chore: import upstream snapshot with attribution

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+from diffusers import UNet2DConditionModel
+
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
+
+if TYPE_CHECKING:
+    from invokeai.app.invocations.model import ModelIdentifierField
+    from invokeai.app.services.shared.invocation_context import InvocationContext
+    from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
+
+
+class LoRAExt(ExtensionBase):
+    def __init__(
+        self,
+        node_context: InvocationContext,
+        model_id: ModelIdentifierField,
+        weight: float,
+    ):
+        super().__init__()
+        self._node_context = node_context
+        self._model_id = model_id
+        self._weight = weight
+
+    @contextmanager
+    def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
+        lora_model = self._node_context.models.load(self._model_id).model
+        assert isinstance(lora_model, ModelPatchRaw)
+        LayerPatcher.apply_smart_model_patch(
+            model=unet,
+            prefix="lora_unet_",
+            patch=lora_model,
+            patch_weight=self._weight,
+            original_weights=original_weights,
+            original_modules={},
+            dtype=unet.dtype,
+            force_direct_patching=True,
+            force_sidecar_patching=False,
+        )
+        del lora_model
+
+        yield