chore: ruff check - fix flake8-bugbear

invokeai/backend/install/migrate_to_3.py

@@ -123,8 +123,6 @@ class MigrateTo3(object):
                     logger.error(str(e))
                 except KeyboardInterrupt:
                     raise
-                except Exception as e:
-                    logger.error(str(e))
             for f in files:
                 # don't copy raw learned_embeds.bin or pytorch_lora_weights.bin
                 # let them be copied as part of a tree copy operation
@@ -143,8 +141,6 @@ class MigrateTo3(object):
                     logger.error(str(e))
                 except KeyboardInterrupt:
                     raise
-                except Exception as e:
-                    logger.error(str(e))
 
     def migrate_support_models(self):
         """

invokeai/backend/install/model_install_backend.py

@@ -176,7 +176,7 @@ class ModelInstall(object):
     # logic here a little reversed to maintain backward compatibility
     def starter_models(self, all_models: bool = False) -> Set[str]:
         models = set()
-        for key, value in self.datasets.items():
+        for key, _value in self.datasets.items():
             name, base, model_type = ModelManager.parse_key(key)
             if all_models or model_type in [ModelType.Main, ModelType.Vae]:
                 models.add(key)

invokeai/backend/ip_adapter/attention_processor.py

@@ -130,7 +130,7 @@ class IPAttnProcessor2_0(torch.nn.Module):
             assert ip_adapter_image_prompt_embeds is not None
             assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
 
-            for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales):
+            for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True):
                 # The batch dimensions should match.
                 assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
                 # The token_len dimensions should match.

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:

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -54,13 +54,13 @@ class Context:
         self.clear_requests(cleanup=True)
 
     def register_cross_attention_modules(self, model):
-        for name, module in get_cross_attention_modules(model, CrossAttentionType.SELF):
+        for name, _module in get_cross_attention_modules(model, CrossAttentionType.SELF):
             if name in self.self_cross_attention_module_identifiers:
-                assert False, f"name {name} cannot appear more than once"
+                raise AssertionError(f"name {name} cannot appear more than once")
             self.self_cross_attention_module_identifiers.append(name)
-        for name, module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
+        for name, _module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
             if name in self.tokens_cross_attention_module_identifiers:
-                assert False, f"name {name} cannot appear more than once"
+                raise AssertionError(f"name {name} cannot appear more than once")
             self.tokens_cross_attention_module_identifiers.append(name)
 
     def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
@@ -170,7 +170,7 @@ class Context:
             self.saved_cross_attention_maps = {}
 
     def offload_saved_attention_slices_to_cpu(self):
-        for key, map_dict in self.saved_cross_attention_maps.items():
+        for _key, map_dict in self.saved_cross_attention_maps.items():
             for offset, slice in map_dict["slices"].items():
                 map_dict[offset] = slice.to("cpu")
 
@@ -433,7 +433,7 @@ def inject_attention_function(unet, context: Context):
         module.identifier = identifier
         try:
             module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier))
+            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier)) # noqa: B023
         except AttributeError as e:
             if is_attribute_error_about(e, "set_attention_slice_wrangler"):
                 print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
@@ -445,7 +445,7 @@ def remove_attention_function(unet):
     cross_attention_modules = get_cross_attention_modules(
         unet, CrossAttentionType.TOKENS
     ) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
-    for identifier, module in cross_attention_modules:
+    for _identifier, module in cross_attention_modules:
         try:
             # clear wrangler callback
             module.set_attention_slice_wrangler(None)

invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py

@@ -56,7 +56,7 @@ class AttentionMapSaver:
 
         merged = None
 
-        for key, maps in self.collated_maps.items():
+        for _key, maps in self.collated_maps.items():
             # maps has shape [(H*W), N] for N tokens
             # but we want [N, H, W]
             this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -123,7 +123,7 @@ class InvokeAIDiffuserComponent:
         # control_data should be type List[ControlNetData]
         # this loop covers both ControlNet (one ControlNetData in list)
         #      and MultiControlNet (multiple ControlNetData in list)
-        for i, control_datum in enumerate(control_data):
+        for _i, control_datum in enumerate(control_data):
             control_mode = control_datum.control_mode
             # soft_injection and cfg_injection are the two ControlNet control_mode booleans
             #     that are combined at higher level to make control_mode enum
@@ -214,7 +214,7 @@ class InvokeAIDiffuserComponent:
                     # add controlnet outputs together if have multiple controlnets
                     down_block_res_samples = [
                         samples_prev + samples_curr
-                        for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
+                        for samples_prev, samples_curr in zip(down_block_res_samples, down_samples, strict=True)
                     ]
                     mid_block_res_sample += mid_sample
 

invokeai/backend/util/hotfixes.py

@@ -732,7 +732,7 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
 
         controlnet_down_block_res_samples = ()
 
-        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks, strict=True):
             down_block_res_sample = controlnet_block(down_block_res_sample)
             controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
 
@@ -745,7 +745,7 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
             scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
 
             scales = scales * conditioning_scale
-            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
+            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales, strict=True)]
             mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
         else:
             down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]