chore: ruff check - fix flake8-bugbear

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -269,7 +269,7 @@ def create_unet_diffusers_config(original_config, image_size: int, controlnet=Fa
             resolution *= 2
 
     up_block_types = []
-    for i in range(len(block_out_channels)):
+    for _i in range(len(block_out_channels)):
         block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
         up_block_types.append(block_type)
         resolution //= 2
@@ -1223,7 +1223,7 @@ def download_from_original_stable_diffusion_ckpt(
             # scan model
             scan_result = scan_file_path(checkpoint_path)
             if scan_result.infected_files != 0:
-                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
+                raise Exception("The model {checkpoint_path} is potentially infected by malware. Aborting import.")
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
             checkpoint = torch.load(checkpoint_path, map_location=device)
@@ -1664,7 +1664,7 @@ def download_controlnet_from_original_ckpt(
             # scan model
             scan_result = scan_file_path(checkpoint_path)
             if scan_result.infected_files != 0:
-                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
+                raise Exception("The model {checkpoint_path} is potentially infected by malware. Aborting import.")
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
             checkpoint = torch.load(checkpoint_path, map_location=device)

invokeai/backend/model_management/lora.py

@@ -242,7 +242,7 @@ class ModelPatcher:
     ):
         skipped_layers = []
         try:
-            for i in range(clip_skip):
+            for _i in range(clip_skip):
                 skipped_layers.append(text_encoder.text_model.encoder.layers.pop(-1))
 
             yield

invokeai/backend/model_management/model_load_optimizations.py

@@ -26,5 +26,5 @@ def skip_torch_weight_init():
 
         yield None
     finally:
-        for torch_module, saved_function in zip(torch_modules, saved_functions):
+        for torch_module, saved_function in zip(torch_modules, saved_functions, strict=True):
             torch_module.reset_parameters = saved_function

invokeai/backend/model_management/model_manager.py

@@ -655,7 +655,7 @@ class ModelManager(object):
         """
         # TODO: redo
         for model_dict in self.list_models():
-            for model_name, model_info in model_dict.items():
+            for _model_name, model_info in model_dict.items():
                 line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}'
                 print(line)
 

invokeai/backend/model_management/model_probe.py

@@ -237,7 +237,7 @@ class ModelProbe(object):
         # scan model
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
-            raise "The model {model_name} is potentially infected by malware. Aborting import."
+            raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
 
 
 # ##################################################3

invokeai/backend/model_management/models/__init__.py

@@ -109,7 +109,7 @@ class OpenAPIModelInfoBase(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-for base_model, models in MODEL_CLASSES.items():
+for _base_model, models in MODEL_CLASSES.items():
     for model_type, model_class in models.items():
         model_configs = set(model_class._get_configs().values())
         model_configs.discard(None)

invokeai/backend/model_management/models/base.py

@@ -153,7 +153,7 @@ class ModelBase(metaclass=ABCMeta):
 
         else:
             res_type = sys.modules["diffusers"]
-            res_type = getattr(res_type, "pipelines")
+            res_type = res_type.pipelines
 
         for subtype in subtypes:
             res_type = getattr(res_type, subtype)

invokeai/backend/model_management/models/lora.py

@@ -462,7 +462,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
         dtype: Optional[torch.dtype] = None,
     ):
         # TODO: try revert if exception?
-        for key, layer in self.layers.items():
+        for _key, layer in self.layers.items():
             layer.to(device=device, dtype=dtype)
 
     def calc_size(self) -> int: