added FacesetRelighter:

Synthesize new faces from existing ones by relighting them using DeepPortraitRelighter network.
With the relighted faces neural network will better reproduce face shadows.

Therefore you can synthsize shadowed faces from fully lit faceset.
https://i.imgur.com/wxcmQoi.jpg

as a result, better fakes on dark faces:
https://i.imgur.com/5xXIbz5.jpg

in OpenCL build Relighter runs on CPU,

install pytorch directly via pip install, look at requirements

nnlib/nnlib.py

@@ -20,6 +20,9 @@ class nnlib(object):
 
     dlib = None
 
+    torch = None
+    torch_device = None
+
     keras = None
     keras_contrib = None
 
@@ -128,7 +131,27 @@ UNet = nnlib.UNet
 UNetTemporalPredictor = nnlib.UNetTemporalPredictor
 NLayerDiscriminator = nnlib.NLayerDiscriminator
 """
+    @staticmethod
+    def import_torch(device_config=None):
+        if nnlib.torch is not None:
+            return
 
+        if device_config is None:
+            device_config = nnlib.active_DeviceConfig
+        else:
+            nnlib.active_DeviceConfig = device_config
+
+        if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
+            os.environ.pop('CUDA_VISIBLE_DEVICES')
+
+        import torch
+        nnlib.torch = torch
+
+        if device_config.cpu_only or device_config.backend == 'plaidML':
+            nnlib.torch_device = torch.device(type='cpu')
+        else:
+            nnlib.torch_device = torch.device(type='cuda', index=device_config.gpu_idxs[0] )
+            torch.cuda.set_device(nnlib.torch_device)
 
     @staticmethod
     def _import_tf(device_config):
@@ -634,7 +657,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 reduction_axes = list(range(len(input_shape)))
                 del reduction_axes[self.axis]
                 del reduction_axes[0]
-                
+
                 broadcast_shape = [1] * len(input_shape)
                 broadcast_shape[self.axis] = input_shape[self.axis]
                 mean = K.mean(x, reduction_axes, keepdims=True)
@@ -912,7 +935,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 base_config = super(Adam, self).get_config()
                 return dict(list(base_config.items()) + list(config.items()))
         nnlib.Adam = Adam
-        
+
         class DenseMaxout(keras.layers.Layer):
             """A dense maxout layer.
             A `MaxoutDense` layer takes the element-wise maximum of
@@ -1039,7 +1062,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 base_config = super(DenseMaxout, self).get_config()
                 return dict(list(base_config.items()) + list(config.items()))
         nnlib.DenseMaxout = DenseMaxout
-        
+
         def CAInitializerMP( conv_weights_list ):
             #Convolution Aware Initialization https://arxiv.org/abs/1702.06295
             data = [ (i, K.int_shape(conv_weights)) for i, conv_weights in enumerate(conv_weights_list) ]