diff --git a/models/ModelBase.py b/models/ModelBase.py
index 7fc166f..8b66a5f 100644
--- a/models/ModelBase.py
+++ b/models/ModelBase.py
@@ -106,14 +106,8 @@ class ModelBase(object):
             self.options.pop('target_epoch') 
            
         self.batch_size = self.options['batch_size']
-
-        self.sort_by_yaw = self.options['sort_by_yaw']
-        if not self.sort_by_yaw:
-            self.options.pop('sort_by_yaw') 
-        
+        self.sort_by_yaw = self.options['sort_by_yaw']        
         self.random_flip = self.options['random_flip']
-        if self.random_flip:
-            self.options.pop('random_flip') 
         
         self.src_scale_mod = self.options['src_scale_mod']
         if self.src_scale_mod == 0:
diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index cc46e91..c3d445c 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -50,6 +50,12 @@ class SAEModel(ModelBase):
             self.options['bg_style_power'] = np.clip ( input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, help_message="How fast NN will learn dst background style during generalization of src and dst faces. If style is learned good enough, set this value to 0.1-0.3 to prevent artifacts appearing."), 0.0, 100.0 )            
         else:
             self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power)
+        
+        if is_first_run or ask_override: 
+            default_pixel_loss = False if is_first_run else self.options.get('pixel_loss', False)
+            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", default_pixel_loss, help_message="Default perceptual (DSSIM) loss good for initial understanding structure of faces. Use pixel loss after 20-30k epochs to enhance fine details.")
+        else:
+            self.options['pixel_loss'] = self.options.get('pixel_loss', False)
             
         default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
         default_ed_ch_dims = 42
@@ -207,20 +213,30 @@ class SAEModel(ModelBase):
                 if self.options['learn_mask']:
                     src_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights
                     dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_dstm.trainable_weights
-                
-            src_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked_ar[i], pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] ) )) for i in range(len(target_src_masked_ar)) ])
-        
+            
+            if self.options['pixel_loss']:
+                src_loss = sum([ K.mean( 100*K.square( target_src_masked_ar[i] - pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] )) for i in range(len(target_src_masked_ar)) ])
+            else:
+                src_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked_ar[i], pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] ) )) for i in range(len(target_src_masked_ar)) ])
+            
             if self.options['face_style_power'] != 0:
                 face_style_power = self.options['face_style_power'] / 100.0
                 src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2*face_style_power)( psd_target_dst_masked_ar[-1], target_dst_masked_ar[-1] ) 
                 
             if self.options['bg_style_power'] != 0:
                 bg_style_power = self.options['bg_style_power'] / 100.0
-                src_loss += K.mean( (100*bg_style_power)*K.square(tf_dssim(2.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )))
-
+                if self.options['pixel_loss']:
+                    src_loss += K.mean( (100*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
+                else:
+                    src_loss += K.mean( (100*bg_style_power)*K.square(tf_dssim(2.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )))
+                    
             self.src_train = K.function ([warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm ],[src_loss], optimizer().get_updates(src_loss, src_loss_train_weights) )
             
-            dst_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked_ar[i], pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i] ) )) for i in range(len(target_dst_masked_ar)) ])
+            if self.options['pixel_loss']:
+                dst_loss = sum([ K.mean( 100*K.square( target_dst_masked_ar[i] - pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i]  )) for i in range(len(target_dst_masked_ar)) ])
+            else:
+                dst_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked_ar[i], pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i] ) )) for i in range(len(target_dst_masked_ar)) ])
+            
             self.dst_train = K.function ([warped_dst, target_dst, target_dstm ],[dst_loss], optimizer().get_updates(dst_loss, dst_loss_train_weights) )
    
             if self.options['learn_mask']: