diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 392002c..ee45c55 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -51,11 +51,11 @@ class SAEModel(ModelBase):
             self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1)
 
         if is_first_run:
-            self.options['archi'] = io.input_str ("AE architecture (df, liae ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes.").lower()
+            self.options['archi'] = io.input_str ("AE architecture (df, liae, df-s, liae-s ?:help skip:%s) : " % (default_archi) , default_archi, ['df','df-s','liae','liae-s'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. -s version is slower, but has decreased change to collapse.").lower()
         else:
             self.options['archi'] = self.options.get('archi', default_archi)
 
-        default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
+        default_ae_dims = 256 if 'liae' in self.options['archi'] else 512
         default_e_ch_dims = 42
         default_d_ch_dims = default_e_ch_dims // 2
 
@@ -125,13 +125,18 @@ class SAEModel(ModelBase):
         target_dst_ar  = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
         target_dstm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
 
-        padding = 'reflect' if self.options['remove_gray_border'] else 'zero'
+        padding = 'zero'
+        norm = ''
+        
+        if '-s' in self.options['archi']:
+            norm = 'bn'
+        
         common_flow_kwargs = { 'padding': padding,
-                               'norm': 'bn',
+                               'norm': norm,
                                'act':'' }
 
         weights_to_load = []
-        if self.options['archi'] == 'liae':
+        if 'liae' in self.options['archi']:
             self.encoder = modelify(SAEModel.LIAEEncFlow(resolution, ch_dims=e_ch_dims, **common_flow_kwargs)  ) (Input(bgr_shape))
 
             enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
@@ -175,7 +180,7 @@ class SAEModel(ModelBase):
                 pred_dst_dstm = self.decoderm(warped_dst_inter_code)
                 pred_src_dstm = self.decoderm(warped_src_dst_inter_code)
 
-        elif self.options['archi'] == 'df':
+        elif 'df' in self.options['archi']:
             self.encoder = modelify(SAEModel.DFEncFlow(resolution, ae_dims=ae_dims, ch_dims=e_ch_dims, **common_flow_kwargs)  ) (Input(bgr_shape))
 
             dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
@@ -248,7 +253,7 @@ class SAEModel(ModelBase):
             self.src_dst_opt      = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
             self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
 
-            if self.options['archi'] == 'liae':
+            if 'liae' in self.options['archi']:
                 src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
                 if self.options['learn_mask']:
                     src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights
@@ -344,7 +349,7 @@ class SAEModel(ModelBase):
                    [self.src_dst_mask_opt, 'src_dst_mask_opt']
                  ]
         ar = []
-        if self.options['archi'] == 'liae':
+        if 'liae' in self.options['archi']:
             ar += [[self.encoder, 'encoder.h5'],
                   [self.inter_B, 'inter_B.h5'],
                   [self.inter_AB, 'inter_AB.h5'],
@@ -352,7 +357,7 @@ class SAEModel(ModelBase):
                  ]
             if self.options['learn_mask']:
                  ar += [ [self.decoderm, 'decoderm.h5'] ]
-        elif self.options['archi'] == 'df':
+        elif 'df' in self.options['archi']:
            ar += [[self.encoder, 'encoder.h5'],
                  [self.decoder_src, 'decoder_src.h5'],
                  [self.decoder_dst, 'decoder_dst.h5']