diff --git a/invokeai/backend/model_manager/load/model_cache/model_cache_default.py b/invokeai/backend/model_manager/load/model_cache/model_cache_default.py
index 551412d66a..910087c4bb 100644
--- a/invokeai/backend/model_manager/load/model_cache/model_cache_default.py
+++ b/invokeai/backend/model_manager/load/model_cache/model_cache_default.py
@@ -123,7 +123,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         assert current_thread is not None
         assigned = [x for x, tid in self._execution_devices.items() if current_thread == tid]
         if not assigned:
-            raise ValueError("No GPU has been reserved for the use of thread {current_thread}")
+            raise ValueError(f"No GPU has been reserved for the use of thread {current_thread}")
         return assigned[0]
 
     @contextmanager
diff --git a/invokeai/backend/util/devices.py b/invokeai/backend/util/devices.py
index 745c128099..c7db33a667 100644
--- a/invokeai/backend/util/devices.py
+++ b/invokeai/backend/util/devices.py
@@ -59,7 +59,13 @@ class TorchDevice:
     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()
+            try:
+                return cls._model_cache.get_execution_device()
+            except ValueError as e:  # May happen if no gpu was reserved. Return a generic device.
+                if str(e).startswith("No GPU has been reserved"):
+                    pass
+                else:
+                    raise e
         app_config = get_config()
         if app_config.device != "auto":
             device = torch.device(app_config.device)