Rename ModelPatcher methods to reflect that they are general model patching methods and are not LoRA-specific.

invokeai/backend/patches/model_patcher.py

@@ -17,7 +17,7 @@ class ModelPatcher:
     @staticmethod
     @torch.no_grad()
     @contextmanager
-    def apply_lora_patches(
+    def apply_model_patches(
         model: torch.nn.Module,
         patches: Iterable[Tuple[LoRAModelRaw, float]],
         prefix: str,
@@ -37,7 +37,7 @@ class ModelPatcher:
         original_weights = OriginalWeightsStorage(cached_weights)
         try:
             for patch, patch_weight in patches:
-                ModelPatcher.apply_lora_patch(
+                ModelPatcher.apply_model_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -54,7 +54,7 @@ class ModelPatcher:
 
     @staticmethod
     @torch.no_grad()
-    def apply_lora_patch(
+    def apply_model_patch(
         model: torch.nn.Module,
         prefix: str,
         patch: LoRAModelRaw,
@@ -89,7 +89,7 @@ class ModelPatcher:
                 model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
             )
 
-            ModelPatcher._apply_lora_layer_patch(
+            ModelPatcher._apply_model_layer_patch(
                 module_to_patch=module,
                 module_to_patch_key=module_key,
                 patch=layer,
@@ -99,7 +99,7 @@ class ModelPatcher:
 
     @staticmethod
     @torch.no_grad()
-    def _apply_lora_layer_patch(
+    def _apply_model_layer_patch(
         module_to_patch: torch.nn.Module,
         module_to_patch_key: str,
         patch: BaseLayerPatch,
@@ -146,7 +146,7 @@ class ModelPatcher:
     @staticmethod
     @torch.no_grad()
     @contextmanager
-    def apply_lora_sidecar_patches(
+    def apply_model_sidecar_patches(
         model: torch.nn.Module,
         patches: Iterable[Tuple[LoRAModelRaw, float]],
         prefix: str,
@@ -169,7 +169,7 @@ class ModelPatcher:
         original_modules: dict[str, torch.nn.Module] = {}
         try:
             for patch, patch_weight in patches:
-                ModelPatcher._apply_lora_sidecar_patch(
+                ModelPatcher._apply_model_sidecar_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -187,7 +187,7 @@ class ModelPatcher:
                 ModelPatcher._set_submodule(parent_module, module_name, orig_module)
 
     @staticmethod
-    def _apply_lora_sidecar_patch(
+    def _apply_model_sidecar_patch(
         model: torch.nn.Module,
         patch: LoRAModelRaw,
         patch_weight: float,
@@ -216,7 +216,7 @@ class ModelPatcher:
                 model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
             )
 
-            ModelPatcher._apply_lora_layer_wrapper_patch(
+            ModelPatcher._apply_model_layer_wrapper_patch(
                 model=model,
                 module_to_patch=module,
                 module_to_patch_key=module_key,
@@ -228,7 +228,7 @@ class ModelPatcher:
 
     @staticmethod
     @torch.no_grad()
-    def _apply_lora_layer_wrapper_patch(
+    def _apply_model_layer_wrapper_patch(
         model: torch.nn.Module,
         module_to_patch: torch.nn.Module,
         module_to_patch_key: str,

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -31,7 +31,7 @@ class LoRAExt(ExtensionBase):
     def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         lora_model = self._node_context.models.load(self._model_id).model
         assert isinstance(lora_model, LoRAModelRaw)
-        ModelPatcher.apply_lora_patch(
+        ModelPatcher.apply_model_patch(
             model=unet,
             prefix="lora_unet_",
             patch=lora_model,