Set device for facelib/facexlib and gfpgan

* FaceXLib/FaceLib doesn't pass the device argument to RetinaFace but instead chooses one itself and sets it to a global - in order to use a device other than its internally chosen default it is necessary to manually replace the default value
* The GFPGAN constructor needs the device argument to work with MPS or a CUDA device ID that differs from the default

modules/codeformer_model.py

@@ -36,6 +36,7 @@ def setup_model(dirname):
         from basicsr.utils.download_util import load_file_from_url
         from basicsr.utils import imwrite, img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
+        from facelib.detection.retinaface import retinaface
         from modules.shared import cmd_opts
 
         net_class = CodeFormer
@@ -65,6 +66,8 @@ def setup_model(dirname):
                 net.load_state_dict(checkpoint)
                 net.eval()
 
+                if hasattr(retinaface, 'device'):
+                    retinaface.device = devices.device_codeformer
                 face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
 
                 self.net = net

modules/gfpgan_model.py

@@ -36,7 +36,9 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    if hasattr(facexlib.detection.retinaface, 'device'):
+        facexlib.detection.retinaface.device = devices.device_gfpgan
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model