diff --git a/models/ModelBase.py b/models/ModelBase.py
index f39c0e8..59cfd57 100644
--- a/models/ModelBase.py
+++ b/models/ModelBase.py
@@ -85,12 +85,13 @@ class ModelBase(object):
         else:
             self.options['write_preview_history'] = self.options.get('write_preview_history', False)
 
-        if ask_target_iter and (self.iter == 0 or ask_override):
-            self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
-        else:
-            self.options['target_iter'] = max(model_data.get('target_iter',0), self.options.get('target_epoch',0))
-            if 'target_epoch' in self.options:
-                self.options.pop('target_epoch')
+        if ask_target_iter:
+            if (self.iter == 0 or ask_override):
+                self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
+            else:
+                self.options['target_iter'] = max(model_data.get('target_iter',0), self.options.get('target_epoch',0))
+                if 'target_epoch' in self.options:
+                    self.options.pop('target_epoch')
 
         if ask_batch_size and (self.iter == 0 or ask_override):
             default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
@@ -98,20 +99,23 @@ class ModelBase(object):
         else:
             self.options['batch_size'] = self.options.get('batch_size', 0)
 
-        if ask_sort_by_yaw and (self.iter == 0):
-            self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
-        else:
-            self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
+        if ask_sort_by_yaw:
+            if (self.iter == 0):
+                self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
+            else:
+                self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
 
-        if ask_random_flip and (self.iter == 0):
-            self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
-        else:
-            self.options['random_flip'] = self.options.get('random_flip', True)
+        if ask_random_flip:
+            if (self.iter == 0):
+                self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
+            else:
+                self.options['random_flip'] = self.options.get('random_flip', True)
 
-        if ask_src_scale_mod and (self.iter == 0):
-            self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
-        else:
-            self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
+        if ask_src_scale_mod:
+            if (self.iter == 0):
+                self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
+            else:
+                self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
 
         self.write_preview_history = self.options['write_preview_history']
         if not self.options['write_preview_history']:
diff --git a/samplelib/SampleGeneratorImageTemporal.py b/samplelib/SampleGeneratorImageTemporal.py
index 2f3457c..d370bda 100644
--- a/samplelib/SampleGeneratorImageTemporal.py
+++ b/samplelib/SampleGeneratorImageTemporal.py
@@ -42,11 +42,13 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
         if samples_len == 0:
             raise ValueError('No training data provided.')
 
-        if samples_len - self.temporal_image_count < 0:
+        mult_max = 4
+        l = samples_len - (self.temporal_image_count-1)*mult_max + 1
+        if l < 0:
             raise ValueError('Not enough samples to fit temporal line.')
 
         shuffle_idxs = []
-        samples_sub_len = samples_len - self.temporal_image_count + 1
+        samples_sub_len = samples_len - l + 1
 
         while True:
 
@@ -60,9 +62,9 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
                 idx = shuffle_idxs.pop()
 
                 temporal_samples = []
-
+                mult = np.random.randint(mult_max)
                 for i in range( self.temporal_image_count ):
-                    sample = samples[ idx+i ]
+                    sample = samples[ idx+i*mult ]
                     try:
                         temporal_samples += SampleProcessor.process (sample, self.sample_process_options, self.output_sample_types, self.debug)
                     except: