Add a new model for matting: BiRefNet_dynamic-matting.

app.py

@@ -150,13 +150,14 @@ usage_to_weights_file = {
     'General-reso_512': 'BiRefNet_512x512',
     'General-Lite': 'BiRefNet_lite',
     'General-Lite-2K': 'BiRefNet_lite-2K',
-    'Anime-Lite': 'BiRefNet_lite-Anime',
+    # 'Anime-Lite': 'BiRefNet_lite-Anime',
     'DIS': 'BiRefNet-DIS5K',
     'HRSOD': 'BiRefNet-HRSOD',
     'COD': 'BiRefNet-COD',
     'DIS-TR_TEs': 'BiRefNet-DIS5K-TR_TEs',
     'General-legacy': 'BiRefNet-legacy',
     'General-dynamic': 'BiRefNet_dynamic',
+    'Matting-dynamic': 'BiRefNet_dynamic-matting',
 }
 
 birefnet = transformers.AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
@@ -186,7 +187,7 @@ def predict(images, resolution, weights_file):
         elif weights_file in ['General-reso_512']:
             resolution = (512, 512)
         else:
-            if weights_file in ['General-dynamic']:
+            if '_dynamic' in weights_file:
                 resolution = None
                 print('Using the original size (div by 32) for inference.')
             else: