diff --git a/invokeai/backend/peft/sdxl_format_utils.py b/invokeai/backend/peft/sdxl_format_utils.py
index 1e3a99db0e..af5adee8db 100644
--- a/invokeai/backend/peft/sdxl_format_utils.py
+++ b/invokeai/backend/peft/sdxl_format_utils.py
@@ -1,14 +1,15 @@
 import bisect
-from typing import Dict, List, Tuple
 
 import torch
 
 
-# code from
-# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
-def make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
-    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
-    unet_conversion_map_layer = []
+def make_sdxl_unet_conversion_map() -> list[tuple[str, str]]:
+    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format.
+
+    Ported from:
+    https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
+    """
+    unet_conversion_map_layer: list[tuple[str, str]] = []
 
     for i in range(3):  # num_blocks is 3 in sdxl
         # loop over downblocks/upblocks
@@ -66,7 +67,7 @@ def make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
         ("skip_connection.", "conv_shortcut."),
     ]
 
-    unet_conversion_map = []
+    unet_conversion_map: list[tuple[str, str]] = []
     for sd, hf in unet_conversion_map_layer:
         if "resnets" in hf:
             for sd_res, hf_res in unet_conversion_map_resnet:
@@ -96,8 +97,7 @@ SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
 }
 
 
-@classmethod
-def convert_sdxl_keys_to_diffusers_format(cls, state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+def convert_sdxl_keys_to_diffusers_format(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
     """Convert the keys of an SDXL LoRA state_dict to diffusers format.
 
     The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
@@ -124,7 +124,7 @@ def convert_sdxl_keys_to_diffusers_format(cls, state_dict: Dict[str, torch.Tenso
     stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
     stability_unet_keys.sort()
 
-    new_state_dict = {}
+    new_state_dict: dict[str, torch.Tensor] = {}
     for full_key, value in state_dict.items():
         if full_key.startswith("lora_unet_"):
             search_key = full_key.replace("lora_unet_", "")