feat(app): better errors when scanning models with picklescan

Differentiate between malware detection and scan error.

Author: psychedelicious

invokeai/app/services/model_load/model_load_default.py

@@ -85,8 +85,11 @@ def load_model_from_path(
 
         def torch_load_file(checkpoint: Path) -> AnyModel:
             scan_result = scan_file_path(checkpoint)
-            if scan_result.infected_files != 0 or scan_result.scan_err:
-                raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
+            if scan_result.infected_files != 0:
+                raise Exception(f"The model at {checkpoint} is potentially infected by malware. Aborting load.")
+            if scan_result.scan_err:
+                raise Exception(f"Error scanning model at {checkpoint} for malware. Aborting load.")
+
             result = torch_load(checkpoint, map_location="cpu")
             return result
 

invokeai/backend/model_manager/probe.py

@@ -483,8 +483,10 @@ def _scan_model(cls, model_name: str, checkpoint: Path) -> None:
         """
         # scan model
         scan_result = scan_file_path(checkpoint)
-        if scan_result.infected_files != 0 or scan_result.scan_err:
-            raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
+        if scan_result.infected_files != 0:
+            raise Exception(f"The model {model_name} is potentially infected by malware. Aborting import.")
+        if scan_result.scan_err:
+            raise Exception(f"Error scanning model {model_name} for malware. Aborting import.")
 
 
 # Probing utilities

invokeai/backend/model_manager/util/model_util.py

@@ -58,8 +58,11 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str,
     else:
         if scan:
             scan_result = scan_file_path(path)
-            if scan_result.infected_files != 0 or scan_result.scan_err:
-                raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
+            if scan_result.infected_files != 0:
+                raise Exception(f"The model at {path} is potentially infected by malware. Aborting import.")
+            if scan_result.scan_err:
+                raise Exception(f"Error scanning model at {path} for malware. Aborting import.")
+
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint