diff --git a/lora_loading_patch.py b/lora_loading_patch.py
index 033e1c3..5e10eee 100644
--- a/lora_loading_patch.py
+++ b/lora_loading_patch.py
@@ -1,10 +1,19 @@
 # ruff: noqa
-from diffusers.utils import convert_unet_state_dict_to_peft, get_peft_kwargs, is_peft_version, get_adapter_name, logging
+from diffusers.utils import (
+    convert_unet_state_dict_to_peft,
+    get_peft_kwargs,
+    is_peft_version,
+    get_adapter_name,
+    logging,
+)
 
 logger = logging.get_logger(__name__)
 
+
 # patching inject_adapter_in_model and load_peft_state_dict with low_cpu_mem_usage=True until it's merged into diffusers
-def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None):
+def load_lora_into_transformer(
+    cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None
+):
     """
     This will load the LoRA layers specified in `state_dict` into `transformer`.
 
@@ -29,7 +38,9 @@ def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, ada
 
     transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
     state_dict = {
-        k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
+        k.replace(f"{cls.transformer_name}.", ""): v
+        for k, v in state_dict.items()
+        if k in transformer_keys
     }
 
     if len(state_dict.keys()) > 0:
@@ -50,10 +61,20 @@ def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, ada
 
         if network_alphas is not None and len(network_alphas) >= 1:
             prefix = cls.transformer_name
-            alpha_keys = [k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix]
-            network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
+            alpha_keys = [
+                k
+                for k in network_alphas.keys()
+                if k.startswith(prefix) and k.split(".")[0] == prefix
+            ]
+            network_alphas = {
+                k.replace(f"{prefix}.", ""): v
+                for k, v in network_alphas.items()
+                if k in alpha_keys
+            }
 
-        lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
+        lora_config_kwargs = get_peft_kwargs(
+            rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict
+        )
         if "use_dora" in lora_config_kwargs:
             if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                 raise ValueError(
@@ -69,10 +90,16 @@ def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, ada
 
         # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
         # otherwise loading LoRA weights will lead to an error
-        is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
+        is_model_cpu_offload, is_sequential_cpu_offload = (
+            cls._optionally_disable_offloading(_pipeline)
+        )
 
-        inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name, low_cpu_mem_usage=True)
-        incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name, low_cpu_mem_usage=True)
+        inject_adapter_in_model(
+            lora_config, transformer, adapter_name=adapter_name, low_cpu_mem_usage=True
+        )
+        incompatible_keys = set_peft_model_state_dict(
+            transformer, state_dict, adapter_name, low_cpu_mem_usage=True
+        )
 
         if incompatible_keys is not None:
             # check only for unexpected keys
diff --git a/predict.py b/predict.py
index f906dfa..866cb99 100644
--- a/predict.py
+++ b/predict.py
@@ -102,7 +102,9 @@ def setup(self) -> None:  # pyright: ignore
             "FLUX.1-dev",
             torch_dtype=torch.bfloat16,
         ).to("cuda")
-        dev_pipe.__class__.load_lora_into_transformer = classmethod(load_lora_into_transformer)
+        dev_pipe.__class__.load_lora_into_transformer = classmethod(
+            load_lora_into_transformer
+        )
 
         print("Loading Flux schnell pipeline")
         if not FLUX_SCHNELL_PATH.exists():
@@ -133,7 +135,9 @@ def setup(self) -> None:  # pyright: ignore
             tokenizer=dev_pipe.tokenizer,
             tokenizer_2=dev_pipe.tokenizer_2,
         ).to("cuda")
-        dev_img2img_pipe.__class__.load_lora_into_transformer = classmethod(load_lora_into_transformer)
+        dev_img2img_pipe.__class__.load_lora_into_transformer = classmethod(
+            load_lora_into_transformer
+        )
 
         print("Loading Flux schnell img2img pipeline")
         schnell_img2img_pipe = FluxImg2ImgPipeline(
@@ -162,7 +166,9 @@ def setup(self) -> None:  # pyright: ignore
             tokenizer=dev_pipe.tokenizer,
             tokenizer_2=dev_pipe.tokenizer_2,
         ).to("cuda")
-        dev_inpaint_pipe.__class__.load_lora_into_transformer = classmethod(load_lora_into_transformer)
+        dev_inpaint_pipe.__class__.load_lora_into_transformer = classmethod(
+            load_lora_into_transformer
+        )
 
         print("Loading Flux schnell inpaint pipeline")
         schnell_inpaint_pipe = FluxInpaintPipeline(
diff --git a/ruff.toml b/ruff.toml
index 0f7b5c0..f7e5bbf 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -27,7 +27,7 @@ exclude = [
     "site-packages",
     "venv",
     "ai-toolkit",
-    "LLaVA"
+    "LLaVA",
 ]
 
 # Same as Black.