chore: import upstream snapshot with attribution

invokeai/app/services/model_load/model_load_base.py

@@ -0,0 +1,52 @@
+# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
+"""Base class for model loader."""
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Callable, Optional
+
+from invokeai.backend.model_manager.configs.factory import AnyModelConfig
+from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
+
+
+class ModelLoadServiceBase(ABC):
+    """Wrapper around AnyModelLoader."""
+
+    @abstractmethod
+    def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+        """
+        Given a model's configuration, load it and return the LoadedModel object.
+
+        :param model_config: Model configuration record (as returned by ModelRecordBase.get_model())
+        :param submodel: For main (pipeline models), the submodel to fetch.
+        """
+
+    @property
+    @abstractmethod
+    def ram_cache(self) -> ModelCache:
+        """Return the RAM cache used by this loader."""
+
+    @abstractmethod
+    def load_model_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
+    ) -> LoadedModelWithoutConfig:
+        """
+        Load the model file or directory 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 this returns a LoadedModelWithoutConfig object, which is the same as
+        LoadedModel, but without the config attribute.
+
+        Args:
+          model_path: A pathlib.Path to a checkpoint-style models file
+          loader: A Callable that expects a Path and returns a Dict[str, Tensor]
+
+        Returns:
+          A LoadedModel object.
+        """