diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 14fba1a..73569e6 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -305,29 +305,6 @@ class SAEModel(ModelBase):
         dssim_pixel_alpha = np.expand_dims(dssim_pixel_alpha,-1)
 
         src_loss, dst_loss, src_sample_losses, dst_sample_losses = self.src_dst_train ([dssim_pixel_alpha, warped_src, target_src, target_src_mask, warped_dst, target_dst, target_dst_mask])
-        
-        # 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
-        #
-        ##gathering array of sample_losses
-        #self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
-        #self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
-        #
-        #if len(self.src_sample_losses) >= 48: #array is big enough
-        #    #fetching idxs which losses are bigger than average
-        #    x = np.array (self.src_sample_losses)
-        #    self.src_sample_losses = []
-        #    b = x[:,1]
-        #    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
-        #    generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
-        #    
-        #    
-        #if len(self.dst_sample_losses) >= 48: #array is big enough
-        #    #fetching idxs which losses are bigger than average
-        #    x = np.array (self.dst_sample_losses)
-        #    self.dst_sample_losses = []
-        #    b = x[:,1]
-        #    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
-        #    generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
             
         if self.options['learn_mask']:
             src_mask_loss, dst_mask_loss, = self.src_dst_mask_train ([warped_src, target_src_mask, warped_dst, target_dst_mask])
@@ -574,4 +551,30 @@ class SAEModel(ModelBase):
             
         return func
         
-Model = SAEModel
\ No newline at end of file
+Model = SAEModel
+
+
+        
+# 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
+#
+##gathering array of sample_losses
+#self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
+#self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
+#
+#if len(self.src_sample_losses) >= 128: #array is big enough
+#    #fetching idxs which losses are bigger than average
+#    x = np.array (self.src_sample_losses)
+#    self.src_sample_losses = []
+#    b = x[:,1]
+#    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
+#    generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
+#    print ("src repeated %d" % (len(idxs)) )
+#    
+#if len(self.dst_sample_losses) >= 128: #array is big enough
+#    #fetching idxs which losses are bigger than average
+#    x = np.array (self.dst_sample_losses)
+#    self.dst_sample_losses = []
+#    b = x[:,1]
+#    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
+#    generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
+#    print ("dst repeated %d" % (len(idxs)) )
\ No newline at end of file
diff --git a/samples/SampleGeneratorFace.py b/samples/SampleGeneratorFace.py
index 13358fa..43181bf 100644
--- a/samples/SampleGeneratorFace.py
+++ b/samples/SampleGeneratorFace.py
@@ -55,6 +55,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
         return next(generator)
     
     #forces to repeat these sample idxs as fast as possible
+    #currently unused
     def repeat_sample_idxs(self, idxs): # [ idx, ... ]
         #send idxs list to all sub generators.
         for gen_sq in self.generators_sq: