diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 5a1e0ca..ce2086d 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -363,6 +363,9 @@ class SAEHDModel(ModelBase):
         self.gan_power = gan_power = self.options['gan_power'] if not self.pretrain else 0.0
 
         masked_training = self.options['masked_training']
+        ct_mode = self.options['ct_mode']
+        if ct_mode == 'none':
+            ct_mode = None
 
         models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
         models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
@@ -730,19 +733,19 @@ class SAEHDModel(ModelBase):
             training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
             training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
 
-            random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' and not self.pretrain else None
+            random_ct_samples_path=training_data_dst_path if ct_mode is not None and not self.pretrain else None
 
             cpu_count = min(multiprocessing.cpu_count(), 8)
             src_generators_count = cpu_count // 2
             dst_generators_count = cpu_count // 2
-            if self.options['ct_mode'] != 'none':
+            if ct_mode is not None:
                 src_generators_count = int(src_generators_count * 1.5)
 
             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),
-                        output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['random_warp'], 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': self.options['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': self.options['ct_mode'],                            'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
+                        output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['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.ALL_EYES_HULL, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                               ],
                         generators_count=src_generators_count ),
@@ -898,8 +901,6 @@ class SAEHDModel(ModelBase):
     #override
     def get_MergerConfig(self):
         import merger
-        return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type,
-                                     default_mode = 'overlay' if self.options['ct_mode'] != 'none' or self.options['face_style_power'] or self.options['bg_style_power'] else 'seamless',
-                                    )
+        return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
 
 Model = SAEHDModel
diff --git a/samplelib/SampleProcessor.py b/samplelib/SampleProcessor.py
index 1b4fc89..f735925 100644
--- a/samplelib/SampleProcessor.py
+++ b/samplelib/SampleProcessor.py
@@ -72,7 +72,7 @@ class SampleProcessor(object):
                 motion_blur    = opts.get('motion_blur', None)
                 gaussian_blur  = opts.get('gaussian_blur', None)
                 normalize_tanh = opts.get('normalize_tanh', False)
-                ct_mode        = opts.get('ct_mode', 'None')
+                ct_mode        = opts.get('ct_mode', None)
                 data_format    = opts.get('data_format', 'NHWC')
                 
                 if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK: