feat(model_probe): provide more clues when we fail to load a model.

invokeai/backend/model_management/model_probe.py

@@ -1,12 +1,11 @@
 import json
-import torch
-import safetensors.torch
-
 from dataclasses import dataclass
-
-from diffusers import ModelMixin, ConfigMixin
 from pathlib import Path
 from typing import Callable, Literal, Union, Dict, Optional
+
+import safetensors.torch
+import torch
+from diffusers import ModelMixin, ConfigMixin
 from picklescan.scanner import scan_file_path
 
 from .models import (
@@ -17,8 +16,8 @@ from .models import (
     SilenceWarnings,
     InvalidModelException,
 )
-from .util import lora_token_vector_length
 from .models.base import read_checkpoint_meta
+from .util import lora_token_vector_length
 
 
 @dataclass
@@ -171,6 +170,7 @@ class ModelProbe(object):
         Get the model type of a hugging-face style folder.
         """
         class_name = None
+        error_hint = None
         if model:
             class_name = model.__class__.__name__
         else:
@@ -190,12 +190,18 @@ class ModelProbe(object):
                 with open(config_path, "r") as file:
                     conf = json.load(file)
                 class_name = conf["_class_name"]
+            else:
+                error_hint = f"No model_index.json or config.json found in {folder_path}."
 
         if class_name and (type := cls.CLASS2TYPE.get(class_name)):
             return type
+        else:
+            error_hint = f"class {class_name} is not one of the supported classes [{', '.join(cls.CLASS2TYPE.keys())}]"
 
         # give up
-        raise InvalidModelException(f"Unable to determine model type for {folder_path}")
+        raise InvalidModelException(
+            f"Unable to determine model type for {folder_path}" + (f"; {error_hint}" if error_hint else "")
+        )
 
     @classmethod
     def _scan_and_load_checkpoint(cls, model_path: Path) -> dict: