diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index cfd67e2..cb89358 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -441,15 +441,12 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
 
                     gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
-                    gpu_target_dst_anti_masked = gpu_target_dst_style_anti_masked
-
                     gpu_target_src_masked_opt  = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
                     gpu_target_dst_masked_opt  = gpu_target_dst_masked if masked_training else gpu_target_dst
 
                     gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
                     gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
                     gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
-                    gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*(1.0-gpu_target_dstm_blur)
 
                     gpu_psd_target_dst_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur
                     gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
@@ -479,14 +476,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     if self.options['background_power'] > 0:
                         bg_factor = self.options['background_power']
                         if self.options['ms_ssim_loss']:
-                            gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_src_anti_masked, gpu_pred_src_src_anti_masked, max_val=1.0)
+                            gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
                         else:
                             if resolution < 256:
-                                gpu_src_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src_anti_masked, gpu_pred_src_src_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                                gpu_src_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
                             else:
-                                gpu_src_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src_anti_masked, gpu_pred_src_src_anti_masked, 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_anti_masked, gpu_pred_src_src_anti_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
-                        gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src_anti_masked - gpu_pred_src_src_anti_masked ), axis=[1,2,3])
+                                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_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
 
                     face_style_power = self.options['face_style_power'] / 100.0
                     if face_style_power != 0 and not self.pretrain:
@@ -521,14 +518,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     if self.options['background_power'] > 0:
                         bg_factor = self.options['background_power']
                         if self.options['ms_ssim_loss']:
-                            gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_dst_anti_masked, gpu_pred_dst_dst_anti_masked, max_val=1.0)
+                            gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
                         else:
                             if resolution < 256:
-                                gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_anti_masked, gpu_pred_dst_dst_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                                gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
                             else:
-                                gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_anti_masked, gpu_pred_dst_dst_anti_masked, 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_anti_masked, gpu_pred_dst_dst_anti_masked, 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_anti_masked - gpu_pred_dst_dst_anti_masked ), axis=[1,2,3])
+                                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_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
 
                     gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )