diff --git a/invokeai/app/invocations/infill.py b/invokeai/app/invocations/infill.py
index edee275e72..50fec22994 100644
--- a/invokeai/app/invocations/infill.py
+++ b/invokeai/app/invocations/infill.py
@@ -133,7 +133,9 @@ class LaMaInfillInvocation(InfillImageProcessorInvocation):
     """Infills transparent areas of an image using the LaMa model"""
 
     def infill(self, image: Image.Image, context: InvocationContext):
-        lama = LaMA(context)
+        # Note that this accesses a protected attribute to get to the model manager service.
+        # Is there a better way?
+        lama = LaMA(context._services.model_manager)
         return lama(image)
 
 
diff --git a/invokeai/app/services/model_install/model_install_base.py b/invokeai/app/services/model_install/model_install_base.py
index 0ea901fb46..388f4a5ba2 100644
--- a/invokeai/app/services/model_install/model_install_base.py
+++ b/invokeai/app/services/model_install/model_install_base.py
@@ -468,7 +468,12 @@ class ModelInstallServiceBase(ABC):
         """
 
     @abstractmethod
-    def download_and_cache(self, source: Union[str, AnyHttpUrl], access_token: Optional[str] = None) -> Path:
+    def download_and_cache_ckpt(
+        self,
+        source: str | AnyHttpUrl,
+        access_token: Optional[str] = None,
+        timeout: int = 0,
+    ) -> Path:
         """
         Download the model file located at source to the models cache and return its Path.
 
diff --git a/invokeai/app/services/model_install/model_install_default.py b/invokeai/app/services/model_install/model_install_default.py
index c4127acf7a..32c86ad3a3 100644
--- a/invokeai/app/services/model_install/model_install_default.py
+++ b/invokeai/app/services/model_install/model_install_default.py
@@ -399,9 +399,9 @@ class ModelInstallService(ModelInstallServiceBase):
         escaped_source = slugify(str(source))
         return app_config.download_cache_path / escaped_source
 
-    def download_and_cache(
+    def download_and_cache_ckpt(
         self,
-        source: Union[str, AnyHttpUrl],
+        source: str | AnyHttpUrl,
         access_token: Optional[str] = None,
         timeout: int = 0,
     ) -> Path:
diff --git a/invokeai/app/services/model_load/model_load_base.py b/invokeai/app/services/model_load/model_load_base.py
index cc80333e93..d59f7a370d 100644
--- a/invokeai/app/services/model_load/model_load_base.py
+++ b/invokeai/app/services/model_load/model_load_base.py
@@ -2,7 +2,10 @@
 """Base class for model loader."""
 
 from abc import ABC, abstractmethod
-from typing import Optional
+from pathlib import Path
+from typing import Callable, Dict, Optional
+
+from torch import Tensor
 
 from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
@@ -38,3 +41,25 @@ class ModelLoadServiceBase(ABC):
     @abstractmethod
     def convert_cache(self) -> ModelConvertCacheBase:
         """Return the checkpoint convert cache used by this loader."""
+
+    @abstractmethod
+    def load_ckpt_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], Dict[str, Tensor]]] = None
+    ) -> LoadedModel:
+        """
+        Load the checkpoint-format model file located at the indicated Path.
+
+        This will load an arbitrary model file into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that the LoadedModel object will have a `config` attribute of None.
+
+        Args:
+          model_path: A pathlib.Path to a checkpoint-style models file
+          loader: A Callable that expects a Path and returns a Dict[str|int, Any]
+
+        Returns:
+          A LoadedModel object.
+        """
diff --git a/invokeai/app/services/model_load/model_load_default.py b/invokeai/app/services/model_load/model_load_default.py
index 21d3c56f36..a87b6123ce 100644
--- a/invokeai/app/services/model_load/model_load_default.py
+++ b/invokeai/app/services/model_load/model_load_default.py
@@ -1,7 +1,13 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
 """Implementation of model loader service."""
 
-from typing import Optional, Type
+from pathlib import Path
+from typing import Callable, Dict, Optional, Type
+
+from picklescan.scanner import scan_file_path
+from safetensors.torch import load_file as safetensors_load_file
+from torch import Tensor
+from torch import load as torch_load
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
@@ -88,6 +94,51 @@ class ModelLoadService(ModelLoadServiceBase):
             )
         return loaded_model
 
+    def load_ckpt_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], Dict[str, Tensor]]] = None
+    ) -> LoadedModel:
+        """
+        Load the checkpoint-format model file located at the indicated Path.
+
+        This will load an arbitrary model file into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that the LoadedModel object will have a `config` attribute of None.
+
+        Args:
+          model_path: A pathlib.Path to a checkpoint-style models file
+          loader: A Callable that expects a Path and returns a Dict[str|int, Any]
+
+        Returns:
+          A LoadedModel object.
+        """
+        cache_key = str(model_path)
+        ram_cache = self.ram_cache
+        try:
+            return LoadedModel(_locker=ram_cache.get(key=cache_key))
+        except IndexError:
+            pass
+
+        def torch_load_file(checkpoint: Path) -> Dict[str, Tensor]:
+            scan_result = scan_file_path(checkpoint)
+            if scan_result.infected_files != 0:
+                raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
+            result: Dict[str, Tensor] = torch_load(checkpoint, map_location="cpu")
+            return result
+
+        if loader is None:
+            loader = (
+                torch_load_file
+                if model_path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin"))
+                else lambda path: safetensors_load_file(path, device="cpu")
+            )
+
+        raw_model = loader(model_path)
+        ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModel(_locker=ram_cache.get(key=cache_key))
+
     def _emit_load_event(
         self,
         context_data: InvocationContextData,
diff --git a/invokeai/app/services/model_manager/model_manager_base.py b/invokeai/app/services/model_manager/model_manager_base.py
index af1b68e1ec..7a5f433aca 100644
--- a/invokeai/app/services/model_manager/model_manager_base.py
+++ b/invokeai/app/services/model_manager/model_manager_base.py
@@ -1,11 +1,15 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 
 from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Callable, Dict, Optional
 
 import torch
+from pydantic.networks import AnyHttpUrl
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
+from invokeai.backend.model_manager.load import LoadedModel
 
 from ..config import InvokeAIAppConfig
 from ..download import DownloadQueueServiceBase
@@ -66,3 +70,35 @@ class ModelManagerServiceBase(ABC):
     @abstractmethod
     def stop(self, invoker: Invoker) -> None:
         pass
+
+    @abstractmethod
+    def load_ckpt_from_url(
+        self,
+        source: str | AnyHttpUrl,
+        access_token: Optional[str] = None,
+        timeout: Optional[int] = 0,
+        loader: Optional[Callable[[Path], Dict[str, torch.Tensor]]] = None,
+    ) -> LoadedModel:
+        """
+        Download, cache, and Load the model file located at the indicated URL.
+
+        This will check the model download cache for the model designated
+        by the provided URL and download it if needed using download_and_cache_ckpt().
+        It will then load the model into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that the LoadedModel object will have a `config` attribute of None.
+
+        Args:
+          source: A URL or a string that can be converted in one. Repo_ids
+                  do not work here.
+          access_token: Optional access token for restricted resources.
+          timeout: Wait up to the indicated number of seconds before timing
+                   out long downloads.
+          loader: A Callable that expects a Path and returns a Dict[str|int, Any]
+
+        Returns:
+          A LoadedModel object.
+        """
diff --git a/invokeai/app/services/model_manager/model_manager_default.py b/invokeai/app/services/model_manager/model_manager_default.py
index 1a2b9a3402..57c409c066 100644
--- a/invokeai/app/services/model_manager/model_manager_default.py
+++ b/invokeai/app/services/model_manager/model_manager_default.py
@@ -1,13 +1,15 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
-from typing import Optional
+from pathlib import Path
+from typing import Callable, Dict, Optional
 
 import torch
+from pydantic.networks import AnyHttpUrl
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load import LoadedModel, ModelCache, ModelConvertCache, ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
@@ -100,3 +102,36 @@ class ModelManagerService(ModelManagerServiceBase):
             event_bus=events,
         )
         return cls(store=model_record_service, install=installer, load=loader)
+
+    def load_ckpt_from_url(
+        self,
+        source: str | AnyHttpUrl,
+        access_token: Optional[str] = None,
+        timeout: Optional[int] = 0,
+        loader: Optional[Callable[[Path], Dict[str, torch.Tensor]]] = None,
+    ) -> LoadedModel:
+        """
+        Download, cache, and Load the model file located at the indicated URL.
+
+        This will check the model download cache for the model designated
+        by the provided URL and download it if needed using download_and_cache_ckpt().
+        It will then load the model into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that the LoadedModel object will have a `config` attribute of None.
+
+        Args:
+          source: A URL or a string that can be converted in one. Repo_ids
+                  do not work here.
+          access_token: Optional access token for restricted resources.
+          timeout: Wait up to the indicated number of seconds before timing
+                   out long downloads.
+          loader: A Callable that expects a Path and returns a Dict[str|int, Any]
+
+        Returns:
+          A LoadedModel object.
+        """
+        model_path = self.install.download_and_cache_ckpt(source=source, access_token=access_token, timeout=timeout)
+        return self.load.load_ckpt_from_path(model_path=model_path, loader=loader)
diff --git a/invokeai/app/services/shared/invocation_context.py b/invokeai/app/services/shared/invocation_context.py
index 50551efa31..485be2ba91 100644
--- a/invokeai/app/services/shared/invocation_context.py
+++ b/invokeai/app/services/shared/invocation_context.py
@@ -1,14 +1,11 @@
 import threading
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Union
+from typing import TYPE_CHECKING, Callable, Dict, Optional, Union
 
-from picklescan.scanner import scan_file_path
+import torch
 from PIL.Image import Image
 from pydantic.networks import AnyHttpUrl
-from safetensors.torch import load_file as safetensors_load_file
-from torch import Tensor
-from torch import load as torch_load
 
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import MetadataField, WithBoard, WithMetadata
@@ -263,7 +260,7 @@ class ImagesInterface(InvocationContextInterface):
 
 
 class TensorsInterface(InvocationContextInterface):
-    def save(self, tensor: Tensor) -> str:
+    def save(self, tensor: torch.Tensor) -> str:
         """Saves a tensor, returning its name.
 
         Args:
@@ -276,7 +273,7 @@ class TensorsInterface(InvocationContextInterface):
         name = self._services.tensors.save(obj=tensor)
         return name
 
-    def load(self, name: str) -> Tensor:
+    def load(self, name: str) -> torch.Tensor:
         """Loads a tensor by name.
 
         Args:
@@ -316,8 +313,10 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
+    """Common API for loading, downloading and managing models."""
+
     def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
-        """Checks if a model exists.
+        """Check if a model exists.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -326,14 +325,18 @@ class ModelsInterface(InvocationContextInterface):
             True if the model exists, False if not.
         """
         if isinstance(identifier, str):
-            return self._services.model_manager.store.exists(identifier)
-
-        return self._services.model_manager.store.exists(identifier.key)
+            # For some reason, Mypy is not getting the type annotations for many of
+            # the model manager service calls and raises a "returning Any in typed
+            # context" error. Hence the extra typing hints here and below.
+            result: bool = self._services.model_manager.store.exists(identifier)
+        else:
+            result = self._services.model_manager.store.exists(identifier.key)
+        return result
 
     def load(
         self, identifier: Union[str, "ModelIdentifierField"], submodel_type: Optional[SubModelType] = None
     ) -> LoadedModel:
-        """Loads a model.
+        """Load a model.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -342,22 +345,22 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             An object representing the loaded model.
         """
-
         # The model manager emits events as it loads the model. It needs the context data to build
         # the event payloads.
 
         if isinstance(identifier, str):
             model = self._services.model_manager.store.get_model(identifier)
-            return self._services.model_manager.load.load_model(model, submodel_type, self._data)
+            result: LoadedModel = self._services.model_manager.load.load_model(model, submodel_type, self._data)
         else:
             _submodel_type = submodel_type or identifier.submodel_type
             model = self._services.model_manager.store.get_model(identifier.key)
-            return self._services.model_manager.load.load_model(model, _submodel_type, self._data)
+            result = self._services.model_manager.load.load_model(model, _submodel_type, self._data)
+        return result
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
     ) -> LoadedModel:
-        """Loads a model by its attributes.
+        """Load a model by its attributes.
 
         Args:
             name: Name of the model.
@@ -369,7 +372,6 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             An object representing the loaded model.
         """
-
         configs = self._services.model_manager.store.search_by_attr(model_name=name, base_model=base, model_type=type)
         if len(configs) == 0:
             raise UnknownModelException(f"No model found with name {name}, base {base}, and type {type}")
@@ -377,10 +379,11 @@ class ModelsInterface(InvocationContextInterface):
         if len(configs) > 1:
             raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
 
-        return self._services.model_manager.load.load_model(configs[0], submodel_type, self._data)
+        result: LoadedModel = self._services.model_manager.load.load_model(configs[0], submodel_type, self._data)
+        return result
 
     def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
-        """Gets a model's config.
+        """Get a model's config.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -389,12 +392,13 @@ class ModelsInterface(InvocationContextInterface):
             The model's config.
         """
         if isinstance(identifier, str):
-            return self._services.model_manager.store.get_model(identifier)
-
-        return self._services.model_manager.store.get_model(identifier.key)
+            result: AnyModelConfig = self._services.model_manager.store.get_model(identifier)
+        else:
+            result = self._services.model_manager.store.get_model(identifier.key)
+        return result
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
-        """Searches for models by path.
+        """Search for models by path.
 
         Args:
             path: The path to search for.
@@ -402,7 +406,8 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             A list of models that match the path.
         """
-        return self._services.model_manager.store.search_by_path(path)
+        result: list[AnyModelConfig] = self._services.model_manager.store.search_by_path(path)
+        return result
 
     def search_by_attrs(
         self,
@@ -411,7 +416,7 @@ class ModelsInterface(InvocationContextInterface):
         type: Optional[ModelType] = None,
         format: Optional[ModelFormat] = None,
     ) -> list[AnyModelConfig]:
-        """Searches for models by attributes.
+        """Search for models by attributes.
 
         Args:
             name: The name to search for (exact match).
@@ -422,13 +427,13 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             A list of models that match the attributes.
         """
-
-        return self._services.model_manager.store.search_by_attr(
+        result: list[AnyModelConfig] = self._services.model_manager.store.search_by_attr(
             model_name=name,
             base_model=base,
             model_type=type,
             model_format=format,
         )
+        return result
 
     def download_and_cache_ckpt(
         self,
@@ -451,26 +456,49 @@ class ModelsInterface(InvocationContextInterface):
                    out long downloads.
 
         Result:
-          Path of the downloaded model
+          Path to the downloaded model
 
         May Raise:
           HTTPError
           TimeoutError
         """
         installer = self._services.model_manager.install
-        path: Path = installer.download_and_cache(
+        path: Path = installer.download_and_cache_ckpt(
             source=source,
             access_token=access_token,
             timeout=timeout,
         )
         return path
 
+    def load_ckpt_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], Dict[str, torch.Tensor]]] = None
+    ) -> LoadedModel:
+        """
+        Load the checkpoint-format model file located at the indicated Path.
+
+        This will load an arbitrary model file into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that the LoadedModel object will have a `config` attribute of None.
+
+        Args:
+          model_path: A pathlib.Path to a checkpoint-style models file
+          loader: A Callable that expects a Path and returns a Dict[str|int, Any]
+
+        Returns:
+          A LoadedModel object.
+        """
+        result: LoadedModel = self._services.model_manager.load.load_ckpt_from_path(model_path, loader=loader)
+        return result
+
     def load_ckpt_from_url(
         self,
-        source: Union[str, AnyHttpUrl],
+        source: str | AnyHttpUrl,
         access_token: Optional[str] = None,
         timeout: Optional[int] = 0,
-        loader: Optional[Callable[[Path], Dict[str | int, Any]]] = None,
+        loader: Optional[Callable[[Path], Dict[str, torch.Tensor]]] = None,
     ) -> LoadedModel:
         """
         Download, cache, and Load the model file located at the indicated URL.
@@ -495,29 +523,10 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
           A LoadedModel object.
         """
-        ram_cache = self._services.model_manager.load.ram_cache
-        try:
-            return LoadedModel(_locker=ram_cache.get(key=str(source)))
-        except IndexError:
-            pass
-
-        def torch_load_file(checkpoint: Path) -> Dict[str | int, Any]:
-            scan_result = scan_file_path(checkpoint)
-            if scan_result.infected_files != 0:
-                raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
-            return torch_load(path, map_location="cpu")
-
-        path = self.download_and_cache_ckpt(source, access_token, timeout)
-        if loader is None:
-            loader = (
-                torch_load_file
-                if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin"))
-                else lambda path: safetensors_load_file(path, device="cpu")
-            )
-
-        raw_model = loader(path)
-        ram_cache.put(key=str(source), model=raw_model)
-        return LoadedModel(_locker=ram_cache.get(key=str(source)))
+        result: LoadedModel = self._services.model_manager.load_ckpt_from_url(
+            source=source, access_token=access_token, timeout=timeout, loader=loader
+        )
+        return result
 
 
 class ConfigInterface(InvocationContextInterface):
diff --git a/invokeai/backend/image_util/infill_methods/lama.py b/invokeai/backend/image_util/infill_methods/lama.py
index 8c3f33efad..c7fea497ca 100644
--- a/invokeai/backend/image_util/infill_methods/lama.py
+++ b/invokeai/backend/image_util/infill_methods/lama.py
@@ -1,11 +1,13 @@
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.shared.invocation_context import InvocationContext
+
+if TYPE_CHECKING:
+    from invokeai.app.services.model_manager import ModelManagerServiceBase
 
 
 def norm_img(np_img):
@@ -16,20 +18,20 @@ def norm_img(np_img):
     return np_img
 
 
-def load_jit_model(url_or_path, device):
+def load_jit_model(url_or_path, device) -> torch.nn.Module:
     model_path = url_or_path
     logger.info(f"Loading model from: {model_path}")
-    model = torch.jit.load(model_path, map_location="cpu").to(device)
+    model: torch.nn.Module = torch.jit.load(model_path, map_location="cpu").to(device)  # type: ignore
     model.eval()
     return model
 
 
 class LaMA:
-    def __init__(self, context: InvocationContext):
-        self._context = context
+    def __init__(self, model_manager: "ModelManagerServiceBase"):
+        self._model_manager = model_manager
 
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
-        loaded_model = self._context.models.load_ckpt_from_url(
+        loaded_model = self._model_manager.load_ckpt_from_url(
             source="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
             loader=lambda path: load_jit_model(path, "cpu"),
         )
diff --git a/invokeai/backend/model_manager/config.py b/invokeai/backend/model_manager/config.py
index 82f88c0e81..1a5d95b7d8 100644
--- a/invokeai/backend/model_manager/config.py
+++ b/invokeai/backend/model_manager/config.py
@@ -36,7 +36,7 @@ from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
 # RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[ModelMixin, RawModel, torch.nn.Module]
+AnyModel = Union[ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor]]
 
 
 class InvalidModelConfigException(Exception):
diff --git a/tests/app/services/model_load/test_load_api.py b/tests/app/services/model_load/test_load_api.py
index 463be86c68..167c2a09df 100644
--- a/tests/app/services/model_load/test_load_api.py
+++ b/tests/app/services/model_load/test_load_api.py
@@ -47,5 +47,3 @@ def test_download_and_load(mock_context: InvocationContext):
     with loaded_model_1 as model_1, loaded_model_2 as model_2:
         assert model_1 == model_2
         assert isinstance(model_1, dict)
-
-