Union[foo, None]=>Optional[foo]

invokeai/backend/model_management/model_cache.py

@@ -100,7 +100,6 @@ class ModelCache(object):
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
         '''
         #max_cache_size = 9999
-        execution_device = torch.device('cuda')
 
         self.model_infos: Dict[str, ModelBase] = dict()
         self.lazy_offloading = lazy_offloading

invokeai/backend/model_management/model_manager.py

@@ -306,7 +306,6 @@ class ModelManager(object):
         and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
         """
-
         self.config_path = None
         if isinstance(config, (str, Path)):
             self.config_path = Path(config)

invokeai/backend/model_management/model_probe.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 
 from diffusers import ModelMixin, ConfigMixin
 from pathlib import Path
-from typing import Callable, Literal, Union, Dict
+from typing import Callable, Literal, Union, Dict, Optional
 from picklescan.scanner import scan_file_path
 
 from .models import (
@@ -64,7 +64,7 @@ class ModelProbe(object):
     @classmethod
     def probe(cls,
               model_path: Path,
-              model: Union[Dict, ModelMixin] = None,
+              model: Optional[Union[Dict, ModelMixin]],
               prediction_type_helper: Callable[[Path],SchedulerPredictionType] = None)->ModelProbeInfo:
         '''
         Probe the model at model_path and return sufficient information about it