diff --git a/models/Model_Quick96/Model.py b/models/Model_Quick96/Model.py
index 7ac3f0d..dba5738 100644
--- a/models/Model_Quick96/Model.py
+++ b/models/Model_Quick96/Model.py
@@ -13,6 +13,13 @@ from samplelib import *
 from pathlib import Path
 
 class QModel(ModelBase):
+    #override
+    def on_initialize_options(self):
+        ask_override = False if self.read_from_conf else self.ask_override()
+        if self.is_first_run() or ask_override:
+            if (self.read_from_conf and not self.config_file_exists) or not self.read_from_conf:
+                self.ask_batch_size()
+
     #override
     def on_initialize(self):
         device_config = nn.getCurrentDeviceConfig()
@@ -82,7 +89,7 @@ class QModel(ModelBase):
         if self.is_training:
             # Adjust batch size for multiple GPU
             gpu_count = max(1, len(devices) )
-            bs_per_gpu = max(1, 4 // gpu_count)
+            bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
             self.set_batch_size( gpu_count*bs_per_gpu)
 
             # Compute losses per GPU
diff --git a/models/Model_XSeg/Model.py b/models/Model_XSeg/Model.py
index b6e875f..4f536cf 100644
--- a/models/Model_XSeg/Model.py
+++ b/models/Model_XSeg/Model.py
@@ -20,7 +20,7 @@ class XSegModel(ModelBase):
 
     #override
     def on_initialize_options(self):
-        ask_override = self.ask_override()
+        ask_override = False if self.read_from_conf else self.ask_override()
 
         if not self.is_first_run() and ask_override:
             if io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch."):
@@ -30,11 +30,13 @@ class XSegModel(ModelBase):
         default_pretrain           = self.options['pretrain']           = self.load_or_def_option('pretrain', False)
 
         if self.is_first_run():
-            self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Choose the same as your deepfake model.").lower()
+            if (self.read_from_conf and not self.config_file_exists) or not self.read_from_conf:
+                self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Choose the same as your deepfake model.").lower()
 
         if self.is_first_run() or ask_override:
-            self.ask_batch_size(4, range=[2,16])
-            self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain)
+            if (self.read_from_conf and not self.config_file_exists) or not self.read_from_conf:
+                self.ask_batch_size(4, range=[2,16])
+                self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain)
         
         if not self.is_exporting and (self.options['pretrain'] and self.get_pretraining_data_path() is None):
             raise Exception("pretraining_data_path is not defined")
@@ -53,13 +55,11 @@ class XSegModel(ModelBase):
 
         self.resolution = resolution = 256
 
-
         self.face_type = {'h'  : FaceType.HALF,
                           'mf' : FaceType.MID_FULL,
                           'f'  : FaceType.FULL,
                           'wf' : FaceType.WHOLE_FACE,
                           'head' : FaceType.HEAD}[ self.options['face_type'] ]
-        
             
         place_model_on_cpu = len(devices) == 0
         models_opt_device = '/CPU:0' if place_model_on_cpu else nn.tf_default_device_name
@@ -287,4 +287,4 @@ class XSegModel(ModelBase):
         config_path = Path(__file__).parent.absolute() / Path("config_schema.json")
         return config_path
                 
-Model = XSegModel
\ No newline at end of file
+Model = XSegModel