diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 2772870..79329ff 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -170,7 +170,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
             self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
 
-            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
+            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly. Forces random_warp=Y, random_flips=Y, gan_power=0.0, lr_dropout=N, styles=0.0, uniform_yaw=Y")
 
         if self.options['pretrain'] and self.get_pretraining_data_path() is None:
             raise Exception("pretraining_data_path is not defined")
@@ -220,7 +220,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
         self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
         random_warp = False if self.pretrain else self.options['random_warp']
-
+        random_src_flip = self.random_src_flip if not self.pretrain else True
+        random_dst_flip = self.random_dst_flip if not self.pretrain else True
+        
         if self.pretrain:
             self.options_show_override['gan_power'] = 0.0
             self.options_show_override['random_warp'] = False
@@ -631,7 +633,7 @@ 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_src_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=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},
@@ -641,7 +643,7 @@ 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_dst_flip),
+                        sample_process_options=SampleProcessor.Options(random_flip=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},