diff --git a/invokeai/app/invocations/flux_text_to_image.py b/invokeai/app/invocations/flux_text_to_image.py
index 334e8fd1ea..ed744f441f 100644
--- a/invokeai/app/invocations/flux_text_to_image.py
+++ b/invokeai/app/invocations/flux_text_to_image.py
@@ -101,10 +101,7 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         # if the cache is not empty.
         # context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
 
-        with (
-            transformer_info as transformer,
-            scheduler_info as scheduler
-        ):
+        with transformer_info as transformer, scheduler_info as scheduler:
             assert isinstance(transformer, FluxTransformer2DModel)
             assert isinstance(scheduler, FlowMatchEulerDiscreteScheduler)
 
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index 4672f6a83d..c3902c1cb1 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -60,11 +60,11 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-
 class TransformerField(BaseModel):
     transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
     scheduler: ModelIdentifierField = Field(description="Info to load scheduler submodel")
 
+
 class T5EncoderField(BaseModel):
     tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
     text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
diff --git a/invokeai/backend/model_manager/load/model_util.py b/invokeai/backend/model_manager/load/model_util.py
index b3b78104d9..9794b8098e 100644
--- a/invokeai/backend/model_manager/load/model_util.py
+++ b/invokeai/backend/model_manager/load/model_util.py
@@ -52,9 +52,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         return model.calc_size()
     elif isinstance(
         model,
-        (
-            T5TokenizerFast,
-        ),
+        (T5TokenizerFast,),
     ):
         return len(model)
     else:
diff --git a/invokeai/backend/model_manager/util/select_hf_files.py b/invokeai/backend/model_manager/util/select_hf_files.py
index 2e86d9a62e..60abc3384c 100644
--- a/invokeai/backend/model_manager/util/select_hf_files.py
+++ b/invokeai/backend/model_manager/util/select_hf_files.py
@@ -54,7 +54,7 @@ def filter_files(
                 "lora_weights.safetensors",
                 "weights.pb",
                 "onnx_data",
-                "spiece.model", # Added for `black-forest-labs/FLUX.1-schnell`.
+                "spiece.model",  # Added for `black-forest-labs/FLUX.1-schnell`.
             )
         ):
             paths.append(file)
diff --git a/invokeai/backend/quantization/fast_quantized_diffusion_model.py b/invokeai/backend/quantization/fast_quantized_diffusion_model.py
index 395efc99c4..b1531094d1 100644
--- a/invokeai/backend/quantization/fast_quantized_diffusion_model.py
+++ b/invokeai/backend/quantization/fast_quantized_diffusion_model.py
@@ -19,7 +19,7 @@ from invokeai.backend.requantize import requantize
 
 class FastQuantizedDiffusersModel(QuantizedDiffusersModel):
     @classmethod
-    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike], base_class = FluxTransformer2DModel, **kwargs):
+    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike], base_class=FluxTransformer2DModel, **kwargs):
         """We override the `from_pretrained()` method in order to use our custom `requantize()` implementation."""
         base_class = base_class or cls.base_class
         if base_class is None:
diff --git a/invokeai/backend/quantization/fast_quantized_transformers_model.py b/invokeai/backend/quantization/fast_quantized_transformers_model.py
index 99f889b4af..5f16bae611 100644
--- a/invokeai/backend/quantization/fast_quantized_transformers_model.py
+++ b/invokeai/backend/quantization/fast_quantized_transformers_model.py
@@ -15,7 +15,9 @@ from invokeai.backend.requantize import requantize
 
 class FastQuantizedTransformersModel(QuantizedTransformersModel):
     @classmethod
-    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike], auto_class = AutoModelForTextEncoding, **kwargs):
+    def from_pretrained(
+        cls, model_name_or_path: Union[str, os.PathLike], auto_class=AutoModelForTextEncoding, **kwargs
+    ):
         """We override the `from_pretrained()` method in order to use our custom `requantize()` implementation."""
         auto_class = auto_class or cls.auto_class
         if auto_class is None: