Upgrade the weights loading method to avoid duplicated loading.

app.py

@@ -40,6 +40,7 @@ weights_path = 'BiRefNet'
 birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', weights_path)), trust_remote_code=True)
 birefnet.to(device)
 birefnet.eval()
+birefnet.weights_path = weights_path
 
 usage_to_weights_file = {
     'General': 'BiRefNet',
@@ -53,17 +54,16 @@ usage_to_weights_file = {
 
 @spaces.GPU
 def predict(image, resolution, weights_file):
-    global weights_path
     global birefnet
-    if weights_file != weights_path:
+    if birefnet.weights_path != weights_file:
         # Load BiRefNet with chosen weights
         _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else 'BiRefNet'))
         print('Change weights to:', _weights_file)
-        print('\t', weights_file, weights_path)
-        birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
+        print('\t', weights_file, birefnet.weights_path)
+        birefnet = birefnet.from_pretrained(_weights_file)
         birefnet.to(device)
         birefnet.eval()
-        weights_path = weights_file
+        birefnet.weights_path = weights_file
 
     resolution = f"{image.shape[1]}x{image.shape[0]}" if resolution == '' else resolution
     # Image is a RGB numpy array.