Merge branch 'dev' into patch-1

1 files changed, 14 insertions, 21 deletions

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index f4369257..02a1727d 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,24 +1,20 @@
-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 shared, modelloader, images, devices

-from modules.upscaler import Upscaler, UpscalerData

+from modules import modelloader, images, devices

 from modules.shared import opts

-

+from modules.upscaler import Upscaler, UpscalerData

 

 

 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 +48,7 @@ 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']

@@ -138,7 +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)

@@ -147,26 +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_path,

+                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)