diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 4dc8f72..ab7e75a 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -361,10 +361,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
 
                     gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm,  max(1, resolution // 32) )
+                    gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
                     gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
 
                     gpu_target_dst_masked      = gpu_target_dst*gpu_target_dstm_blur
-                    gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
+                    gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
 
                     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
@@ -372,8 +373,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
                     gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
 
-                    gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
-                    gpu_psd_target_dst_anti_masked = gpu_pred_src_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)
 
                     if resolution < 256:
                         gpu_src_loss =  tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
@@ -389,12 +390,12 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
                     face_style_power = self.options['face_style_power'] / 100.0
                     if face_style_power != 0 and not self.pretrain:
-                        gpu_src_loss += nn.style_loss(gpu_psd_target_dst_masked, gpu_target_dst_masked, gaussian_blur_radius=resolution//16, loss_weight=10000*face_style_power)
+                        gpu_src_loss += nn.style_loss(gpu_psd_target_dst_style_masked, gpu_target_dst_masked, gaussian_blur_radius=resolution//16, loss_weight=10000*face_style_power)
 
                     bg_style_power = self.options['bg_style_power'] / 100.0
                     if bg_style_power != 0 and not self.pretrain:
-                        gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim(gpu_psd_target_dst_anti_masked, gpu_target_dst_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
-                        gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square( gpu_psd_target_dst_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
+                        gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim( gpu_psd_target_dst_style_anti_masked,  gpu_target_dst_style_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                        gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square(gpu_psd_target_dst_style_anti_masked - gpu_target_dst_style_anti_masked), axis=[1,2,3] )
 
                     if resolution < 256:
                         gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])