Change attention processor apply logic

invokeai/backend/model_patcher.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
@@ -32,8 +32,27 @@ with LoRAHelper.apply_lora_unet(unet, loras):
 """
 
 
-# TODO: rename smth like ModelPatcher and add TI method?
 class ModelPatcher:
+    @staticmethod
+    @contextmanager
+    def patch_unet_attention_processor(unet: UNet2DConditionModel, processor_cls: Type[Any]):
+        """A context manager that patches `unet` with the provided attention processor.
+
+        Args:
+            unet (UNet2DConditionModel): The UNet model to patch.
+            processor (Type[Any]): Class which will be initialized for each key and passed to set_attn_processor(...).
+        """
+        unet_orig_processors = unet.attn_processors
+        try:
+            # create separate instance for each attention, to be able modify each attention separately
+            new_attn_processors = {key: processor_cls() for key in unet_orig_processors.keys()}
+            unet.set_attn_processor(new_attn_processors)
+
+            yield None
+
+        finally:
+            unet.set_attn_processor(unet_orig_processors)
+
     @staticmethod
     def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
         assert "." not in lora_key