New script:

5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat
Copies faces containing XSeg polygons to aligned_xseg\ dir.
Useful only if you want to collect labeled faces and reuse them in other fakes.

Now you can use trained XSeg mask in the SAEHD training process.
It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model.
use
5.XSeg.optional) trained mask for data_dst/data_src - apply.bat
5.XSeg.optional) trained mask for data_dst/data_src - remove.bat

Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions.

XSeg trainer : now you can choose type of face
XSeg trainer : now you can restart training in “override settings”
Merger: XSeg-* modes now can be used with all types of faces.

Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed,
because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.

models/Model_XSeg/Model.py

@@ -7,7 +7,7 @@ import numpy as np
 from core import mathlib
 from core.interact import interact as io
 from core.leras import nn
-from facelib import FaceType, TernausNet, XSegNet
+from facelib import FaceType, XSegNet
 from models import ModelBase
 from samplelib import *
 
@@ -20,6 +20,19 @@ class XSegModel(ModelBase):
     def on_initialize_options(self):
         self.set_batch_size(4)   
         
+        ask_override = self.ask_override()
+        
+        default_face_type          = self.options['face_type']          = self.load_or_def_option('face_type', 'wf')
+                   
+        if not self.is_first_run() and ask_override:     
+            self.restart_training = io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch.")
+        else:
+            self.restart_training = False
+
+        if self.is_first_run():
+            self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf'], help_message="Half / mid face / full face / whole face. Choose the same as your deepfake model.").lower()
+           
+        
     #override
     def on_initialize(self):
         device_config = nn.getCurrentDeviceConfig()
@@ -31,7 +44,14 @@ class XSegModel(ModelBase):
         devices = device_config.devices
 
         self.resolution = resolution = 256
-        self.face_type = FaceType.WHOLE_FACE
+        
+        if self.restart_training:
+            self.set_iter(0)
+        
+        self.face_type = {'h'  : FaceType.HALF,
+                          'mf' : FaceType.MID_FULL,
+                          'f'  : FaceType.FULL,
+                          'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
         
         place_model_on_cpu = len(devices) == 0
         models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
@@ -40,7 +60,7 @@ class XSegModel(ModelBase):
         mask_shape = nn.get4Dshape(resolution,resolution,1)
  
         # Initializing model classes
-        self.model = XSegNet(name=f'XSeg', 
+        self.model = XSegNet(name='XSeg', 
                                resolution=resolution, 
                                load_weights=not self.is_first_run(),
                                weights_file_root=self.get_model_root_path(),