diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 7147b6f..66c5b45 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -258,9 +258,9 @@ class SAEModel(ModelBase):
                     src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights
 
             if not self.options['pixel_loss']:
-                src_loss_batch = sum([ (  10*K.square( dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i],  pred_src_src_masked_ar_opt[i] ) )) for i in range(len(target_src_masked_ar_opt)) ])
+                src_loss_batch = sum([ (  100*K.square( dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i],  pred_src_src_masked_ar_opt[i] ) )) for i in range(len(target_src_masked_ar_opt)) ])
             else:
-                src_loss_batch = sum([ K.mean ( 10*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
+                src_loss_batch = sum([ K.mean ( 100*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
 
             src_loss = K.mean(src_loss_batch)
 
@@ -272,15 +272,15 @@ class SAEModel(ModelBase):
             bg_style_power = self.options['bg_style_power'] / 100.0
             if bg_style_power != 0:
                 if not self.options['pixel_loss']:
-                    bg_loss = K.mean( (10*bg_style_power)*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )))
+                    bg_loss = K.mean( (100*bg_style_power)*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )))
                 else:
-                    bg_loss = K.mean( (10*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
+                    bg_loss = K.mean( (100*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
                 src_loss += bg_loss
 
             if not self.options['pixel_loss']:
-                dst_loss_batch = sum([ (  10*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_dst_masked_ar_opt[i],  pred_dst_dst_masked_ar_opt[i] ) )) for i in range(len(target_dst_masked_ar_opt)) ])
+                dst_loss_batch = sum([ (  100*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_dst_masked_ar_opt[i],  pred_dst_dst_masked_ar_opt[i] ) )) for i in range(len(target_dst_masked_ar_opt)) ])
             else:
-                dst_loss_batch = sum([ K.mean ( 10*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
+                dst_loss_batch = sum([ K.mean ( 100*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
 
             dst_loss = K.mean(dst_loss_batch)