Merge branch 'dev' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into tomesd

1 files changed, 10 insertions, 11 deletions

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 9a9c38f1..a009eb42 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -6,7 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url

 

 import modules.esrgan_model_arch as arch

-from modules import shared, modelloader, images, devices

+from modules import modelloader, images, devices

 from modules.upscaler import Upscaler, UpscalerData

 from modules.shared import opts

 

@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer

     if 'conv_first.weight' in state_dict:

         crt_net = {}

-        items = []

-        for k, v in state_dict.items():

-            items.append(k)

+        items = list(state_dict)

 

         crt_net['model.0.weight'] = state_dict['conv_first.weight']

         crt_net['model.0.bias'] = state_dict['conv_first.bias']

@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:

         re8x = 0

         crt_net = {}

-        items = []

-        for k, v in state_dict.items():

-            items.append(k)

+        items = list(state_dict)

 

         crt_net['model.0.weight'] = state_dict['conv_first.weight']

         crt_net['model.0.bias'] = state_dict['conv_first.bias']

@@ -156,13 +152,16 @@ class UpscalerESRGAN(Upscaler):
 

     def load_model(self, path: str):

         if "http" in path:

-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,

-                                          file_name="%s.pth" % self.model_name,

-                                          progress=True)

+            filename = load_file_from_url(

+                url=self.model_url,

+                model_dir=self.model_path,

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

+                progress=True,

+            )

         else:

             filename = path

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

-            print("Unable to load %s from %s" % (self.model_path, filename))

+            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)