Merge branch 'dev' into img2img-save

1 files changed, 10 insertions, 13 deletions

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 2fced999..02a1727d 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,15 +1,13 @@
-import os

+import sys

 

 import numpy as np

 import torch

 from PIL import Image

-from basicsr.utils.download_util import load_file_from_url

 

 import modules.esrgan_model_arch as arch

 from modules import modelloader, images, devices

-from modules.upscaler import Upscaler, UpscalerData

 from modules.shared import opts

-

+from modules.upscaler import Upscaler, UpscalerData

 

 

 def mod2normal(state_dict):

@@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)

             scalers.append(scaler_data)

         for file in model_paths:

-            if "http" in file:

+            if file.startswith("http"):

                 name = self.model_name

             else:

                 name = modelloader.friendly_name(file)

@@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler):
             self.scalers.append(scaler_data)

 

     def do_upscale(self, img, selected_model):

-        model = self.load_model(selected_model)

-        if model is None:

+        try:

+            model = self.load_model(selected_model)

+        except Exception as e:

+            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)

             return img

         model.to(devices.device_esrgan)

         img = esrgan_upscale(model, img)

         return img

 

     def load_model(self, path: str):

-        if "http" in path:

-            filename = load_file_from_url(

+        if path.startswith("http"):

+            # TODO: this doesn't use `path` at all?

+            filename = modelloader.load_file_from_url(

                 url=self.model_url,

                 model_dir=self.model_download_path,

                 file_name=f"{self.model_name}.pth",

-                progress=True,

             )

         else:

             filename = path

-        if not os.path.exists(filename) or filename is None:

-            print(f"Unable to load {self.model_path} from {filename}")

-            return None

 

         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)