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.

facelib/XSegNet.py

@@ -14,20 +14,22 @@ class XSegNet(object):
     VERSION = 1
 
     def __init__ (self, name, 
-                        resolution, 
+                        resolution=256, 
                         load_weights=True, 
                         weights_file_root=None, 
                         training=False, 
                         place_model_on_cpu=False, 
                         run_on_cpu=False, 
                         optimizer=None, 
-                        data_format="NHWC"):
-                        
+                        data_format="NHWC",
+                        raise_on_no_model_files=False):
+                
+        self.resolution = resolution
+        self.weights_file_root = Path(weights_file_root) if weights_file_root is not None else Path(__file__).parent
+        
         nn.initialize(data_format=data_format)
         tf = nn.tf
 
-        self.weights_file_root = Path(weights_file_root) if weights_file_root is not None else Path(__file__).parent
-
         with tf.device ('/CPU:0'):
             #Place holders on CPU
             self.input_t  = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,3) )
@@ -62,11 +64,17 @@ class XSegNet(object):
             do_init = not load_weights
 
             if not do_init:
-                do_init = not model.load_weights( self.weights_file_root / filename )
+                model_file_path = self.weights_file_root / filename
+                do_init = not model.load_weights( model_file_path )
+                if do_init and raise_on_no_model_files:
+                    raise Exception(f'{model_file_path} does not exists.')
 
             if do_init:
                 model.init_weights()
-
+    
+    def get_resolution(self):
+        return self.resolution
+        
     def flow(self, x):
         return self.model(x)
 
@@ -78,7 +86,7 @@ class XSegNet(object):
             model.save_weights( self.weights_file_root / filename )
 
     def extract (self, input_image):
-        input_shape_len = len(input_image.shape)
+        input_shape_len = len(input_image.shape)            
         if input_shape_len == 3:
             input_image = input_image[None,...]