fix(config): use new get_config across the app, use correct settings

invokeai/app/api/dependencies.py

@@ -65,7 +65,7 @@ class ApiDependencies:
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
 
-        output_folder = config.output_path
+        output_folder = config.outputs_path
         if output_folder is None:
             raise ValueError("Output folder is not set")
 

invokeai/app/invocations/__init__.py

@@ -3,9 +3,9 @@ import sys
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
-custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
+custom_nodes_path = Path(get_config().custom_nodes_path.resolve())
 custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
 custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

invokeai/app/invocations/baseinvocation.py

@@ -33,7 +33,7 @@ from invokeai.app.invocations.fields import (
     FieldKind,
     Input,
 )
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
@@ -191,7 +191,7 @@ class BaseInvocation(ABC, BaseModel):
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
         """Gets all invocations, respecting the allowlist and denylist."""
-        app_config = InvokeAIAppConfig.get_config()
+        app_config = get_config()
         allowed_invocations: set[BaseInvocation] = set()
         for sc in cls._invocation_classes:
             invocation_type = sc.get_type()

invokeai/app/invocations/latent.py

@@ -866,7 +866,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.config.get().tiled_decode:
+            if self.tiled or context.config.get().force_tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()

invokeai/app/services/model_install/model_install_default.py

@@ -279,9 +279,9 @@ class ModelInstallService(ModelInstallServiceBase):
     def sync_to_config(self) -> None:
         """Synchronize models on disk to those in the config record store database."""
         self._scan_models_directory()
-        if autoimport := self._app_config.autoimport_dir:
+        if self._app_config.autoimport_path:
             self._logger.info("Scanning autoimport directory for new models")
-            installed = self.scan_directory(self._app_config.root_path / autoimport)
+            installed = self.scan_directory(self._app_config.autoimport_path)
             self._logger.info(f"{len(installed)} new models registered")
         self._logger.info("Model installer (re)initialized")
 
@@ -365,7 +365,7 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> Path:
         """Download the model file located at source to the models cache and return its Path."""
         model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
-        model_path = self._app_config.models_convert_cache_path / model_hash
+        model_path = self._app_config.convert_cache_path / model_hash
 
         # We expect the cache directory to contain one and only one downloaded file.
         # We don't know the file's name in advance, as it is set by the download
@@ -591,7 +591,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         # add 'main' specific fields
         if isinstance(info, CheckpointConfigBase):
-            legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config_path).resolve()
+            legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
             info.config_path = legacy_conf.as_posix()
         self.record_store.add_model(info)
         return info.key

invokeai/app/services/model_manager/model_manager_default.py

@@ -78,13 +78,13 @@ class ModelManagerService(ModelManagerServiceBase):
         logger.setLevel(app_config.log_level.upper())
 
         ram_cache = ModelCache(
-            max_cache_size=app_config.ram_cache_size,
+            max_cache_size=app_config.ram,
-            max_vram_cache_size=app_config.vram_cache_size,
+            max_vram_cache_size=app_config.vram,
             logger=logger,
             execution_device=execution_device,
         )
         convert_cache = ModelConvertCache(
-            cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
+            cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache
         )
         loader = ModelLoadService(
             app_config=app_config,

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -151,7 +151,7 @@ class SqliteSessionQueue(SessionQueueBase):
 
             # TODO: how does this work in a multi-user scenario?
             current_queue_size = self._get_current_queue_size(queue_id)
-            max_queue_size = self.__invoker.services.configuration.get_config().max_queue_size
+            max_queue_size = self.__invoker.services.configuration.max_queue_size
             max_new_queue_items = max_queue_size - current_queue_size
 
             priority = 0

invokeai/app/services/shared/invocation_context.py

@@ -423,7 +423,7 @@ class ConfigInterface(InvocationContextInterface):
             The app's config.
         """
 
-        return self._services.configuration.get_config()
+        return self._services.configuration
 
 
 class UtilInterface(InvocationContextInterface):

invokeai/backend/image_util/depth_anything/__init__.py

@@ -9,14 +9,14 @@ from einops import repeat
 from PIL import Image
 from torchvision.transforms import Compose
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
 from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
 from invokeai.backend.util.devices import choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.backend.util.util import download_with_progress_bar
 
-config = InvokeAIAppConfig.get_config()
+config = get_config()
 logger = InvokeAILogger.get_logger(config=config)
 
 DEPTH_ANYTHING_MODELS = {

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -6,7 +6,7 @@ import pathlib
 import numpy as np
 import onnxruntime as ort
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import choose_torch_device
 from invokeai.backend.util.util import download_with_progress_bar
 
@@ -24,7 +24,7 @@ DWPOSE_MODELS = {
     },
 }
 
-config = InvokeAIAppConfig.get_config()
+config = get_config
 
 
 class Wholebody:

invokeai/backend/image_util/lama.py

@@ -6,9 +6,11 @@ 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.config_default import get_config
 from invokeai.backend.util.devices import choose_torch_device
 
+config = get_config()
+
 
 def norm_img(np_img):
     if len(np_img.shape) == 2:
@@ -29,7 +31,7 @@ def load_jit_model(url_or_path, device):
 class LaMA:
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
         device = choose_torch_device()
-        model_location = get_invokeai_config().models_path / "core/misc/lama/lama.pt"
+        model_location = get_config().models_path / "core/misc/lama/lama.pt"
         model = load_jit_model(model_location, device)
 
         image = np.asarray(input_image.convert("RGB"))

invokeai/backend/image_util/patchmatch.py

@@ -8,9 +8,9 @@ be suppressed or deferred
 import numpy as np
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
-config = InvokeAIAppConfig.get_config()
+config = get_config()
 
 
 class PatchMatch:

invokeai/backend/image_util/txt2mask.py

@@ -33,11 +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 InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
-config = InvokeAIAppConfig.get_config()
+config = get_config()
 
 
 class SegmentedGrayscale(object):

invokeai/backend/model_manager/merge.py

@@ -118,7 +118,7 @@ class ModelMerger(object):
         config = self._installer.app_config
         store = self._installer.record_store
         base_models: Set[BaseModelType] = set()
-        variant = None if self._installer.app_config.full_precision else "fp16"
+        variant = None if self._installer.app_config.precision == "float32" else "fp16"
 
         assert (
             len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -21,7 +21,7 @@ from diffusers.utils.outputs import BaseOutput
 from pydantic import Field
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
@@ -251,7 +251,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        config = InvokeAIAppConfig.get_config()
+        config = get_config()
         if config.attention_type == "xformers":
             self.enable_xformers_memory_efficient_attention()
             return

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -8,7 +8,7 @@ import torch
 from diffusers import UNet2DConditionModel
 from typing_extensions import TypeAlias
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningData,
     ExtraConditioningInfo,
@@ -54,7 +54,7 @@ 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 = InvokeAIAppConfig.get_config()
+        config = get_config()
         self.conditioning = None
         self.model = model
         self.model_forward_callback = model_forward_callback

invokeai/backend/util/devices.py

@@ -7,11 +7,12 @@ import torch
 from torch import autocast
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
-config = InvokeAIAppConfig.get_config()
+config = get_config()
 
 
 def choose_torch_device() -> torch.device:

invokeai/backend/util/logging.py

@@ -181,6 +181,7 @@ from pathlib import Path
 from typing import Any, Dict, Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
 try:
     import syslog
@@ -339,7 +340,7 @@ class InvokeAILogger(object):  # noqa D102
 
     @classmethod
     def get_logger(
-        cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
+        cls, name: str = "InvokeAI", config: InvokeAIAppConfig = get_config()
     ) -> logging.Logger:  # noqa D102
         if name in cls.loggers:
             return cls.loggers[name]