diff --git a/models/Model_AMP/Model.py b/models/Model_AMP/Model.py
index f4ede17..2e1015e 100644
--- a/models/Model_AMP/Model.py
+++ b/models/Model_AMP/Model.py
@@ -50,7 +50,7 @@ class AMPModel(ModelBase):
         default_random_jpeg        = self.options['random_jpeg']        = self.load_or_def_option('random_jpeg', False)
 
         # Uncomment it just if you want to impelement other loss functions
-        #default_background_power   = self.options['background_power']   = self.load_or_def_option('background_power', 0.0)
+        default_background_power   = self.options['background_power']   = self.load_or_def_option('background_power', 0.0)
         default_ct_mode            = self.options['ct_mode']            = self.load_or_def_option('ct_mode', 'none')
         default_random_color       = self.options['random_color']       = self.load_or_def_option('random_color', False)
         default_clipgrad           = self.options['clipgrad']           = self.load_or_def_option('clipgrad', False)
@@ -149,7 +149,7 @@ class AMPModel(ModelBase):
                 self.options['gan_noise'] = np.clip ( io.input_number("GAN noisy labels", default_gan_noise, add_info="0 - 0.5", help_message="Marks some images with the wrong label, helps prevent collapse"), 0, 0.5)
 
 
-            #self.options['background_power'] = np.clip ( io.input_number("Background power", default_background_power, add_info="0.0..1.0", help_message="Learn the area outside of the mask. Helps smooth out area near the mask boundaries. Can be used at any time"), 0.0, 1.0 )
+            self.options['background_power'] = np.clip ( io.input_number("Background power", default_background_power, add_info="0.0..1.0", help_message="Learn the area outside of the mask. Helps smooth out area near the mask boundaries. Can be used at any time"), 0.0, 1.0 )
 
             self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot', 'fs-aug'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
             self.options['random_color'] = io.input_bool ("Random color", default_random_color, help_message="Samples are randomly rotated around the L axis in LAB colorspace, helps generalize training")
@@ -185,6 +185,11 @@ class AMPModel(ModelBase):
         random_warp  = self.options['random_warp']
         random_hsv_power = self.options['random_hsv_power']
         
+        if 'eyes_mouth_prio' in self.options:
+            self.options.pop('eyes_mouth_prio')
+            
+        bg_factor = self.options['background_power']
+        
         eyes_prio = self.options['eyes_prio']
         mouth_prio = self.options['mouth_prio']
         masked_training = self.options['masked_training'] 
@@ -533,6 +538,27 @@ class AMPModel(ModelBase):
                         gpu_dst_loss += tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
                         # Pixel loss
                         gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
+                        
+                    if bg_factor > 0:
+                        if self.options['loss_function'] == 'MS-SSIM':
+                            gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
+                            gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
+                            gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
+                            gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
+                        elif self.options['loss_function'] == 'MS-SSIM+L1':
+                            gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
+                            gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
+
+                        else:
+                            gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                            gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
+                            gpu_src_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                            gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
+                            
+                        gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
+                        gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
+                            
+                    
                     
                     # Eyes+mouth prio loss
                     # if eyes_mouth_prio: