Improve error handling in BiRefNetMatting model loading

Refines exception handling in the load_model method to provide more informative error messages and re-raise exceptions for upstream handling. Removes boolean return values in favor of exception-based flow, and updates execute to rely on exceptions for error detection.

canvas_node.py

@@ -613,42 +613,38 @@ class BiRefNetMatting:
     def load_model(self, model_path):
         try:
             if model_path not in self.model_cache:
-
                 full_model_path = os.path.join(self.base_path, "BiRefNet")
-
                 log_info(f"Loading BiRefNet model from {full_model_path}...")
 
                 try:
-
                     self.model = AutoModelForImageSegmentation.from_pretrained(
                         "ZhengPeng7/BiRefNet",
                         trust_remote_code=True,
                         cache_dir=full_model_path
                     )
-
                     self.model.eval()
                     if torch.cuda.is_available():
                         self.model = self.model.cuda()
-
                     self.model_cache[model_path] = self.model
                     log_info("Model loaded successfully from Hugging Face")
-                    log_debug(f"Model type: {type(self.model)}")
-                    log_debug(f"Model device: {next(self.model.parameters()).device}")
-
                 except Exception as e:
-                    log_error(f"Failed to load model: {str(e)}")
-                    raise
-
+                    log_error(f"Failed to load model from Hugging Face: {str(e)}")
+                    # Re-raise with a more informative message
+                    raise RuntimeError(
+                        "Failed to download or load the matting model. "
+                        "This could be due to a network issue, file permissions, or a corrupted model cache. "
+                        f"Please check your internet connection and the model cache path: {full_model_path}. "
+                        f"Original error: {str(e)}"
+                    ) from e
             else:
                 self.model = self.model_cache[model_path]
                 log_debug("Using cached model")
 
-            return True
-
         except Exception as e:
+            # Catch the re-raised exception or any other error
             log_error(f"Error loading model: {str(e)}")
             log_exception("Model loading failed")
-            return False
+            raise  # Re-raise the exception to be caught by the execute method
 
     def preprocess_image(self, image):
 
@@ -678,11 +674,9 @@ class BiRefNetMatting:
 
     def execute(self, image, model_path, threshold=0.5, refinement=1):
         try:
-
             PromptServer.instance.send_sync("matting_status", {"status": "processing"})
 
-            if not self.load_model(model_path):
-                raise RuntimeError("Failed to load model")
+            self.load_model(model_path)
 
             if isinstance(image, torch.Tensor):
                 original_size = image.shape[-2:] if image.dim() == 4 else image.shape[-2:]