factor', 0.5)
+        default_eyes_mouth_prio    = self.options['eyes_mouth_prio']    = self.load_or_def_option('eyes_mouth_prio', False)
         default_uniform_yaw        = self.options['uniform_yaw']        = self.load_or_def_option('uniform_yaw', False)
 
         # Uncomment it just if you want to impelement other loss functions
@@ -93,6 +94,9 @@ class AMPModel(ModelBase):
             self.options['morph_factor'] = morph_factor
 
         if self.is_first_run() or ask_override:
+
+
+            self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
             self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
             
             self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
@@ -152,6 +156,8 @@ class AMPModel(ModelBase):
         morph_factor = self.options['morph_factor']
         gan_power    = self.gan_power = self.options['gan_power']
         random_warp  = self.options['random_warp']
+        
+        eyes_mouth_prio = self.options['eyes_mouth_prio'] 
 
         blur_out_mask = self.options['blur_out_mask']
 
@@ -453,8 +459,9 @@ class AMPModel(ModelBase):
                     gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
 
                     # Eyes+mouth prio loss
-                    gpu_src_loss += tf.reduce_mean (300*tf.abs (gpu_target_src*gpu_target_srcm_em-gpu_pred_src_src*gpu_target_srcm_em), axis=[1,2,3])
-                    gpu_dst_loss += tf.reduce_mean (300*tf.abs (gpu_target_dst*gpu_target_dstm_em-gpu_pred_dst_dst*gpu_target_dstm_em), axis=[1,2,3])
+                    if eyes_mouth_prio:
+                        gpu_src_loss += tf.reduce_mean (300*tf.abs (gpu_target_src*gpu_target_srcm_em-gpu_pred_src_src*gpu_target_srcm_em), axis=[1,2,3])
+                        gpu_dst_loss += tf.reduce_mean (300*tf.abs (gpu_target_dst*gpu_target_dstm_em-gpu_pred_dst_dst*gpu_target_dstm_em), axis=[1,2,3])
 
                     # Mask loss
                     gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )