Run Ruff

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:

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)

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:

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: