diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index a755d3d..9b647af 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -62,7 +62,7 @@ class SAEHDModel(ModelBase):
             resolution = np.clip ( (resolution // 16) * 16, 64, 512)
             self.options['resolution'] = resolution
             self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf'], help_message="Half / mid face / full face / whole face. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead, but requires manual merge in Adobe After Effects.").lower()
-            self.options['archi'] = io.input_str ("AE architecture", default_archi, ['dfhd','liaehd','df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'hd' is heavyweight version for the best quality.").lower()
+            self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd','liaech'], help_message="'df' keeps faces more natural.\n'liae' can fix overly different face shapes.\n'hd' are experimental versions.\n'liaech' - new experimental model by @chervoniy. Based on liae, but produces more src-like face.").lower()
 
         default_d_dims             = 48 if self.options['archi'] == 'dfhd' else 64
         default_d_dims             = self.options['d_dims']             = self.load_or_def_option('d_dims', default_d_dims)
@@ -125,8 +125,6 @@ class SAEHDModel(ModelBase):
         nn.initialize(data_format=self.model_data_format)
         tf = nn.tf
 
-        Encoder, Inter, Decoder = nn.get_ae_models()
-  
         device_config = nn.getCurrentDeviceConfig()
         devices = device_config.devices
 
@@ -163,8 +161,6 @@ class SAEHDModel(ModelBase):
         output_ch = 3
         bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
         mask_shape = nn.get4Dshape(resolution,resolution,1)
-        lowest_dense_res = resolution // 16
-
         self.model_filename_list = []
 
 
@@ -180,12 +176,18 @@ class SAEHDModel(ModelBase):
             self.target_dstm_all = tf.placeholder (nn.tf_floatx, mask_shape)
             
         # Initializing model classes
+        if archi == 'liaech':
+            lowest_dense_res, Encoder, Inter, Decoder = nn.get_ae_models_chervoniy(resolution)
+        else:
+            lowest_dense_res, Encoder, Inter, Decoder = nn.get_ae_models(resolution)
+  
+        
         with tf.device (models_opt_device):
             if 'df' in archi:
                 self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
                 encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
 
-                self.inter = Inter (in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims, is_hd=is_hd, name='inter')
+                self.inter = Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, is_hd=is_hd, name='inter')
                 inter_out_ch = self.inter.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
 
                 self.decoder_src = Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder_src')
@@ -205,8 +207,8 @@ class SAEHDModel(ModelBase):
                 self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
                 encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
 
-                self.inter_AB = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_AB')
-                self.inter_B  = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_B')
+                self.inter_AB = Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_AB')
+                self.inter_B  = Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_B')
 
                 inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
                 inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))