diff --git a/models/ModelBase.py b/models/ModelBase.py
index 3cb88c5..ce219ab 100644
--- a/models/ModelBase.py
+++ b/models/ModelBase.py
@@ -185,7 +185,9 @@ class ModelBase(object):
         self.write_preview_history = self.options.get('write_preview_history', False)
         self.target_iter = self.options.get('target_iter',0)
         self.random_flip = self.options.get('random_flip',True)
-
+        self.random_src_flip = self.options.get('random_src_flip', False)
+        self.random_dst_flip = self.options.get('random_dst_flip', True)
+        
         self.on_initialize()
         self.options['batch_size'] = self.batch_size
 
@@ -297,6 +299,14 @@ class ModelBase(object):
     def ask_random_flip(self):
         default_random_flip = self.load_or_def_option('random_flip', True)
         self.options['random_flip'] = io.input_bool("Flip faces randomly", default_random_flip, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
+    
+    def ask_random_src_flip(self):
+        default_random_src_flip = self.load_or_def_option('random_src_flip', False)
+        self.options['random_src_flip'] = io.input_bool("Flip SRC faces randomly", default_random_src_flip, help_message="Random horizontal flip SRC faceset. Covers more angles, but the face may look less naturally.")
+
+    def ask_random_dst_flip(self):
+        default_random_dst_flip = self.load_or_def_option('random_dst_flip', True)
+        self.options['random_dst_flip'] = io.input_bool("Flip DST faces randomly", default_random_dst_flip, help_message="Random horizontal flip DST faceset. Makes generalization of src->dst better, if src random flip is not enabled.")
 
     def ask_batch_size(self, suggest_batch_size=None, range=None):
         default_batch_size = self.load_or_def_option('batch_size', suggest_batch_size or self.batch_size)
diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 0ef99a6..2772870 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -65,7 +65,8 @@ class SAEHDModel(ModelBase):
             self.ask_autobackup_hour()
             self.ask_write_preview_history()
             self.ask_target_iter()
-            self.ask_random_flip()
+            self.ask_random_src_flip()
+            self.ask_random_dst_flip()
             self.ask_batch_size(suggest_batch_size)
 
         if self.is_first_run():
@@ -630,7 +631,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             self.set_training_data_generators ([
                     SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=self.random_src_flip),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode,                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
@@ -640,7 +641,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                         generators_count=src_generators_count ),
 
                     SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=self.random_dst_flip),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                                'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                                'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False                      , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},