restore 3.9 compatibility by replacing | with Union[]

invokeai/app/cli/commands.py

@@ -47,7 +47,7 @@ def add_parsers(
     commands: list[type],
     command_field: str = "type",
     exclude_fields: list[str] = ["id", "type"],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
+    add_arguments: Union[Callable[[argparse.ArgumentParser], None],None] = None
     ):
     """Adds parsers for each command to the subparsers"""
 
@@ -72,7 +72,7 @@ def add_parsers(
 def add_graph_parsers(
     subparsers,
     graphs: list[LibraryGraph],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
+    add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
 ):
     for graph in graphs:
         command_parser = subparsers.add_parser(graph.name, help=graph.description)

invokeai/app/cli_app.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import argparse
-import os
 import re
 import shlex
 import sys
@@ -348,7 +347,7 @@ def invoke_cli():
 
                 # Parse invocation
                 command: CliCommand = None # type:ignore
-                system_graph: LibraryGraph|None = None
+                system_graph: Union[LibraryGraph,None] = None
                 if args['type'] in system_graph_names:
                     system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
                     invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))

invokeai/app/services/board_images.py

@@ -132,7 +132,7 @@ class BoardImagesService(BoardImagesServiceABC):
 
 
 def board_record_to_dto(
-    board_record: BoardRecord, cover_image_name: str | None, image_count: int
+    board_record: BoardRecord, cover_image_name: Union[str, None], image_count: int
 ) -> BoardDTO:
     """Converts a board record to a board DTO."""
     return BoardDTO(

invokeai/app/services/events.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any
+from typing import Any, Union
 from invokeai.app.models.image import ProgressImage
 from invokeai.app.util.misc import get_timestamp
 from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo
@@ -28,7 +28,7 @@ class EventServiceBase:
         graph_execution_state_id: str,
         node: dict,
         source_node_id: str,
-        progress_image: ProgressImage | None,
+        progress_image: Union[ProgressImage, None],
         step: int,
         total_steps: int,
     ) -> None:

invokeai/app/services/graph.py

@@ -3,7 +3,6 @@
 import copy
 import itertools
 import uuid
-from types import NoneType
 from typing import (
     Annotated,
     Any,
@@ -26,6 +25,8 @@ from ..invocations.baseinvocation import (
     InvocationContext,
 )
 
+# in 3.10 this would be "from types import NoneType"
+NoneType = type(None)
 
 class EdgeConnection(BaseModel):
     node_id: str = Field(description="The id of the node for this edge connection")
@@ -846,7 +847,7 @@ class GraphExecutionState(BaseModel):
             ]
         }
 
-    def next(self) -> BaseInvocation | None:
+    def next(self) -> Union[BaseInvocation, None]:
         """Gets the next node ready to execute."""
 
         # TODO: enable multiple nodes to execute simultaneously by tracking currently executing nodes

invokeai/app/services/image_file_storage.py

@@ -2,7 +2,7 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
-from typing import Dict, Optional
+from typing import Dict, Optional, Union
 
 from PIL.Image import Image as PILImageType
 from PIL import Image, PngImagePlugin
@@ -80,7 +80,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __cache: Dict[Path, PILImageType]
     __max_cache_size: int
 
-    def __init__(self, output_folder: str | Path):
+    def __init__(self, output_folder: Union[str, Path]):
         self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
@@ -164,7 +164,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
         return path
 
-    def validate_path(self, path: str | Path) -> bool:
+    def validate_path(self, path: Union[str, Path]) -> bool:
         """Validates the path given for an image or thumbnail."""
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
@@ -175,7 +175,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         for folder in folders:
             folder.mkdir(parents=True, exist_ok=True)
 
-    def __get_cache(self, image_name: Path) -> PILImageType | None:
+    def __get_cache(self, image_name: Path) -> Union[PILImageType, None]:
         return None if image_name not in self.__cache else self.__cache[image_name]
 
     def __set_cache(self, image_name: Path, image: PILImageType):

invokeai/app/services/image_record_storage.py

@@ -116,7 +116,7 @@ class ImageRecordStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_most_recent_image_for_board(self, board_id: str) -> ImageRecord | None:
+    def get_most_recent_image_for_board(self, board_id: str) -> Union[ImageRecord, None]:
         """Gets the most recent image for a board."""
         pass
 

invokeai/app/services/invocation_queue.py

@@ -5,6 +5,7 @@ from abc import ABC, abstractmethod
 from queue import Queue
 
 from pydantic import BaseModel, Field
+from typing import Union
 
 
 class InvocationQueueItem(BaseModel):
@@ -22,7 +23,7 @@ class InvocationQueueABC(ABC):
         pass
 
     @abstractmethod
-    def put(self, item: InvocationQueueItem | None) -> None:
+    def put(self, item: Union[InvocationQueueItem, None]) -> None:
         pass
 
     @abstractmethod
@@ -57,7 +58,7 @@ class MemoryInvocationQueue(InvocationQueueABC):
 
         return item
 
-    def put(self, item: InvocationQueueItem | None) -> None:
+    def put(self, item: Union[InvocationQueueItem, None]) -> None:
         self.__queue.put(item)
 
     def cancel(self, graph_execution_state_id: str) -> None:

invokeai/app/services/invoker.py

@@ -2,6 +2,7 @@
 
 from abc import ABC
 from threading import Event, Thread
+from typing import Union
 
 from ..invocations.baseinvocation import InvocationContext
 from .graph import Graph, GraphExecutionState
@@ -21,7 +22,7 @@ class Invoker:
 
     def invoke(
         self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
-    ) -> str | None:
+    ) -> Union[str, None]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
         Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
 
@@ -45,7 +46,7 @@ class Invoker:
 
         return invocation.id
 
-    def create_execution_state(self, graph: Graph | None = None) -> GraphExecutionState:
+    def create_execution_state(self, graph: Union[Graph, None] = None) -> GraphExecutionState:
         """Creates a new execution state for the given graph"""
         new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
         self.services.graph_execution_manager.set(new_state)

invokeai/app/services/latent_storage.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
-from typing import Dict
+from typing import Dict, Union
 
 import torch
 
@@ -55,7 +55,7 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         if name in self.__cache:
             del self.__cache[name]
 
-    def __get_cache(self, name: str) -> torch.Tensor|None:
+    def __get_cache(self, name: str) -> Union[torch.Tensor, None]:
         return None if name not in self.__cache else self.__cache[name]
 
     def __set_cache(self, name: str, data: torch.Tensor):
@@ -69,9 +69,9 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
 class DiskLatentsStorage(LatentsStorageBase):
     """Stores latents in a folder on disk without caching"""
 
-    __output_folder: str | Path
+    __output_folder: Union[str, Path]
 
-    def __init__(self, output_folder: str | Path):
+    def __init__(self, output_folder: Union[str, Path]):
         self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__output_folder.mkdir(parents=True, exist_ok=True)
 
@@ -91,4 +91,4 @@ class DiskLatentsStorage(LatentsStorageBase):
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
-    
+    

invokeai/backend/generator/base.py

@@ -21,7 +21,7 @@ from PIL import Image, ImageChops, ImageFilter
 from accelerate.utils import set_seed
 from diffusers import DiffusionPipeline
 from tqdm import trange
-from typing import Callable, List, Iterator, Optional, Type
+from typing import Callable, List, Iterator, Optional, Type, Union
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
@@ -178,7 +178,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
     def generate(self,
-               init_image: Image.Image | torch.FloatTensor,
+               init_image: Union[Image.Image, torch.FloatTensor],
                strength: float=0.75,
                **keyword_args
                )->Iterator[InvokeAIGeneratorOutput]:
@@ -195,7 +195,7 @@ class Img2Img(InvokeAIGenerator):
 # Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
 class Inpaint(Img2Img):
     def generate(self,
-                 mask_image: Image.Image | torch.FloatTensor,
+                 mask_image: Union[Image.Image, torch.FloatTensor],
                  # Seam settings - when 0, doesn't fill seam
                  seam_size: int = 96,
                  seam_blur: int = 16,

invokeai/backend/generator/inpaint.py

@@ -203,8 +203,8 @@ class Inpaint(Img2Img):
         cfg_scale,
         ddim_eta,
         conditioning,
-        init_image: Image.Image | torch.FloatTensor,
+        init_image: Union[Image.Image, torch.FloatTensor],
-        mask_image: Image.Image | torch.FloatTensor,
+        mask_image: Union[Image.Image, torch.FloatTensor],
         strength: float,
         mask_blur_radius: int = 8,
         # Seam settings - when 0, doesn't fill seam

invokeai/backend/model_management/models/__init__.py

@@ -68,7 +68,11 @@ def get_model_config_enums():
     enums = list()
 
     for model_config in MODEL_CONFIGS:
-        fields = inspect.get_annotations(model_config)
+
+        if hasattr(inspect,'get_annotations'):
+            fields = inspect.get_annotations(model_config)
+        else:
+            fields = model_config.__annotations__
         try:
             field = fields["model_format"]
         except:

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -7,7 +7,7 @@ import secrets
 from collections.abc import Sequence
 from dataclasses import dataclass, field
 from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 import einops
 import PIL.Image
@@ -17,12 +17,11 @@ import psutil
 import torch
 import torchvision.transforms as T
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
+from diffusers.models.controlnet import ControlNetModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
     StableDiffusionPipeline,
 )
-from diffusers.pipelines.controlnet import MultiControlNetModel
 
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
     StableDiffusionImg2ImgPipeline,
@@ -46,7 +45,7 @@ from .diffusion import (
     InvokeAIDiffuserComponent,
     PostprocessingSettings,
 )
-from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
+from .offloading import FullyLoadedModelGroup, ModelGroup
 
 @dataclass
 class PipelineIntermediateState:
@@ -105,7 +104,7 @@ class AddsMaskGuidance:
     _debug: Optional[Callable] = None
 
     def __call__(
-        self, step_output: BaseOutput | SchedulerOutput, t: torch.Tensor, conditioning
+        self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning
     ) -> BaseOutput:
         output_class = step_output.__class__  # We'll create a new one with masked data.
 

invokeai/backend/stable_diffusion/offloading.py

@@ -4,7 +4,7 @@ import warnings
 import weakref
 from abc import ABCMeta, abstractmethod
 from collections.abc import MutableMapping
-from typing import Callable
+from typing import Callable, Union
 
 import torch
 from accelerate.utils import send_to_device
@@ -117,7 +117,7 @@ class LazilyLoadedModelGroup(ModelGroup):
     """
 
     _hooks: MutableMapping[torch.nn.Module, RemovableHandle]
-    _current_model_ref: Callable[[], torch.nn.Module | _NoModel]
+    _current_model_ref: Callable[[], Union[torch.nn.Module, _NoModel]]
 
     def __init__(self, execution_device: torch.device):
         super().__init__(execution_device)

invokeai/backend/util/devices.py

@@ -4,6 +4,7 @@ from contextlib import nullcontext
 
 import torch
 from torch import autocast
+from typing import Union
 from invokeai.app.services.config import InvokeAIAppConfig
 
 CPU_DEVICE = torch.device("cpu")
@@ -49,7 +50,7 @@ def choose_autocast(precision):
     return nullcontext
 
 
-def normalize_device(device: str | torch.device) -> torch.device:
+def normalize_device(device: Union[str, torch.device]) -> torch.device:
     """Ensure device has a device index defined, if appropriate."""
     device = torch.device(device)
     if device.index is None: