Use Spandrel for upscaling and face restoration architectures

.github/workflows/run_tests.yaml

@@ -20,6 +20,12 @@ jobs:
           cache-dependency-path: |
             **/requirements*txt
             launch.py
+      - name: Cache models
+        id: cache-models
+        uses: actions/cache@v3
+        with:
+          path: models
+          key: "2023-12-30"
       - name: Install test dependencies
         run: pip install wait-for-it -r requirements-test.txt
         env:
@@ -33,6 +39,8 @@ jobs:
           TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
           WEBUI_LAUNCH_LIVE_OUTPUT: "1"
           PYTHONUNBUFFERED: "1"
+      - name: Print installed packages
+        run: pip freeze
       - name: Start test server
         run: >
           python -m coverage run

.gitignore

@@ -37,3 +37,4 @@ notification.mp3
 /node_modules
 /package-lock.json
 /.coverage*
+/test/test_outputs

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -7,9 +7,7 @@
 
 import modules.upscaler
 from modules import devices, modelloader, script_callbacks, errors
-from scunet_model_arch import SCUNet
 
-from modules.modelloader import load_file_from_url
 from modules.shared import opts
 
 
@@ -120,17 +118,10 @@ def load_model(self, path: str):
         device = devices.get_device_for('scunet')
         if path.startswith("http"):
             # TODO: this doesn't use `path` at all?
-            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
+            filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for _, v in model.named_parameters():
-            v.requires_grad = False
-        model = model.to(device)
-
-        return model
+        return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet')
 
 
 def on_ui_settings():