Fix the transformers cache bug in versions >= v4.22.0.

app.py

@@ -10,7 +10,7 @@ from typing import Tuple
 
 from PIL import Image
 from gradio_imageslider import ImageSlider
-from transformers import AutoModelForImageSegmentation
+import transformers
 from torchvision import transforms
 
 import requests
@@ -18,6 +18,7 @@ from io import BytesIO
 import zipfile
 
 
+transformers.utils.move_cache()
 torch.set_float32_matmul_precision('high')
 torch.jit.script = lambda f: f
 
@@ -89,7 +90,7 @@ usage_to_weights_file = {
     'General-dynamic': 'BiRefNet_dynamic',
 }
 
-birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
+birefnet = transformers.AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
 birefnet.to(device)
 birefnet.eval(); birefnet.half()
 
@@ -102,7 +103,7 @@ def predict(images, resolution, weights_file):
     # 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('Using weights: {}.'.format(_weights_file))
-    birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
+    birefnet = transformers.AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
     birefnet.to(device)
     birefnet.eval(); birefnet.half()