further changes for ruff

invokeai/app/api/dependencies.py

@@ -88,7 +88,9 @@ class ApiDependencies:
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
-        model_install_service = ModelInstallService(app_config=config, record_store=model_record_service, event_bus=events)
+        model_install_service = ModelInstallService(
+            app_config=config, record_store=model_record_service, event_bus=events
+        )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
         processor = DefaultInvocationProcessor()

invokeai/app/api/routers/model_records.py

@@ -51,7 +51,9 @@ async def list_model_records(
     found_models: list[AnyModelConfig] = []
     if base_models:
         for base_model in base_models:
-            found_models.extend(record_store.search_by_attr(base_model=base_model, model_type=model_type, model_name=model_name))
+            found_models.extend(
+                record_store.search_by_attr(base_model=base_model, model_type=model_type, model_name=model_name)
+            )
     else:
         found_models.extend(record_store.search_by_attr(model_type=model_type, model_name=model_name))
     return ModelsList(models=found_models)
@@ -250,14 +252,16 @@ async def import_model(
         raise HTTPException(status_code=409, detail=str(e))
     return result
 
+
 @model_records_router.get(
     "/import",
     operation_id="list_model_install_jobs",
 )
 async def list_model_install_jobs(
-        source: Optional[str] = Query(description="Filter list by install source, partial string match.",
+    source: Optional[str] = Query(
+        description="Filter list by install source, partial string match.",
         default=None,
-                                      )
+    ),
 ) -> List[ModelInstallJob]:
     """
     Return list of model install jobs.
@@ -268,6 +272,7 @@ async def list_model_install_jobs(
     jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs(source)
     return jobs
 
+
 @model_records_router.patch(
     "/import",
     operation_id="prune_model_install_jobs",
@@ -276,14 +281,14 @@ async def list_model_install_jobs(
         400: {"description": "Bad request"},
     },
 )
-async def prune_model_install_jobs(
+async def prune_model_install_jobs() -> Response:
-) -> Response:
     """
     Prune all completed and errored jobs from the install job list.
     """
     ApiDependencies.invoker.services.model_install.prune_jobs()
     return Response(status_code=204)
 
+
 @model_records_router.patch(
     "/sync",
     operation_id="sync_models_to_config",
@@ -292,8 +297,7 @@ async def prune_model_install_jobs(
         400: {"description": "Bad request"},
     },
 )
-async def sync_models_to_config(
+async def sync_models_to_config() -> Response:
-) -> Response:
     """
     Traverse the models and autoimport directories. Model files without a corresponding
     record in the database are added. Orphan records without a models file are deleted.

invokeai/app/api/sockets.py

@@ -37,9 +37,5 @@ class SocketIO:
         if "queue_id" in data:
             await self.__sio.leave_room(sid, data["queue_id"])
 
-
     async def _handle_model_event(self, event: Event) -> None:
-        await self.__sio.emit(
+        await self.__sio.emit(event=event[1]["event"], data=event[1]["data"])
-            event=event[1]["event"],
-            data=event[1]["data"]
-        )

invokeai/app/services/config/__init__.py

@@ -2,4 +2,4 @@
 
 from .config_default import InvokeAIAppConfig, get_invokeai_config
 
-__all__ = ['InvokeAIAppConfig', 'get_invokeai_config']
+__all__ = ["InvokeAIAppConfig", "get_invokeai_config"]

invokeai/app/services/events/events_base.py

@@ -331,9 +331,7 @@ class EventServiceBase:
         """
         self.__emit_model_event(
             event_name="model_install_started",
-            payload={
+            payload={"source": source},
-                "source": source
-            },
         )
 
     def emit_model_install_completed(self, source: str, key: str) -> None:
@@ -351,7 +349,8 @@ class EventServiceBase:
             },
         )
 
-    def emit_model_install_progress(self,
+    def emit_model_install_progress(
+        self,
         source: str,
         current_bytes: int,
         total_bytes: int,
@@ -373,8 +372,8 @@ class EventServiceBase:
             },
         )
 
-
+    def emit_model_install_error(
-    def emit_model_install_error(self,
+        self,
         source: str,
         error_type: str,
         error: str,

invokeai/app/services/model_install/__init__.py

@@ -9,10 +9,11 @@ from .model_install_base import (
 )
 from .model_install_default import ModelInstallService
 
-__all__ = ['ModelInstallServiceBase',
+__all__ = [
-           'ModelInstallService',
+    "ModelInstallServiceBase",
-           'InstallStatus',
+    "ModelInstallService",
-           'ModelInstallJob',
+    "InstallStatus",
-           'UnknownInstallJobException',
+    "ModelInstallJob",
-           'ModelSource',
+    "UnknownInstallJobException",
-           ]
+    "ModelSource",
+]

invokeai/app/services/model_install/model_install_base.py

@@ -32,10 +32,17 @@ ModelSource = Union[str, Path, AnyHttpUrl]
 
 class ModelInstallJob(BaseModel):
     """Object that tracks the current status of an install request."""
+
     status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
-    config_in: Dict[str, Any] = Field(default_factory=dict, description="Configuration information (e.g. 'description') to apply to model.")
+    config_in: Dict[str, Any] = Field(
-    config_out: Optional[AnyModelConfig] = Field(default=None, description="After successful installation, this will hold the configuration object.")
+        default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
-    inplace: bool = Field(default=False, description="Leave model in its current location; otherwise install under models directory")
+    )
+    config_out: Optional[AnyModelConfig] = Field(
+        default=None, description="After successful installation, this will hold the configuration object."
+    )
+    inplace: bool = Field(
+        default=False, description="Leave model in its current location; otherwise install under models directory"
+    )
     source: ModelSource = Field(description="Source (URL, repo_id, or local path) of model")
     local_path: Path = Field(description="Path to locally-downloaded model; may be the same as the source")
     error_type: Optional[str] = Field(default=None, description="Class name of the exception that led to status==ERROR")
@@ -189,7 +196,7 @@ class ModelInstallServiceBase(ABC):
         """Return the ModelInstallJob corresponding to the provided source."""
 
     @abstractmethod
-    def list_jobs(self, source: Optional[ModelSource]=None) -> List[ModelInstallJob]:  # noqa D102
+    def list_jobs(self, source: Optional[ModelSource] = None) -> List[ModelInstallJob]:  # noqa D102
         """
         List active and complete install jobs.
 

invokeai/app/services/model_install/model_install_default.py

@@ -46,10 +46,11 @@ class ModelInstallService(ModelInstallServiceBase):
     _cached_model_paths: Set[Path]
     _models_installed: Set[str]
 
-    def __init__(self,
+    def __init__(
+        self,
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
-                 event_bus: Optional[EventServiceBase] = None
+        event_bus: Optional[EventServiceBase] = None,
     ):
         """
         Initialize the installer object.
@@ -135,8 +136,8 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or {}
-        if config.get('source') is None:
+        if config.get("source") is None:
-            config['source'] = model_path.resolve().as_posix()
+            config["source"] = model_path.resolve().as_posix()
         return self._register(model_path, config)
 
     def install_path(
@@ -146,8 +147,8 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or {}
-        if config.get('source') is None:
+        if config.get("source") is None:
-            config['source'] = model_path.resolve().as_posix()
+            config["source"] = model_path.resolve().as_posix()
 
         info: AnyModelConfig = self._probe_model(Path(model_path), config)
 
@@ -181,7 +182,8 @@ class ModelInstallService(ModelInstallServiceBase):
 
         # Installing a local path
         if isinstance(source, (str, Path)) and Path(source).exists():  # a path that is already on disk
-            job = ModelInstallJob(config_in=config,
+            job = ModelInstallJob(
+                config_in=config,
                 source=source,
                 inplace=inplace,
                 local_path=Path(source),
@@ -193,7 +195,7 @@ class ModelInstallService(ModelInstallServiceBase):
         else:  # here is where we'd download a URL or repo_id. Implementation pending download queue.
             raise UnknownModelException("File or directory not found")
 
-    def list_jobs(self, source: Optional[ModelSource]=None) -> List[ModelInstallJob]:  # noqa D102
+    def list_jobs(self, source: Optional[ModelSource] = None) -> List[ModelInstallJob]:  # noqa D102
         jobs = self._install_jobs
         if not source:
             return list(jobs.values())
@@ -205,7 +207,7 @@ class ModelInstallService(ModelInstallServiceBase):
         try:
             return self._install_jobs[source]
         except KeyError:
-            raise UnknownInstallJobException(f'{source}: unknown install job')
+            raise UnknownInstallJobException(f"{source}: unknown install job")
 
     def wait_for_installs(self) -> Dict[ModelSource, ModelInstallJob]:  # noqa D102
         self._install_queue.join()
@@ -213,7 +215,9 @@ class ModelInstallService(ModelInstallServiceBase):
 
     def prune_jobs(self) -> None:
         """Prune all completed and errored jobs."""
-        finished_jobs = [source for source in self._install_jobs
+        finished_jobs = [
+            source
+            for source in self._install_jobs
             if self._install_jobs[source].status in [InstallStatus.COMPLETED, InstallStatus.ERROR]
         ]
         for source in finished_jobs:
@@ -295,7 +299,6 @@ class ModelInstallService(ModelInstallServiceBase):
         self.record_store.update_model(key, model)
         return model
 
-
     def _scan_register(self, model: Path) -> bool:
         if model in self._cached_model_paths:
             return True
@@ -308,7 +311,6 @@ class ModelInstallService(ModelInstallServiceBase):
             pass
         return True
 
-
     def _scan_install(self, model: Path) -> bool:
         if model in self._cached_model_paths:
             return True
@@ -378,11 +380,9 @@ class ModelInstallService(ModelInstallServiceBase):
     def _create_key(self) -> str:
         return sha256(randbytes(100)).hexdigest()[0:32]
 
-    def _register(self,
+    def _register(
-                  model_path: Path,
+        self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
-                  config: Optional[Dict[str, Any]] = None,
+    ) -> str:
-                  info: Optional[AnyModelConfig] = None) -> str:
-
         info = info or ModelProbe.probe(model_path, config)
         key = self._create_key()
 
@@ -393,7 +393,7 @@ class ModelInstallService(ModelInstallServiceBase):
         info.path = model_path.as_posix()
 
         # add 'main' specific fields
-        if hasattr(info, 'config'):
+        if hasattr(info, "config"):
             # make config relative to our root
             legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config).resolve()
             info.config = legacy_conf.relative_to(self.app_config.root_dir).as_posix()

invokeai/app/services/model_records/__init__.py

@@ -8,9 +8,9 @@ from .model_records_base import (  # noqa F401
 from .model_records_sql import ModelRecordServiceSQL  # noqa F401
 
 __all__ = [
-    'ModelRecordServiceBase',
+    "ModelRecordServiceBase",
-    'ModelRecordServiceSQL',
+    "ModelRecordServiceSQL",
-    'DuplicateModelException',
+    "DuplicateModelException",
-    'InvalidModelException',
+    "InvalidModelException",
-    'UnknownModelException',
+    "UnknownModelException",
 ]

invokeai/backend/model_management/model_probe.py

@@ -123,8 +123,8 @@ class ModelProbe(object):
                 base_type=base_type,
                 variant_type=variant_type,
                 prediction_type=prediction_type,
-                name = name,
+                name=name,
-                description = description,
+                description=description,
                 upcast_attention=(
                     base_type == BaseModelType.StableDiffusion2
                     and prediction_type == SchedulerPredictionType.VPrediction
@@ -150,7 +150,7 @@ class ModelProbe(object):
 
     @classmethod
     def get_model_name(cls, model_path: Path) -> str:
-        if model_path.suffix in {'.safetensors', '.bin', '.pt', '.ckpt'}:
+        if model_path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
             return model_path.stem
         else:
             return model_path.name

invokeai/backend/model_manager/__init__.py

@@ -14,15 +14,16 @@ from .config import (
 from .probe import ModelProbe
 from .search import ModelSearch
 
-__all__ = ['ModelProbe', 'ModelSearch',
+__all__ = [
-           'InvalidModelConfigException',
+    "ModelProbe",
-           'ModelConfigFactory',
+    "ModelSearch",
-           'BaseModelType',
+    "InvalidModelConfigException",
-           'ModelType',
+    "ModelConfigFactory",
-           'SubModelType',
+    "BaseModelType",
-           'ModelVariantType',
+    "ModelType",
-           'ModelFormat',
+    "SubModelType",
-           'SchedulerPredictionType',
+    "ModelVariantType",
-           'AnyModelConfig',
+    "ModelFormat",
-           ]
+    "SchedulerPredictionType",
-
+    "AnyModelConfig",
+]

invokeai/backend/model_manager/probe.py

@@ -49,6 +49,7 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
     },
 }
 
+
 class ProbeBase(object):
     """Base class for probes."""
 
@@ -71,6 +72,7 @@ class ProbeBase(object):
         """Get model scheduler prediction type."""
         return None
 
+
 class ModelProbe(object):
     PROBES: Dict[str, Dict[ModelType, type[ProbeBase]]] = {
         "diffusers": {},
@@ -138,29 +140,38 @@ class ModelProbe(object):
         hash = FastModelHash.hash(model_path)
         probe = probe_class(model_path)
 
-        fields['path'] = model_path.as_posix()
+        fields["path"] = model_path.as_posix()
-        fields['type'] = fields.get('type') or model_type
+        fields["type"] = fields.get("type") or model_type
-        fields['base'] = fields.get('base') or probe.get_base_type()
+        fields["base"] = fields.get("base") or probe.get_base_type()
-        fields['variant'] = fields.get('variant') or probe.get_variant_type()
+        fields["variant"] = fields.get("variant") or probe.get_variant_type()
-        fields['prediction_type'] = fields.get('prediction_type') or probe.get_scheduler_prediction_type()
+        fields["prediction_type"] = fields.get("prediction_type") or probe.get_scheduler_prediction_type()
-        fields['name'] = fields.get('name') or cls.get_model_name(model_path)
+        fields["name"] = fields.get("name") or cls.get_model_name(model_path)
-        fields['description'] = fields.get('description') or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
+        fields["description"] = (
-        fields['format'] = fields.get('format') or probe.get_format()
+            fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
-        fields['original_hash'] = fields.get('original_hash') or hash
+        )
-        fields['current_hash'] = fields.get('current_hash') or hash
+        fields["format"] = fields.get("format") or probe.get_format()
+        fields["original_hash"] = fields.get("original_hash") or hash
+        fields["current_hash"] = fields.get("current_hash") or hash
 
         # additional fields needed for main and controlnet models
-        if fields['type'] in [ModelType.Main, ModelType.ControlNet] and fields['format'] == ModelFormat.Checkpoint:
+        if fields["type"] in [ModelType.Main, ModelType.ControlNet] and fields["format"] == ModelFormat.Checkpoint:
-            fields['config'] = cls._get_checkpoint_config_path(model_path,
+            fields["config"] = cls._get_checkpoint_config_path(
-                                                               model_type=fields['type'],
+                model_path,
-                                                               base_type=fields['base'],
+                model_type=fields["type"],
-                                                               variant_type=fields['variant'],
+                base_type=fields["base"],
-                                                               prediction_type=fields['prediction_type']).as_posix()
+                variant_type=fields["variant"],
+                prediction_type=fields["prediction_type"],
+            ).as_posix()
 
         # additional fields needed for main non-checkpoint models
-        elif fields['type'] == ModelType.Main and fields['format'] in [ModelFormat.Onnx, ModelFormat.Olive, ModelFormat.Diffusers]:
+        elif fields["type"] == ModelType.Main and fields["format"] in [
-            fields['upcast_attention'] = fields.get('upcast_attention') or (
+            ModelFormat.Onnx,
-                fields['base'] == BaseModelType.StableDiffusion2 and fields['prediction_type'] == SchedulerPredictionType.VPrediction
+            ModelFormat.Olive,
+            ModelFormat.Diffusers,
+        ]:
+            fields["upcast_attention"] = fields.get("upcast_attention") or (
+                fields["base"] == BaseModelType.StableDiffusion2
+                and fields["prediction_type"] == SchedulerPredictionType.VPrediction
             )
 
         model_info = ModelConfigFactory.make_config(fields)
@@ -168,7 +179,7 @@ class ModelProbe(object):
 
     @classmethod
     def get_model_name(cls, model_path: Path) -> str:
-        if model_path.suffix in {'.safetensors', '.bin', '.pt', '.ckpt'}:
+        if model_path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
             return model_path.stem
         else:
             return model_path.name
@@ -247,13 +258,14 @@ class ModelProbe(object):
         )
 
     @classmethod
-    def _get_checkpoint_config_path(cls,
+    def _get_checkpoint_config_path(
+        cls,
         model_path: Path,
         model_type: ModelType,
         base_type: BaseModelType,
         variant_type: ModelVariantType,
-                                    prediction_type: SchedulerPredictionType) -> Path:
+        prediction_type: SchedulerPredictionType,
-
+    ) -> Path:
         # look for a YAML file adjacent to the model file first
         possible_conf = model_path.with_suffix(".yaml")
         if possible_conf.exists():
@@ -264,9 +276,13 @@ class ModelProbe(object):
             if isinstance(config_file, dict):  # need another tier for sd-2.x models
                 config_file = config_file[prediction_type]
         elif model_type == ModelType.ControlNet:
-            config_file = "../controlnet/cldm_v15.yaml" if base_type == BaseModelType("sd-1") else "../controlnet/cldm_v21.yaml"
+            config_file = (
+                "../controlnet/cldm_v15.yaml" if base_type == BaseModelType("sd-1") else "../controlnet/cldm_v21.yaml"
+            )
         else:
-            raise InvalidModelConfigException(f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}")
+            raise InvalidModelConfigException(
+                f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}"
+            )
         assert isinstance(config_file, str)
         return Path(config_file)
 
@@ -297,6 +313,7 @@ class ModelProbe(object):
 # Checkpoint probing
 # ##################################################3
 
+
 class CheckpointProbeBase(ProbeBase):
     def __init__(self, model_path: Path):
         super().__init__(model_path)
@@ -446,7 +463,6 @@ class T2IAdapterCheckpointProbe(CheckpointProbeBase):
 # classes for probing folders
 #######################################################
 class FolderProbeBase(ProbeBase):
-
     def get_variant_type(self) -> ModelVariantType:
         return ModelVariantType.Normal
 
@@ -537,7 +553,9 @@ class TextualInversionFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         path = self.model_path / "learned_embeds.bin"
         if not path.exists():
-            raise InvalidModelConfigException(f"{self.model_path.as_posix()} does not contain expected 'learned_embeds.bin' file")
+            raise InvalidModelConfigException(
+                f"{self.model_path.as_posix()} does not contain expected 'learned_embeds.bin' file"
+            )
         return TextualInversionCheckpointProbe(path).get_base_type()
 
 
@@ -608,7 +626,9 @@ class IPAdapterFolderProbe(FolderProbeBase):
         elif cross_attention_dim == 2048:
             return BaseModelType.StableDiffusionXL
         else:
-            raise InvalidModelConfigException(f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}.")
+            raise InvalidModelConfigException(
+                f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}."
+            )
 
 
 class CLIPVisionFolderProbe(FolderProbeBase):

invokeai/backend/util/__init__.py

@@ -14,4 +14,4 @@ from .devices import (  # noqa: F401
 from .logging import InvokeAILogger
 from .util import Chdir, ask_user, download_with_resume, instantiate_from_config, url_attachment_name  # noqa: F401
 
-__all__ = ['Chdir', 'InvokeAILogger', 'choose_precision', 'choose_torch_device']
+__all__ = ["Chdir", "InvokeAILogger", "choose_precision", "choose_torch_device"]

tests/app/services/model_install/test_model_install.py

@@ -44,9 +44,9 @@ def store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
 
 
 @pytest.fixture
-def installer(app_config: InvokeAIAppConfig,
+def installer(app_config: InvokeAIAppConfig, store: ModelRecordServiceBase) -> ModelInstallServiceBase:
-              store: ModelRecordServiceBase) -> ModelInstallServiceBase:
+    return ModelInstallService(
-    return ModelInstallService(app_config=app_config,
+        app_config=app_config,
         record_store=store,
         event_bus=DummyEventService(),
     )
@@ -70,10 +70,8 @@ class DummyEventService(EventServiceBase):
 
     def dispatch(self, event_name: str, payload: Any) -> None:
         """Dispatch an event by appending it to self.events."""
-        self.events.append(
+        self.events.append(DummyEvent(event_name=payload["event"], payload=payload["data"]))
-            DummyEvent(event_name=payload['event'],
+
-                       payload=payload['data'])
-        )
 
 def test_registration(installer: ModelInstallServiceBase, test_file: Path) -> None:
     store = installer.record_store
@@ -83,6 +81,7 @@ def test_registration(installer: ModelInstallServiceBase, test_file: Path) -> No
     assert key is not None
     assert len(key) == 32
 
+
 def test_registration_meta(installer: ModelInstallServiceBase, test_file: Path) -> None:
     store = installer.record_store
     key = installer.register_path(test_file)
@@ -91,31 +90,30 @@ def test_registration_meta(installer: ModelInstallServiceBase, test_file: Path)
     assert model_record.name == "test_embedding"
     assert model_record.type == ModelType.TextualInversion
     assert Path(model_record.path) == test_file
-    assert model_record.base == BaseModelType('sd-1')
+    assert model_record.base == BaseModelType("sd-1")
     assert model_record.description is not None
     assert model_record.source is not None
     assert Path(model_record.source) == test_file
 
+
 def test_registration_meta_override_fail(installer: ModelInstallServiceBase, test_file: Path) -> None:
     key = None
     with pytest.raises(ValidationError):
         key = installer.register_path(test_file, {"name": "banana_sushi", "type": ModelType("lora")})
     assert key is None
 
+
 def test_registration_meta_override_succeed(installer: ModelInstallServiceBase, test_file: Path) -> None:
     store = installer.record_store
-    key = installer.register_path(test_file,
+    key = installer.register_path(
-                                  {
+        test_file, {"name": "banana_sushi", "source": "fake/repo_id", "current_hash": "New Hash"}
-                                      "name": "banana_sushi",
-                                      "source": "fake/repo_id",
-                                      "current_hash": "New Hash"
-                                  }
     )
     model_record = store.get_model(key)
     assert model_record.name == "banana_sushi"
     assert model_record.source == "fake/repo_id"
     assert model_record.current_hash == "New Hash"
 
+
 def test_install(installer: ModelInstallServiceBase, test_file: Path, app_config: InvokeAIAppConfig) -> None:
     store = installer.record_store
     key = installer.install_path(test_file)
@@ -123,6 +121,7 @@ def test_install(installer: ModelInstallServiceBase, test_file: Path, app_config
     assert model_record.path == "sd-1/embedding/test_embedding.safetensors"
     assert model_record.source == test_file.as_posix()
 
+
 def test_background_install(installer: ModelInstallServiceBase, test_file: Path, app_config: InvokeAIAppConfig) -> None:
     """Note: may want to break this down into several smaller unit tests."""
     source = test_file
@@ -167,6 +166,7 @@ def test_background_install(installer: ModelInstallServiceBase, test_file: Path,
     with pytest.raises(UnknownInstallJobException):
         assert installer.get_job(source)
 
+
 def test_delete_install(installer: ModelInstallServiceBase, test_file: Path, app_config: InvokeAIAppConfig):
     store = installer.record_store
     key = installer.install_path(test_file)
@@ -174,11 +174,14 @@ def test_delete_install(installer: ModelInstallServiceBase, test_file: Path, app
     assert Path(app_config.models_dir / model_record.path).exists()
     assert test_file.exists()  # original should still be there after installation
     installer.delete(key)
-    assert not Path(app_config.models_dir / model_record.path).exists()  # after deletion, installed copy should not exist
+    assert not Path(
+        app_config.models_dir / model_record.path
+    ).exists()  # after deletion, installed copy should not exist
     assert test_file.exists()  # but original should still be there
     with pytest.raises(UnknownModelException):
         store.get_model(key)
 
+
 def test_delete_register(installer: ModelInstallServiceBase, test_file: Path, app_config: InvokeAIAppConfig):
     store = installer.record_store
     key = installer.register_path(test_file)