diff --git a/facelib/FANSegmentator.py b/facelib/FANSegmentator.py
index 54ae673..d84f23d 100644
--- a/facelib/FANSegmentator.py
+++ b/facelib/FANSegmentator.py
@@ -3,6 +3,7 @@ import os
 import cv2
 from pathlib import Path
 from nnlib import nnlib
+from interact import interact as io
 
 class FANSegmentator(object):
     def __init__ (self, resolution, face_type_str, load_weights=True, weights_file_root=None):
@@ -19,7 +20,15 @@ class FANSegmentator(object):
         
         if load_weights:
             self.model.load_weights (str(self.weights_path))
-            
+        else:
+            io.log_info ("Initializing CA weights...")
+            conv_weights_list = []
+            for layer in self.model.layers:
+                if type(layer) == Conv2D:
+                    conv_weights_list += [layer.weights[0]]  # Conv2D kernel_weights
+            CAInitializerMP(conv_weights_list)
+            self.model.compile(loss='mse', optimizer=Adam(tf_cpu_mode=2))
+
     def __enter__(self):
         return self
         
@@ -43,7 +52,6 @@ class FANSegmentator(object):
         x = FANSegmentator.EncFlow(ngf=ngf)(x)
         x = FANSegmentator.DecFlow(ngf=ngf)(x)
         model = Model(inp,x)
-        model.compile (loss='mse', optimizer=Adam(tf_cpu_mode=2) )
         return model
         
     @staticmethod
diff --git a/models/Model_FANSegmentator/Model.py b/models/Model_FANSegmentator/Model.py
index 5b3918a..a1baff1 100644
--- a/models/Model_FANSegmentator/Model.py
+++ b/models/Model_FANSegmentator/Model.py
@@ -37,13 +37,13 @@ class Model(ModelBase):
             self.set_training_data_generators ([    
                     SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, 
                             sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ), 
-                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution],
+                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution],
                                                   [f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
                                                 ]),
                                                 
                     SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, 
                             sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ), 
-                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution]
+                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution]
                                                 ])
                                                ])