make choose_torch_dtype() usable outside an invocation context

Author: Lincoln Stein

invokeai/app/services/model_install/model_install_default.py

@@ -43,6 +43,7 @@
 from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
+from invokeai.backend.util.devices import TorchDevice
 
 from .model_install_base import (
     MODEL_SOURCE_TO_TYPE_MAP,
@@ -636,7 +637,7 @@ def _next_id(self) -> int:
 
     def _guess_variant(self) -> Optional[ModelRepoVariant]:
         """Guess the best HuggingFace variant type to download."""
-        precision = torch.float16 if self._app_config.precision == "auto" else torch.dtype(self._app_config.precision)
+        precision = TorchDevice.choose_torch_dtype()
         return ModelRepoVariant.FP16 if precision == torch.float16 else None
 
     def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:

invokeai/backend/util/devices.py

@@ -60,6 +60,11 @@ def choose_torch_device(cls) -> torch.device:
         """Return the torch.device to use for accelerated inference."""
         if cls._model_cache:
             return cls._model_cache.get_execution_device()
+        else:
+            return cls._choose_device()
+
+    @classmethod
+    def _choose_device(cls) -> torch.device:
         app_config = get_config()
         if app_config.device != "auto":
             device = torch.device(app_config.device)
@@ -82,8 +87,8 @@ def execution_devices(cls) -> Set[torch.device]:
     @classmethod
     def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
         """Return the precision to use for accelerated inference."""
-        device = device or cls.choose_torch_device()
         config = get_config()
+        device = device or cls._choose_device()
         if device.type == "cuda" and torch.cuda.is_available():
             device_name = torch.cuda.get_device_name(device)
             if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name: