Re-implement universal model loading

modules/esrgan_model.py

@@ -5,15 +5,35 @@ import traceback
 import numpy as np
 import torch
 from PIL import Image
+from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
-from modules import shared
-from modules.shared import opts
-from modules.devices import has_mps
 import modules.images
+from modules import shared
+from modules import shared, modelloader
+from modules.devices import has_mps
+from modules.paths import models_path
+from modules.shared import opts
+
+model_dir = "ESRGAN"
+model_path = os.path.join(models_path, model_dir)
+model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
+model_name = "ESRGAN_x4.pth"
 
 
-def load_model(filename):
+def load_model(path: str, name: str):
+    global model_path
+    global model_url
+    global model_dir
+    global model_name
+    if "http" in path:
+        filename = load_file_from_url(url=model_url, model_dir=model_path, file_name=model_name, progress=True)
+    else:
+        filename = path
+    if not os.path.exists(filename) or filename is None:
+        print("Unable to load %s from %s" % (model_dir, filename))
+        return None
+    print("Loading %s from %s" % (model_dir, filename))
     # this code is adapted from https://github.com/xinntao/ESRGAN
     pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
     crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
@@ -118,24 +138,30 @@ def esrgan_upscale(model, img):
 class UpscalerESRGAN(modules.images.Upscaler):
     def __init__(self, filename, title):
         self.name = title
-        self.model = load_model(filename)
+        self.filename = filename
 
     def do_upscale(self, img):
-        model = self.model.to(shared.device)
+        model = load_model(self.filename, self.name)
+        if model is None:
+            return img
+        model.to(shared.device)
         img = esrgan_upscale(model, img)
         return img
 
 
-def load_models(dirname):
-    for file in os.listdir(dirname):
-        path = os.path.join(dirname, file)
-        model_name, extension = os.path.splitext(file)
-
-        if extension != '.pt' and extension != '.pth':
-            continue
+def setup_model(dirname):
+    global model_path
+    global model_name
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
 
+    model_paths = modelloader.load_models(model_path, command_path=dirname, ext_filter=[".pt", ".pth"])
+    if len(model_paths) == 0:
+        modules.shared.sd_upscalers.append(UpscalerESRGAN(model_url, model_name))
+    for file in model_paths:
+        name = modelloader.friendly_name(file)
         try:
-            modules.shared.sd_upscalers.append(UpscalerESRGAN(path, model_name))
+            modules.shared.sd_upscalers.append(UpscalerESRGAN(file, name))
         except Exception:
-            print(f"Error loading ESRGAN model: {path}", file=sys.stderr)
+            print(f"Error loading ESRGAN model: {file}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)