chore: ruff

invokeai/app/invocations/baseinvocation.py

@@ -587,9 +587,9 @@ def invocation(
         for field_name, field_info in cls.model_fields.items():
             annotation = field_info.annotation
             assert annotation is not None, f"{field_name} on invocation {invocation_type} has no type annotation."
-            assert isinstance(
-                field_info.json_schema_extra, dict
-            ), f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            assert isinstance(field_info.json_schema_extra, dict), (
+                f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            )
 
             original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
 
@@ -712,9 +712,9 @@ def invocation_output(
         for field_name, field_info in cls.model_fields.items():
             annotation = field_info.annotation
             assert annotation is not None, f"{field_name} on invocation output {output_type} has no type annotation."
-            assert isinstance(
-                field_info.json_schema_extra, dict
-            ), f"{field_name} on invocation output {output_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            assert isinstance(field_info.json_schema_extra, dict), (
+                f"{field_name} on invocation output {output_type} has a non-dict json_schema_extra, did you forget to use InputField?"
+            )
 
             cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
 

invokeai/app/invocations/segment_anything.py

@@ -184,9 +184,9 @@ class SegmentAnythingInvocation(BaseInvocation):
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert (
-                bounding_boxes is not None
-            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            assert bounding_boxes is not None, (
+                "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            )
             assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most

invokeai/app/services/config/config_default.py

@@ -482,9 +482,9 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     try:
         # Meta is not included in the model fields, so we need to validate it separately
         config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-        assert (
-            config.schema_version == CONFIG_SCHEMA_VERSION
-        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        assert config.schema_version == CONFIG_SCHEMA_VERSION, (
+            f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        )
         return config
     except Exception as e:
         raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -379,13 +379,13 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 bytes_ = path.read_bytes()
                 workflow_from_file = WorkflowValidator.validate_json(bytes_)
 
-                assert workflow_from_file.id.startswith(
-                    "default_"
-                ), f'Invalid default workflow ID (must start with "default_"): {workflow_from_file.id}'
+                assert workflow_from_file.id.startswith("default_"), (
+                    f'Invalid default workflow ID (must start with "default_"): {workflow_from_file.id}'
+                )
 
-                assert (
-                    workflow_from_file.meta.category is WorkflowCategory.Default
-                ), f"Invalid default workflow category: {workflow_from_file.meta.category}"
+                assert workflow_from_file.meta.category is WorkflowCategory.Default, (
+                    f"Invalid default workflow category: {workflow_from_file.meta.category}"
+                )
 
                 workflows_from_file.append(workflow_from_file)
 

invokeai/backend/model_manager/merge.py

@@ -115,19 +115,19 @@ class ModelMerger(object):
         base_models: Set[BaseModelType] = set()
         variant = None if self._installer.app_config.precision == "float32" else "fp16"
 
-        assert (
-            len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference
-        ), "When merging three models, only the 'add_difference' merge method is supported"
+        assert len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference, (
+            "When merging three models, only the 'add_difference' merge method is supported"
+        )
 
         for key in model_keys:
             info = store.get_model(key)
             model_names.append(info.name)
-            assert isinstance(
-                info, MainDiffusersConfig
-            ), f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
-            assert info.variant == ModelVariantType(
-                "normal"
-            ), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            assert isinstance(info, MainDiffusersConfig), (
+                f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
+            )
+            assert info.variant == ModelVariantType("normal"), (
+                f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            )
 
             # tally base models used
             base_models.add(info.base)

tests/app/services/download/test_download_queue.py

@@ -211,12 +211,12 @@ def test_multifile_download(tmp_path: Path, mm2_session: Session) -> None:
     assert job.bytes > 0, "expected download bytes to be positive"
     assert job.bytes == job.total_bytes, "expected download bytes to equal total bytes"
     assert job.download_path == tmp_path / "sdxl-turbo"
-    assert Path(
-        tmp_path, "sdxl-turbo/model_index.json"
-    ).exists(), f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
-    assert Path(
-        tmp_path, "sdxl-turbo/text_encoder/config.json"
-    ).exists(), f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
+    assert Path(tmp_path, "sdxl-turbo/model_index.json").exists(), (
+        f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
+    )
+    assert Path(tmp_path, "sdxl-turbo/text_encoder/config.json").exists(), (
+        f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
+    )
 
     assert events == {DownloadJobStatus.RUNNING, DownloadJobStatus.COMPLETED}
     queue.stop()

tests/backend/patches/lora_conversions/test_flux_aitoolkit_lora_conversion_utils.py

@@ -48,9 +48,9 @@ def test_flux_aitoolkit_transformer_state_dict_is_in_invoke_format():
     model_keys = set(model.state_dict().keys())
 
     for converted_key_prefix in converted_key_prefixes:
-        assert any(
-            model_key.startswith(converted_key_prefix) for model_key in model_keys
-        ), f"'{converted_key_prefix}' did not match any model keys."
+        assert any(model_key.startswith(converted_key_prefix) for model_key in model_keys), (
+            f"'{converted_key_prefix}' did not match any model keys."
+        )
 
 
 def test_lora_model_from_flux_aitoolkit_state_dict():