fix a number of typechecking errors

invokeai/app/api/routers/download_queue.py

@@ -36,7 +36,7 @@ async def list_downloads() -> List[DownloadJob]:
         400: {"description": "Bad request"},
     },
 )
-async def prune_downloads():
+async def prune_downloads() -> Response:
     """Prune completed and errored jobs."""
     queue = ApiDependencies.invoker.services.download_queue
     queue.prune_jobs()
@@ -87,7 +87,7 @@ async def get_download_job(
 )
 async def cancel_download_job(
     id: int = Path(description="ID of the download job to cancel."),
-):
+) -> Response:
     """Cancel a download job using its ID."""
     try:
         queue = ApiDependencies.invoker.services.download_queue
@@ -105,7 +105,7 @@ async def cancel_download_job(
         204: {"description": "Download jobs have been cancelled"},
     },
 )
-async def cancel_all_download_jobs():
+async def cancel_all_download_jobs() -> Response:
     """Cancel all download jobs."""
     ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
     return Response(status_code=204)

invokeai/app/api/routers/model_manager_v2.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, List, Optional, Set
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
@@ -37,6 +37,35 @@ from ..dependencies import ApiDependencies
 
 model_manager_v2_router = APIRouter(prefix="/v2/models", tags=["model_manager_v2"])
 
+example_model_output = {
+    "path": "sd-1/main/openjourney",
+    "name": "openjourney",
+    "base": "sd-1",
+    "type": "main",
+    "format": "diffusers",
+    "key": "3a0e45ff858926fd4a63da630688b1e1",
+    "original_hash": "1c12f18fb6e403baef26fb9d720fbd2f",
+    "current_hash": "1c12f18fb6e403baef26fb9d720fbd2f",
+    "description": "sd-1 main model openjourney",
+    "source": "/opt/invokeai/models/sd-1/main/openjourney",
+    "last_modified": 1707794711,
+    "vae": "/opt/invokeai/models/sd-1/vae/vae-ft-mse-840000-ema-pruned_fp16.safetensors",
+    "variant": "normal",
+    "prediction_type": "epsilon",
+    "repo_variant": "fp16",
+}
+
+example_model_input = {
+    "path": "base/type/name",
+    "name": "model_name",
+    "base": "sd-1",
+    "type": "main",
+    "format": "diffusers",
+    "description": "Model description",
+    "vae": None,
+    "variant": "normal",
+}
+
 
 class ModelsList(BaseModel):
     """Return list of configs."""
@@ -88,7 +117,10 @@ async def list_model_records(
     "/i/{key}",
     operation_id="get_model_record",
     responses={
-        200: {"description": "Success"},
+        200: {
+            "description": "The model configuration was retrieved successfully",
+            "content": {"application/json": {"example": example_model_output}},
+        },
         400: {"description": "Bad request"},
         404: {"description": "The model could not be found"},
     },
@@ -165,18 +197,22 @@ async def search_by_metadata_tags(
     "/i/{key}",
     operation_id="update_model_record",
     responses={
-        200: {"description": "The model was updated successfully"},
+        200: {
+            "description": "The model was updated successfully",
+            "content": {"application/json": {"example": example_model_output}},
+        },
         400: {"description": "Bad request"},
         404: {"description": "The model could not be found"},
         409: {"description": "There is already a model corresponding to the new name"},
     },
     status_code=200,
-    response_model=AnyModelConfig,
 )
 async def update_model_record(
     key: Annotated[str, Path(description="Unique key of model")],
-    info: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
-) -> AnyModelConfig:
+    info: Annotated[
+        AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
+    ],
+) -> Annotated[AnyModelConfig, Field(example="this is neat")]:
     """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
     logger = ApiDependencies.invoker.services.logger
     record_store = ApiDependencies.invoker.services.model_manager.store
@@ -225,7 +261,10 @@ async def del_model_record(
     "/i/",
     operation_id="add_model_record",
     responses={
-        201: {"description": "The model added successfully"},
+        201: {
+            "description": "The model added successfully",
+            "content": {"application/json": {"example": example_model_output}},
+        },
         409: {"description": "There is already a model corresponding to this path or repo_id"},
         415: {"description": "Unrecognized file/folder format"},
     },
@@ -270,6 +309,7 @@ async def heuristic_import(
     config: Optional[Dict[str, Any]] = Body(
         description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
         default=None,
+        example={"name": "modelT", "description": "antique cars"},
     ),
     access_token: Optional[str] = None,
 ) -> ModelInstallJob:
@@ -497,7 +537,10 @@ async def sync_models_to_config() -> Response:
     "/convert/{key}",
     operation_id="convert_model",
     responses={
-        200: {"description": "Model converted successfully"},
+        200: {
+            "description": "Model converted successfully",
+            "content": {"application/json": {"example": example_model_output}},
+        },
         400: {"description": "Bad request"},
         404: {"description": "Model not found"},
         409: {"description": "There is already a model registered at this location"},
@@ -571,6 +614,15 @@ async def convert_model(
 @model_manager_v2_router.put(
     "/merge",
     operation_id="merge",
+    responses={
+        200: {
+            "description": "Model converted successfully",
+            "content": {"application/json": {"example": example_model_output}},
+        },
+        400: {"description": "Bad request"},
+        404: {"description": "Model not found"},
+        409: {"description": "There is already a model registered at this location"},
+    },
 )
 async def merge(
     keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
@@ -596,7 +648,6 @@ async def merge(
         interp: Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
         merge_dest_directory: Specify a directory to store the merged model in [models directory]
     """
-    print(f"here i am, keys={keys}")
     logger = ApiDependencies.invoker.services.logger
     try:
         logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")

invokeai/app/invocations/ip_adapter.py

@@ -92,10 +92,10 @@ class IPAdapterInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.services.model_records.get_model(self.ip_adapter_model.key)
+        ip_adapter_info = context.services.model_manager.store.get_model(self.ip_adapter_model.key)
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
-        image_encoder_models = context.services.model_records.search_by_attr(
+        image_encoder_models = context.services.model_manager.store.search_by_attr(
             model_name=image_encoder_model_name, base_model=BaseModelType.Any, model_type=ModelType.CLIPVision
         )
         assert len(image_encoder_models) == 1

invokeai/app/invocations/model.py

@@ -106,7 +106,7 @@ class MainModelLoaderInvocation(BaseInvocation):
         key = self.model.key
 
         # TODO: not found exceptions
-        if not context.services.model_records.exists(key):
+        if not context.services.model_manager.store.exists(key):
             raise Exception(f"Unknown model {key}")
 
         return ModelLoaderOutput(
@@ -175,7 +175,7 @@ class LoraLoaderInvocation(BaseInvocation):
 
         lora_key = self.lora.key
 
-        if not context.services.model_records.exists(lora_key):
+        if not context.services.model_manager.store.exists(lora_key):
             raise Exception(f"Unkown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
@@ -255,7 +255,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
 
         lora_key = self.lora.key
 
-        if not context.services.model_records.exists(lora_key):
+        if not context.services.model_manager.store.exists(lora_key):
             raise Exception(f"Unknown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
@@ -321,7 +321,7 @@ class VaeLoaderInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> VAEOutput:
         key = self.vae_model.key
 
-        if not context.services.model_records.exists(key):
+        if not context.services.model_manager.store.exists(key):
             raise Exception(f"Unkown vae: {key}!")
 
         return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))

invokeai/app/services/config/config_base.py

@@ -27,11 +27,11 @@ class InvokeAISettings(BaseSettings):
     """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
 
     initconf: ClassVar[Optional[DictConfig]] = None
-    argparse_groups: ClassVar[Dict] = {}
+    argparse_groups: ClassVar[Dict[str, Any]] = {}
 
     model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
 
-    def parse_args(self, argv: Optional[list] = sys.argv[1:]):
+    def parse_args(self, argv: Optional[List[str]] = sys.argv[1:]) -> None:
         """Call to parse command-line arguments."""
         parser = self.get_parser()
         opt, unknown_opts = parser.parse_known_args(argv)
@@ -68,7 +68,7 @@ class InvokeAISettings(BaseSettings):
         return OmegaConf.to_yaml(conf)
 
     @classmethod
-    def add_parser_arguments(cls, parser):
+    def add_parser_arguments(cls, parser) -> None:
         """Dynamically create arguments for a settings parser."""
         if "type" in get_type_hints(cls):
             settings_stanza = get_args(get_type_hints(cls)["type"])[0]
@@ -117,7 +117,8 @@ class InvokeAISettings(BaseSettings):
         """Return the category of a setting."""
         hints = get_type_hints(cls)
         if command_field in hints:
-            return get_args(hints[command_field])[0]
+            result: str = get_args(hints[command_field])[0]
+            return result
         else:
             return "Uncategorized"
 
@@ -158,7 +159,7 @@ class InvokeAISettings(BaseSettings):
         ]
 
     @classmethod
-    def add_field_argument(cls, command_parser, name: str, field, default_override=None):
+    def add_field_argument(cls, command_parser, name: str, field, default_override=None) -> None:
         """Add the argparse arguments for a setting parser."""
         field_type = get_type_hints(cls).get(name)
         default = (

invokeai/app/services/config/config_common.py

@@ -21,7 +21,7 @@ class PagingArgumentParser(argparse.ArgumentParser):
     It also supports reading defaults from an init file.
     """
 
-    def print_help(self, file=None):
+    def print_help(self, file=None) -> None:
         text = self.format_help()
         pydoc.pager(text)
 

invokeai/app/services/download/download_default.py

@@ -8,12 +8,12 @@ import time
 import traceback
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Set
 
 import requests
 from pydantic.networks import AnyHttpUrl
 from requests import HTTPError
-from tqdm import tqdm
+from tqdm import tqdm, std
 
 from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.util.misc import get_iso_timestamp
@@ -49,12 +49,12 @@ class DownloadQueueService(DownloadQueueServiceBase):
         :param max_parallel_dl: Number of simultaneous downloads allowed [5].
         :param requests_session: Optional requests.sessions.Session object, for unit tests.
         """
-        self._jobs = {}
+        self._jobs: Dict[int, DownloadJob] = {}
         self._next_job_id = 0
-        self._queue = PriorityQueue()
+        self._queue: PriorityQueue[DownloadJob] = PriorityQueue()
         self._stop_event = threading.Event()
         self._job_completed_event = threading.Event()
-        self._worker_pool = set()
+        self._worker_pool: Set[threading.Thread] = set()
         self._lock = threading.Lock()
         self._logger = InvokeAILogger.get_logger("DownloadQueueService")
         self._event_bus = event_bus
@@ -424,7 +424,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
 class TqdmProgress(object):
     """TQDM-based progress bar object to use in on_progress handlers."""
 
-    _bars: Dict[int, tqdm]  # the tqdm object
+    _bars: Dict[int, tqdm]  # type: ignore
     _last: Dict[int, int]  # last bytes downloaded
 
     def __init__(self) -> None:  # noqa D107

invokeai/app/util/misc.py

@@ -5,7 +5,7 @@ import uuid
 import numpy as np
 
 
-def get_timestamp():
+def get_timestamp() -> int:
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
 
 
@@ -20,16 +20,16 @@ def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:
 SEED_MAX = np.iinfo(np.uint32).max
 
 
-def get_random_seed():
+def get_random_seed() -> int:
     rng = np.random.default_rng(seed=None)
     return int(rng.integers(0, SEED_MAX))
 
 
-def uuid_string():
+def uuid_string() -> str:
     res = uuid.uuid4()
     return str(res)
 
 
-def is_optional(value: typing.Any):
+def is_optional(value: typing.Any) -> bool:
     """Checks if a value is typed as Optional. Note that Optional is sugar for Union[x, None]."""
     return typing.get_origin(value) is typing.Union and type(None) in typing.get_args(value)

invokeai/backend/model_manager/load/load_base.py

@@ -22,6 +22,7 @@ from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
+    ModelConfigBase,
     ModelFormat,
     ModelType,
     SubModelType,
@@ -70,7 +71,7 @@ class ModelLoaderBase(ABC):
         pass
 
     @abstractmethod
-    def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load_model(self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """
         Return a model given its confguration.
 
@@ -122,7 +123,7 @@ class AnyModelLoader:
         """Return the convert cache associated used by the loaders."""
         return self._convert_cache
 
-    def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load_model(self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """
         Return a model given its configuration.
 
@@ -144,8 +145,8 @@ class AnyModelLoader:
 
     @classmethod
     def get_implementation(
-        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
-    ) -> Tuple[Type[ModelLoaderBase], AnyModelConfig, Optional[SubModelType]]:
+        cls, config: ModelConfigBase, submodel_type: Optional[SubModelType]
+    ) -> Tuple[Type[ModelLoaderBase], ModelConfigBase, Optional[SubModelType]]:
         """Get subclass of ModelLoaderBase registered to handle base and type."""
         # We have to handle VAE overrides here because this will change the model type and the corresponding implementation returned
         conf2, submodel_type = cls._handle_subtype_overrides(config, submodel_type)
@@ -161,8 +162,8 @@ class AnyModelLoader:
 
     @classmethod
     def _handle_subtype_overrides(
-        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
-    ) -> Tuple[AnyModelConfig, Optional[SubModelType]]:
+        cls, config: ModelConfigBase, submodel_type: Optional[SubModelType]
+    ) -> Tuple[ModelConfigBase, Optional[SubModelType]]:
         if submodel_type == SubModelType.Vae and hasattr(config, "vae") and config.vae is not None:
             model_path = Path(config.vae)
             config_class = (

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -34,8 +34,8 @@ from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot,
 from invokeai.backend.util.devices import choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
 
-from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase
-from .model_locker import ModelLocker, ModelLockerBase
+from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase, ModelLockerBase
+from .model_locker import ModelLocker
 
 if choose_torch_device() == torch.device("mps"):
     from torch import mps

invokeai/backend/model_manager/metadata/fetch/fetch_base.py

@@ -20,7 +20,7 @@ from requests.sessions import Session
 
 from invokeai.backend.model_manager import ModelRepoVariant
 
-from ..metadata_base import AnyModelRepoMetadata, AnyModelRepoMetadataValidator
+from ..metadata_base import AnyModelRepoMetadata, AnyModelRepoMetadataValidator, BaseMetadata
 
 
 class ModelMetadataFetchBase(ABC):
@@ -62,5 +62,5 @@ class ModelMetadataFetchBase(ABC):
     @classmethod
     def from_json(cls, json: str) -> AnyModelRepoMetadata:
         """Given the JSON representation of the metadata, return the corresponding Pydantic object."""
-        metadata = AnyModelRepoMetadataValidator.validate_json(json)
+        metadata: BaseMetadata = AnyModelRepoMetadataValidator.validate_json(json)  # type: ignore
         return metadata

invokeai/backend/model_manager/probe.py

@@ -166,7 +166,7 @@ class ModelProbe(object):
         fields["original_hash"] = fields.get("original_hash") or hash
         fields["current_hash"] = fields.get("current_hash") or hash
 
-        if format_type == ModelFormat.Diffusers:
+        if format_type == ModelFormat.Diffusers and hasattr(probe, "get_repo_variant"):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models

invokeai/backend/model_manager/search.py

@@ -116,9 +116,9 @@ class ModelSearch(ModelSearchBase):
        # returns all models that have 'anime' in the path
     """
 
-    models_found: Set[Path] = Field(default=None)
-    scanned_dirs: Set[Path] = Field(default=None)
-    pruned_paths: Set[Path] = Field(default=None)
+    models_found: Optional[Set[Path]] = Field(default=None)
+    scanned_dirs: Optional[Set[Path]] = Field(default=None)
+    pruned_paths: Optional[Set[Path]] = Field(default=None)
 
     def search_started(self) -> None:
         self.models_found = set()