Add General-lite as another option in the model selection.

app.py

@@ -37,6 +37,7 @@ class ImagePreprocessor():
 
 usage_to_weights_file = {
     'General': 'BiRefNet',
+    'General-lite': 'BiRefNet_T',
     'Portrait': 'BiRefNet-portrait',
     'DIS': 'BiRefNet-DIS5K',
     'HRSOD': 'BiRefNet-HRSOD',
@@ -55,8 +56,8 @@ def predict(image, resolution, weights_file):
     global birefnet
     # Load BiRefNet with chosen weights
     _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else usage_to_weights_file['General']))
-    print('Change weights to:', _weights_file)
-    birefnet = birefnet.from_pretrained(_weights_file)
+    print('Using weights:', _weights_file)
+    birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
     birefnet.to(device)
     birefnet.eval()