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()