diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index efa5a0f..aa854de 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -302,32 +302,34 @@ class SAEModel(ModelBase):
         warped_src, target_src, target_src_mask, src_sample_idxs = generators_samples[0]
         warped_dst, target_dst, target_dst_mask, dst_sample_idxs = generators_samples[1]
 
-        dssim_pixel_alpha = np.clip ( self.epoch / 15000.0, 0.0, 1.0 )          #smooth transition between DSSIM and MSE in 15k epochs
+        dssim_pixel_alpha = np.clip ( (self.epoch - 5000) / 15000.0, 0.0, 1.0 ) #smooth transition between DSSIM and MSE in 5-20k epochs
         dssim_pixel_alpha = np.repeat( dssim_pixel_alpha, (self.batch_size,) )
         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])
         
-        #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) ] )[:,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) ] )[:,0] ]).astype(np.uint)
-            generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
+        # '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) ] )[:,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) ] )[:,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])
diff --git a/samples/SampleGeneratorFace.py b/samples/SampleGeneratorFace.py
index 92788d8..13358fa 100644
--- a/samples/SampleGeneratorFace.py
+++ b/samples/SampleGeneratorFace.py
@@ -54,6 +54,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
         generator = self.generators[self.generator_counter % len(self.generators) ]
         return next(generator)
     
+    #forces to repeat these sample idxs as fast as possible
     def repeat_sample_idxs(self, idxs): # [ idx, ... ]
         #send idxs list to all sub generators.
         for gen_sq in self.generators_sq: