initial commit

modules/gfpgan_model.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import logging
+import os
+
+import torch
+
+from modules import (
+    devices,
+    errors,
+    face_restoration,
+    face_restoration_utils,
+    modelloader,
+    shared,
+)
+
+logger = logging.getLogger(__name__)
+model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
+model_download_name = "GFPGANv1.4.pth"
+gfpgan_face_restorer: face_restoration.FaceRestoration | None = None
+
+
+class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
+    def name(self):
+        return "GFPGAN"
+
+    def get_device(self):
+        return devices.device_gfpgan
+
+    def load_net(self) -> torch.Module:
+        for model_path in modelloader.load_models(
+            model_path=self.model_path,
+            model_url=model_url,
+            command_path=self.model_path,
+            download_name=model_download_name,
+            ext_filter=['.pth'],
+        ):
+            if 'GFPGAN' in os.path.basename(model_path):
+                return modelloader.load_spandrel_model(
+                    model_path,
+                    device=self.get_device(),
+                    expected_architecture='GFPGAN',
+                ).model
+
+        #   if reach here, model not found. previous code will download it iff there are no models in GFPGAN directory
+        #   this will download it if the supporting models exist
+        try:
+            GFPGANmodel = modelloader.load_file_from_url(model_url, model_dir=self.model_path, file_name=model_download_name)
+            return modelloader.load_spandrel_model(
+                GFPGANmodel,
+                device=self.get_device(),
+                expected_architecture='GFPGAN',
+            ).model
+        except:
+            raise ValueError("No GFPGAN model found")
+
+    def restore(self, np_image):
+        def restore_face(cropped_face_t):
+            assert self.net is not None
+            return self.net(cropped_face_t, return_rgb=False)[0]
+
+        return self.restore_with_helper(np_image, restore_face)
+
+
+def gfpgan_fix_faces(np_image):
+    if gfpgan_face_restorer:
+        return gfpgan_face_restorer.restore(np_image)
+    logger.warning("GFPGAN face restorer not set up")
+    return np_image
+
+
+def setup_model(dirname: str) -> None:
+    global gfpgan_face_restorer
+
+    try:
+        face_restoration_utils.patch_facexlib(dirname)
+        gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname)
+        shared.face_restorers.append(gfpgan_face_restorer)
+    except Exception:
+        errors.report("Error setting up GFPGAN", exc_info=True)