fix potential race condition in config system

invokeai/app/api_app.py

@@ -39,7 +39,8 @@ socket_io = SocketIO(app)
 
 # initialize config
 # this is a module global
-app_config = InvokeAIAppConfig()
+app_config = InvokeAIAppConfig.get_config()
+app_config.parse_args()
 
 # Add startup event to load dependencies
 @app.on_event("startup")

invokeai/app/cli_app.py

@@ -37,7 +37,7 @@ from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
 from .services.processor import DefaultInvocationProcessor
 from .services.sqlite import SqliteItemStorage
-from .services.config import get_invokeai_config
+from .services.config import InvokeAIAppConfig
 
 class CliCommand(BaseModel):
     command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
@@ -196,7 +196,8 @@ logger = logger.InvokeAILogger.getLogger()
 
 def invoke_cli():
     # this gets the basic configuration
-    config = get_invokeai_config()
+    config = InvokeAIAppConfig.get_config()
+    config.parse_args()
 
     # get the optional list of invocations to execute on the command line
     parser = config.get_parser()

invokeai/app/services/config.py

@@ -82,13 +82,10 @@ Typical usage:
  from invokeai.invocations.generate import TextToImageInvocation
 
  # get global configuration and print its nsfw_checker value
- conf = InvokeAIAppConfig()
+ conf = InvokeAIAppConfig.get_config()
+ conf.parse_args()
  print(conf.nsfw_checker)
 
- # get the text2image invocation and print its step value
- text2image = TextToImageInvocation()
- print(text2image.steps)
-
 Computed properties:
 
 The InvokeAIAppConfig object has a series of properties that
@@ -103,10 +100,11 @@ a Path object:
  lora_path          - path to the LoRA directory
 
 In most cases, you will want to create a single InvokeAIAppConfig
-object for the entire application. The get_invokeai_config() function
+object for the entire application. The InvokeAIAppConfig.get_config() function
 does this:
 
-  config = get_invokeai_config()
+  config = InvokeAIAppConfig.get_config()
+  config.parse_args()   # read values from the command line/config file
   print(config.root)
 
 # Subclassing
@@ -141,6 +139,7 @@ two configs are kept in separate sections of the config file:
         outdir: outputs
      ...
 '''
+from __future__ import annotations
 import argparse
 import pydoc
 import typing
@@ -155,9 +154,6 @@ from typing import Any, ClassVar, Dict, List, Literal, Type, Union, get_origin,
 INIT_FILE = Path('invokeai.yaml')
 LEGACY_INIT_FILE = Path('invokeai.init')
 
-# This global stores a singleton InvokeAIAppConfig configuration object
-global_config = None
-
 class InvokeAISettings(BaseSettings):
     '''
     Runtime configuration settings in which default values are
@@ -330,6 +326,9 @@ the command-line client (recommended for experts only), or
 can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
 setting environment variables INVOKEAI_<setting>.
      '''
+    singleton_config: ClassVar[InvokeAIAppConfig] = None
+    singleton_init: ClassVar[Dict] = None
+    
     #fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
     host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
@@ -369,33 +368,44 @@ setting environment variables INVOKEAI_<setting>.
     embeddings          : bool = Field(default=True, description='Load contents of embeddings directory', category='Models')
     #fmt: on
 
-    def __init__(self, conf: DictConfig = None, argv: List[str]=None, **kwargs):
+    def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
         '''
-        Initialize InvokeAIAppconfig.
+        Update settings with contents of init file, environment, and 
+        command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
-        :param **kwargs: attributes to initialize with
+        :param clobber: ovewrite any initialization parameters passed during initialization
         '''
-        super().__init__(**kwargs)
-
         # Set the runtime root directory. We parse command-line switches here
         # in order to pick up the --root_dir option.
-        self.parse_args(argv)
+        super().parse_args(argv)
         if conf is None:
             try:
                 conf = OmegaConf.load(self.root_dir / INIT_FILE)
             except:
                 pass
         InvokeAISettings.initconf = conf
-
+        
         # parse args again in order to pick up settings in configuration file
-        self.parse_args(argv)
+        super().parse_args(argv)
 
-        # restore initialization values
+        if self.singleton_init and not clobber:
-        hints = get_type_hints(self)
+            hints = get_type_hints(self.__class__)
-        for k in kwargs:
+            for k in self.singleton_init:
-            setattr(self,k,parse_obj_as(hints[k],kwargs[k]))
+                setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
 
+    @classmethod
+    def get_config(cls,**kwargs)->InvokeAIAppConfig:
+        '''
+        This returns a singleton InvokeAIAppConfig configuration object.
+        '''
+        if cls.singleton_config is None \
+           or type(cls.singleton_config)!=cls \
+           or (kwargs and cls.singleton_init != kwargs):
+            cls.singleton_config = cls(**kwargs)
+            cls.singleton_init = kwargs
+        return cls.singleton_config
+        
     @property
     def root_path(self)->Path:
         '''
@@ -513,11 +523,8 @@ class PagingArgumentParser(argparse.ArgumentParser):
         text = self.format_help()
         pydoc.pager(text)
 
-def get_invokeai_config(cls:Type[InvokeAISettings]=InvokeAIAppConfig,**kwargs)->InvokeAIAppConfig:
+def get_invokeai_config(**kwargs)->InvokeAIAppConfig:
     '''
-    This returns a singleton InvokeAIAppConfig configuration object.
+    Legacy function which returns InvokeAIAppConfig.get_config()
     '''
-    global global_config
+    return InvokeAIAppConfig.get_config(**kwargs)
-    if global_config is None or type(global_config)!=cls:
-        global_config = cls(**kwargs)
-    return global_config

invokeai/app/services/sqlite.py

@@ -26,7 +26,6 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         self._table_name = table_name
         self._id_field = id_field  # TODO: validate that T has this field
         self._lock = Lock()
-
         self._conn = sqlite3.connect(
             self._filename, check_same_thread=False
         )  # TODO: figure out a better threading solution

invokeai/backend/config/invokeai_configure.py

@@ -51,10 +51,7 @@ from invokeai.backend.config.model_install_backend import (
     hf_download_with_resume,
     recommended_datasets,
 )
-from invokeai.app.services.config import (
+from invokeai.app.services.config import InvokeAIAppConfig
-    get_invokeai_config,
-    InvokeAIAppConfig,
-)
 
 warnings.filterwarnings("ignore")
 
@@ -62,7 +59,7 @@ transformers.logging.set_verbosity_error()
 
 
 # --------------------------globals-----------------------
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
@@ -820,8 +817,8 @@ def main():
         if old_init_file.exists() and not new_init_file.exists():
             print('** Migrating invokeai.init to invokeai.yaml')
             migrate_init_file(old_init_file)
-            config = get_invokeai_config()  # reread defaults
+            # Load new init file into config
-
+            config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
 
         if not config.model_conf_path.exists():
             initialize_rootdir(config.root, opt.yes_to_all)

invokeai/backend/config/model_install_backend.py

@@ -19,7 +19,7 @@ from tqdm import tqdm
 
 import invokeai.configs as configs
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..model_management import ModelManager
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
@@ -27,7 +27,7 @@ from ..stable_diffusion import StableDiffusionGeneratorPipeline
 warnings.filterwarnings("ignore")
 
 # --------------------------globals-----------------------
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 

invokeai/backend/image_util/patchmatch.py

@@ -6,7 +6,8 @@ be suppressed or deferred
 """
 import numpy as np
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
+config = InvokeAIAppConfig.get_config()
 
 class PatchMatch:
     """
@@ -21,7 +22,6 @@ class PatchMatch:
 
     @classmethod
     def _load_patch_match(self):
-        config = get_invokeai_config()
         if self.tried_load:
             return
         if config.try_patchmatch:

invokeai/backend/image_util/txt2mask.py

@@ -33,10 +33,11 @@ from PIL import Image, ImageOps
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
+config = InvokeAIAppConfig.get_config()
 
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
@@ -83,7 +84,6 @@ class Txt2Mask(object):
 
     def __init__(self, device="cpu", refined=False):
         logger.info("Initializing clipseg model for text to mask inference")
-        config = get_invokeai_config()
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -26,7 +26,7 @@ import torch
 from safetensors.torch import load_file
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from .model_manager import ModelManager, SDLegacyType
 
@@ -842,7 +842,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
 
 def convert_ldm_clip_checkpoint(checkpoint):
     text_model = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
+        "openai/clip-vit-large-patch14", cache_dir=InvokeAIAppConfig.get_config().cache_dir
     )
 
     keys = list(checkpoint.keys())
@@ -897,7 +897,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 
 
 def convert_paint_by_example_checkpoint(checkpoint):
-    cache_dir = get_invokeai_config().cache_dir
+    cache_dir = InvokeAIAppConfig.get_config().cache_dir
     config = CLIPVisionConfig.from_pretrained(
         "openai/clip-vit-large-patch14", cache_dir=cache_dir
     )
@@ -969,7 +969,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
 
 
 def convert_open_clip_checkpoint(checkpoint):
-    cache_dir = get_invokeai_config().cache_dir
+    cache_dir = InvokeAIAppConfig.get_config().cache_dir
     text_model = CLIPTextModel.from_pretrained(
         "stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
     )
@@ -1092,7 +1092,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param vae: A diffusers VAE to load into the pipeline.
     :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
-    config = get_invokeai_config()
+    config = InvokeAIAppConfig.get_config()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()

invokeai/backend/model_management/model_manager.py

@@ -47,7 +47,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
 from ..stable_diffusion import (
     StableDiffusionGeneratorPipeline,
 )
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
@@ -98,7 +98,7 @@ class ModelManager(object):
         if not isinstance(config, DictConfig):
             config = OmegaConf.load(config)
         self.config = config
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         self.precision = precision
         self.device = torch.device(device_type)
         self.max_loaded_models = max_loaded_models
@@ -1057,7 +1057,7 @@ class ModelManager(object):
         """
         # Three transformer models to check: bert, clip and safety checker, and
         # the diffusers as well
-        config = get_invokeai_config()
+        config = InvokeAIAppConfig.get_config()
         models_dir = config.root_dir / "models"
         legacy_locations = [
             Path(
@@ -1287,7 +1287,7 @@ class ModelManager(object):
 
     @classmethod
     def _delete_model_from_cache(cls,repo_id):
-        cache_info = scan_cache_dir(get_invokeai_config().cache_dir)
+        cache_info = scan_cache_dir(InvokeAIAppConfig.get_config().cache_dir)
 
         # I'm sure there is a way to do this with comprehensions
         # but the code quickly became incomprehensible!
@@ -1304,7 +1304,7 @@ class ModelManager(object):
 
     @staticmethod
     def _abs_path(path: str | Path) -> Path:
-        globals = get_invokeai_config()
+        globals = InvokeAIAppConfig.get_config()
         if path is None or Path(path).is_absolute():
             return path
         return Path(globals.root_dir, path).resolve()

invokeai/backend/prompting/conditioning.py

@@ -21,10 +21,12 @@ from compel.prompt_parser import (
 
 import invokeai.backend.util.logging as logger
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
+config = InvokeAIAppConfig.get_config()
+
 def get_uc_and_c_and_ec(prompt_string,
                         model: InvokeAIDiffuserComponent,
                         log_tokens=False, skip_normalize_legacy_blend=False):
@@ -39,8 +41,6 @@ def get_uc_and_c_and_ec(prompt_string,
                     truncate_long_prompts=False,
                     )
     
-    config = get_invokeai_config()
-
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)

invokeai/backend/restoration/codeformer.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 pretrained_model_url = (
     "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@@ -18,7 +18,7 @@ class CodeFormerRestoration:
         self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
     ) -> None:
 
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         codeformer_dir = self.globals.root_dir / codeformer_dir
         self.model_path = codeformer_dir / codeformer_model_path
         self.codeformer_model_exists = self.model_path.exists()

invokeai/backend/restoration/gfpgan.py

@@ -7,11 +7,11 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         if not os.path.isabs(gfpgan_model_path):
             gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
         self.model_path = gfpgan_model_path

invokeai/backend/restoration/realesrgan.py

@@ -6,8 +6,8 @@ from PIL import Image
 from PIL.Image import Image as ImageType
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:

invokeai/backend/safety_checker.py

@@ -15,9 +15,11 @@ from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from .util import CPU_DEVICE
 
+config = InvokeAIAppConfig.get_config()
+
 class SafetyChecker(object):
     CAUTION_IMG = "caution.png"
     
@@ -26,7 +28,6 @@ class SafetyChecker(object):
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
         self.device = device
-        config = get_invokeai_config()
 
         try:
             safety_model_id = "CompVis/stable-diffusion-safety-checker"

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -17,15 +17,16 @@ from huggingface_hub import (
     hf_hub_url,
 )
 
-import invokeai.backend.util.logging as logger
+from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
+logger = InvokeAILogger.getLogger()
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         """
         Initialize the Concepts object. May optionally pass a root directory.
         """
-        self.config = get_invokeai_config()
+        self.config = InvokeAIAppConfig.get_config()
         self.root = root or self.config.root
         self.hf_api = HfApi()
         self.local_concepts = dict()

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -33,7 +33,7 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..util import CPU_DEVICE, normalize_device
 from .diffusion import (
     AttentionMapSaver,
@@ -346,7 +346,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        config = get_invokeai_config()
+        config = InvokeAIAppConfig.get_config()
         if (
             torch.cuda.is_available()
             and is_xformers_available()

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -10,7 +10,7 @@ from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from .cross_attention_control import (
     Arguments,
@@ -72,7 +72,7 @@ class InvokeAIDiffuserComponent:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
-        config = get_invokeai_config()
+        config = InvokeAIAppConfig.get_config()
         self.conditioning = None
         self.model = model
         self.is_running_diffusers = is_running_diffusers
@@ -112,23 +112,25 @@ class InvokeAIDiffuserComponent:
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
-    def override_cross_attention(
+    # apparently unused code
-        self, conditioning: ExtraConditioningInfo, step_count: int
+    # TODO: delete
-    ) -> Dict[str, AttentionProcessor]:
+    # def override_cross_attention(
-        """
+    #     self, conditioning: ExtraConditioningInfo, step_count: int
-        setup cross attention .swap control. for diffusers this replaces the attention processor, so
+    # ) -> Dict[str, AttentionProcessor]:
-        the previous attention processor is returned so that the caller can restore it later.
+    #     """
-        """
+    #     setup cross attention .swap control. for diffusers this replaces the attention processor, so
-        self.conditioning = conditioning
+    #     the previous attention processor is returned so that the caller can restore it later.
-        self.cross_attention_control_context = Context(
+    #     """
-            arguments=self.conditioning.cross_attention_control_args,
+    #     self.conditioning = conditioning
-            step_count=step_count,
+    #     self.cross_attention_control_context = Context(
-        )
+    #         arguments=self.conditioning.cross_attention_control_args,
-        return override_cross_attention(
+    #         step_count=step_count,
-            self.model,
+    #     )
-            self.cross_attention_control_context,
+    #     return override_cross_attention(
-            is_running_diffusers=self.is_running_diffusers,
+    #         self.model,
-        )
+    #         self.cross_attention_control_context,
+    #         is_running_diffusers=self.is_running_diffusers,
+    #     )
 
     def restore_default_cross_attention(
         self, restore_attention_processor: Optional["AttentionProcessor"] = None

invokeai/backend/util/devices.py

@@ -4,15 +4,15 @@ from contextlib import nullcontext
 
 import torch
 from torch import autocast
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
+config = InvokeAIAppConfig.get_config()
 
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
-    config = get_invokeai_config()    
     if config.always_use_cpu:
         return CPU_DEVICE
     if torch.cuda.is_available():
@@ -32,7 +32,6 @@ def choose_precision(device: torch.device) -> str:
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
-    config = get_invokeai_config()
     if config.full_precision:
         return torch.float32
     if choose_precision(device) == "float16":

invokeai/frontend/install/model_install.py

@@ -40,13 +40,13 @@ from .widgets import (
     TextBox,
     set_min_terminal_size,
 )
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 # minimum size for the UI
 MIN_COLS = 120
 MIN_LINES = 45
 
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 class addModelsForm(npyscreen.FormMultiPage):
     # for responsive resizing - disabled

invokeai/frontend/merge/merge_diffusers.py

@@ -20,12 +20,12 @@ from npyscreen import widget
 from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
-from invokeai.services.config import get_invokeai_config
+from invokeai.services.config import InvokeAIAppConfig
 from ...backend.model_management import ModelManager
 from ...frontend.install.widgets import FloatTitleSlider
 
 DEST_MERGED_MODEL_DIR = "merged_models"
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 def merge_diffusion_models(
     model_ids_or_paths: List[Union[str, Path]],

invokeai/frontend/training/textual_inversion.py

@@ -22,7 +22,7 @@ from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ...backend.training import (
     do_textual_inversion_training,
     parse_args
@@ -423,7 +423,7 @@ def do_front_end(args: Namespace):
         save_args(args)
 
         try:
-            do_textual_inversion_training(get_invokeai_config(),**args)
+            do_textual_inversion_training(InvokeAIAppConfig.get_config(),**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
             logger.error("An exception occurred during training. The exception was:")
@@ -436,7 +436,7 @@ def main():
     global config
     
     args = parse_args()
-    config = get_invokeai_config(argv=[])
+    config = InvokeAIAppConfig.get_config()
 
     # change root if needed
     if args.root_dir:

tests/test_config.py

@@ -5,7 +5,7 @@ from omegaconf import OmegaConf
 from pathlib import Path
 
 os.environ['INVOKEAI_ROOT']='/tmp'
-from invokeai.app.services.config import InvokeAIAppConfig, InvokeAISettings
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.invocations.generate import TextToImageInvocation
 
 init1 = OmegaConf.create(
@@ -32,48 +32,56 @@ def test_use_init():
     # note that we explicitly set omegaconf dict and argv here
     # so that the values aren't read from ~invokeai/invokeai.yaml and
     # sys.argv respectively.
-    conf1 = InvokeAIAppConfig(init1,[])
+    conf1 = InvokeAIAppConfig.get_config()
     assert conf1
+    conf1.parse_args(conf=init1)
     assert conf1.max_loaded_models==5
     assert not conf1.nsfw_checker
 
-    conf2 = InvokeAIAppConfig(init2,[])
+    conf2 = InvokeAIAppConfig.get_config()
     assert conf2
+    conf2.parse_args(conf=init2)
     assert conf2.nsfw_checker
     assert conf2.max_loaded_models==2
     assert not hasattr(conf2,'invalid_attribute')
     
 def test_argv_override():
-    conf = InvokeAIAppConfig(init1,['--nsfw_checker','--max_loaded=10'])
+    conf = InvokeAIAppConfig.get_config()
+    conf.parse_args(conf=init1,argv=['--nsfw_checker','--max_loaded=10'])
     assert conf.nsfw_checker
     assert conf.max_loaded_models==10
     assert conf.outdir==Path('outputs')  # this is the default
     
 def test_env_override():
     # argv overrides 
-    conf = InvokeAIAppConfig(conf=init1,argv=['--max_loaded=10'])
+    conf = InvokeAIAppConfig()
+    conf.parse_args(conf=init1,argv=['--max_loaded=10'])
     assert conf.nsfw_checker==False
-    
     os.environ['INVOKEAI_nsfw_checker'] = 'True'
-    conf = InvokeAIAppConfig(conf=init1,argv=['--max_loaded=10'])
+    conf.parse_args(conf=init1,argv=['--max_loaded=10'])
     assert conf.nsfw_checker==True
 
     # environment variables should be case insensitive
     os.environ['InvokeAI_Max_Loaded_Models'] = '15'
-    conf = InvokeAIAppConfig(conf=init1)
+    conf = InvokeAIAppConfig()
+    conf.parse_args(conf=init1)
     assert conf.max_loaded_models == 15
 
-    conf = InvokeAIAppConfig(conf=init1,argv=['--no-nsfw_checker','--max_loaded=10'])
+    conf = InvokeAIAppConfig()
+    conf.parse_args(conf=init1,argv=['--no-nsfw_checker','--max_loaded=10'])
     assert conf.nsfw_checker==False
     assert conf.max_loaded_models==10
 
-    conf = InvokeAIAppConfig(conf=init1,argv=[],max_loaded_models=20)
+    conf = InvokeAIAppConfig.get_config(max_loaded_models=20)
+    conf.parse_args(conf=init1,argv=[])
     assert conf.max_loaded_models==20
 
 def test_type_coercion():
-    conf = InvokeAIAppConfig(argv=['--root=/tmp/foobar'])
+    conf = InvokeAIAppConfig().get_config()
+    conf.parse_args(argv=['--root=/tmp/foobar'])
     assert conf.root==Path('/tmp/foobar')
     assert isinstance(conf.root,Path)
-    conf = InvokeAIAppConfig(argv=['--root=/tmp/foobar'],root='/tmp/different')
+    conf = InvokeAIAppConfig.get_config(root='/tmp/different')
+    conf.parse_args(argv=['--root=/tmp/foobar'])
     assert conf.root==Path('/tmp/different')
     assert isinstance(conf.root,Path)