Be more clear about Spandrel model nomenclature and types

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -71,18 +71,18 @@ def load_model(self, path, scale=4):
         else:
             filename = path
 
-        model = modelloader.load_spandrel_model(
+        model_descriptor = modelloader.load_spandrel_model(
             filename,
             device=self._get_device(),
             dtype=devices.dtype,
             expected_architecture="SwinIR",
         )
         if getattr(opts, 'SWIN_torch_compile', False):
             try:
-                model = torch.compile(model)
+                model_descriptor.model.compile()
             except Exception:
                 logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
-        return model
+        return model_descriptor
 
     def _get_device(self):
         return devices.get_device_for('swinir')

modules/gfpgan_model.py

@@ -3,6 +3,8 @@
 import logging
 import os
 
+import torch
+
 from modules import (
     devices,
     errors,
@@ -25,7 +27,7 @@ def name(self):
     def get_device(self):
         return devices.device_gfpgan
 
-    def load_net(self) -> None:
+    def load_net(self) -> torch.Module:
         for model_path in modelloader.load_models(
             model_path=self.model_path,
             model_url=model_url,
@@ -34,13 +36,13 @@ def load_net(self) -> None:
             ext_filter=['.pth'],
         ):
             if 'GFPGAN' in os.path.basename(model_path):
-                net = modelloader.load_spandrel_model(
+                model = modelloader.load_spandrel_model(
                     model_path,
                     device=self.get_device(),
                     expected_architecture='GFPGAN',
                 ).model
-                net.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
-                return net
+                model.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
+                return model
         raise ValueError("No GFPGAN model found")
 
     def restore(self, np_image):

modules/modelloader.py

@@ -1,15 +1,18 @@
 from __future__ import annotations
 
+import importlib
 import logging
 import os
-import importlib
+from typing import TYPE_CHECKING
 from urllib.parse import urlparse
 
 import torch
 
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
 
+if TYPE_CHECKING:
+    import spandrel
 
 logger = logging.getLogger(__name__)
 
@@ -140,19 +143,19 @@ def load_spandrel_model(
     *,
     device: str | torch.device | None,
     half: bool = False,
-    dtype: str | None = None,
+    dtype: str | torch.dtype | None = None,
     expected_architecture: str | None = None,
-):
+) -> spandrel.ModelDescriptor:
     import spandrel
-    model = spandrel.ModelLoader(device=device).load_from_file(path)
-    if expected_architecture and model.architecture != expected_architecture:
+    model_descriptor = spandrel.ModelLoader(device=device).load_from_file(path)
+    if expected_architecture and model_descriptor.architecture != expected_architecture:
         logger.warning(
-            f"Model {path!r} is not a {expected_architecture!r} model (got {model.architecture!r})",
+            f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})",
         )
     if half:
-        model = model.model.half()
+        model_descriptor.model.half()
     if dtype:
-        model = model.model.to(dtype=dtype)
-    model.eval()
-    logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", model, path, device, half, dtype)
-    return model
+        model_descriptor.model.to(dtype=dtype)
+    model_descriptor.model.eval()
+    logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", model_descriptor, path, device, half, dtype)
+    return model_descriptor

modules/realesrgan_model.py

@@ -36,14 +36,14 @@ def do_upscale(self, img, path):
             errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
-        mod = modelloader.load_spandrel_model(
+        model_descriptor = modelloader.load_spandrel_model(
             info.local_data_path,
             device=self.device,
             half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
             expected_architecture="ESRGAN",  # "RealESRGAN" isn't a specific thing for Spandrel
         )
         return upscale_with_model(
-            mod,
+            model_descriptor,
             img,
             tile_size=opts.ESRGAN_tile,
             tile_overlap=opts.ESRGAN_tile_overlap,

modules/upscaler_utils.py

@@ -6,7 +6,7 @@
 import tqdm
 from PIL import Image
 
-from modules import devices, images
+from modules import images
 
 logger = logging.getLogger(__name__)