diff --git a/invokeai/app/services/config/config_default.py b/invokeai/app/services/config/config_default.py
index b39e916da3..2af775372d 100644
--- a/invokeai/app/services/config/config_default.py
+++ b/invokeai/app/services/config/config_default.py
@@ -185,7 +185,9 @@ from .config_base import InvokeAISettings
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
-DEFAULT_MAX_VRAM = 0.5
+DEFAULT_RAM_CACHE = 10.0
+DEFAULT_VRAM_CACHE = 0.25
+DEFAULT_CONVERT_CACHE = 20.0
 
 
 class Categories(object):
@@ -261,9 +263,9 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
 
     # CACHE
-    ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
-    vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
-    convert_cache       : float = Field(default=10.0, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
+    ram                 : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
+    vram                : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
+    convert_cache       : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
 
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
     log_memory_usage    : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
diff --git a/invokeai/backend/install/install_helper.py b/invokeai/backend/install/install_helper.py
index 8877e33092..9c386c209c 100644
--- a/invokeai/backend/install/install_helper.py
+++ b/invokeai/backend/install/install_helper.py
@@ -37,7 +37,7 @@ from invokeai.backend.model_manager.metadata import UnknownMetadataException
 from invokeai.backend.util.logging import InvokeAILogger
 
 # name of the starter models file
-INITIAL_MODELS = "INITIAL_MODELS2.yaml"
+INITIAL_MODELS = "INITIAL_MODELS.yaml"
 
 
 def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py
index 3cb7db6c82..4dfa2b070c 100755
--- a/invokeai/backend/install/invokeai_configure.py
+++ b/invokeai/backend/install/invokeai_configure.py
@@ -18,31 +18,30 @@ from argparse import Namespace
 from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import Any, get_args, get_type_hints
+from typing import Any, Optional, Set, Tuple, Type, get_args, get_type_hints
 from urllib import request
 
 import npyscreen
-import omegaconf
 import psutil
 import torch
 import transformers
-import yaml
-from diffusers import AutoencoderKL
+from diffusers import AutoencoderKL, ModelMixin
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
-from omegaconf import OmegaConf
-from pydantic import ValidationError
+from omegaconf import DictConfig, OmegaConf
+from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 import invokeai.configs as configs
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, hf_download_from_pretrained
-from invokeai.backend.model_management.model_probe import BaseModelType, ModelType
+from invokeai.backend.model_manager import BaseModelType, ModelType
+from invokeai.backend.util import choose_precision, choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
+from invokeai.frontend.install.model_install import addModelsForm
 
 # TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
@@ -61,7 +60,7 @@ warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
 
 
-def get_literal_fields(field) -> list[Any]:
+def get_literal_fields(field: str) -> Tuple[Any]:
     return get_args(get_type_hints(InvokeAIAppConfig).get(field))
 
 
@@ -80,8 +79,7 @@ ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
 GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
 GB = 1073741824  # GB in bytes
 HAS_CUDA = torch.cuda.is_available()
-_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
-
+_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0.0, 0.0)
 
 MAX_VRAM /= GB
 MAX_RAM = psutil.virtual_memory().total / GB
@@ -96,13 +94,15 @@ logger = InvokeAILogger.get_logger()
 
 
 class DummyWidgetValue(Enum):
+    """Dummy widget values."""
+
     zero = 0
     true = True
     false = False
 
 
 # --------------------------------------------
-def postscript(errors: None):
+def postscript(errors: Set[str]) -> None:
     if not any(errors):
         message = f"""
 ** INVOKEAI INSTALLATION SUCCESSFUL **
@@ -143,7 +143,7 @@ def yes_or_no(prompt: str, default_yes=True):
 
 
 # ---------------------------------------------
-def HfLogin(access_token) -> str:
+def HfLogin(access_token) -> None:
     """
     Helper for logging in to Huggingface
     The stdout capture is needed to hide the irrelevant "git credential helper" warning
@@ -162,7 +162,7 @@ def HfLogin(access_token) -> str:
 
 # -------------------------------------
 class ProgressBar:
-    def __init__(self, model_name="file"):
+    def __init__(self, model_name: str = "file"):
         self.pbar = None
         self.name = model_name
 
@@ -179,6 +179,22 @@ class ProgressBar:
         self.pbar.update(block_size)
 
 
+# ---------------------------------------------
+def hf_download_from_pretrained(model_class: Type[ModelMixin], model_name: str, destination: Path, **kwargs: Any):
+    filter = lambda x: "fp16 is not a valid" not in x.getMessage()  # noqa E731
+    logger.addFilter(filter)
+    try:
+        model = model_class.from_pretrained(
+            model_name,
+            resume_download=True,
+            **kwargs,
+        )
+        model.save_pretrained(destination, safe_serialization=True)
+    finally:
+        logger.removeFilter(filter)
+    return destination
+
+
 # ---------------------------------------------
 def download_with_progress_bar(model_url: str, model_dest: str, label: str = "the"):
     try:
@@ -249,6 +265,7 @@ def download_conversion_models():
 
 
 # ---------------------------------------------
+# TO DO: use the download queue here.
 def download_realesrgan():
     logger.info("Installing ESRGAN Upscaling models...")
     URLs = [
@@ -288,18 +305,19 @@ def download_lama():
 
 
 # ---------------------------------------------
-def download_support_models():
+def download_support_models() -> None:
     download_realesrgan()
     download_lama()
     download_conversion_models()
 
 
 # -------------------------------------
-def get_root(root: str = None) -> str:
+def get_root(root: Optional[str] = None) -> str:
     if root:
         return root
-    elif os.environ.get("INVOKEAI_ROOT"):
-        return os.environ.get("INVOKEAI_ROOT")
+    elif root := os.environ.get("INVOKEAI_ROOT"):
+        assert root is not None
+        return root
     else:
         return str(config.root_path)
 
@@ -455,6 +473,25 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             max_width=110,
             scroll_exit=True,
         )
+        self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name="Model disk conversion cache size (GB). This is used to cache safetensors files that need to be converted to diffusers..",
+            begin_entry_at=0,
+            editable=False,
+            color="CONTROL",
+            scroll_exit=True,
+        )
+        self.nextrely -= 1
+        self.disk = self.add_widget_intelligent(
+            npyscreen.Slider,
+            value=clip(old_opts.convert_cache, range=(0, 100), step=0.5),
+            out_of=100,
+            lowest=0.0,
+            step=0.5,
+            relx=8,
+            scroll_exit=True,
+        )
+        self.nextrely += 1
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
@@ -495,6 +532,14 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             )
         else:
             self.vram = DummyWidgetValue.zero
+
+        self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            value="Location of the database used to store model path and configuration information:",
+            editable=False,
+            color="CONTROL",
+        )
         self.nextrely += 1
         self.outdir = self.add_widget_intelligent(
             FileBox,
@@ -506,19 +551,21 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             labelColor="GOOD",
             begin_entry_at=40,
             max_height=3,
+            max_width=127,
             scroll_exit=True,
         )
         self.autoimport_dirs = {}
         self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
             FileBox,
-            name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
-            value=str(config.root_path / config.autoimport_dir),
+            name="Optional folder to scan for new checkpoints, ControlNets, LoRAs and TI models",
+            value=str(config.root_path / config.autoimport_dir) if config.autoimport_dir else "",
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
             labelColor="GOOD",
             begin_entry_at=32,
             max_height=3,
+            max_width=127,
             scroll_exit=True,
         )
         self.nextrely += 1
@@ -555,6 +602,10 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
         self.attention_slice_label.hidden = not show
         self.attention_slice_size.hidden = not show
 
+    def show_hide_model_conf_override(self, value):
+        self.model_conf_override.hidden = value
+        self.model_conf_override.display()
+
     def on_ok(self):
         options = self.marshall_arguments()
         if self.validate_field_values(options):
@@ -584,18 +635,21 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
         else:
             return True
 
-    def marshall_arguments(self):
+    def marshall_arguments(self) -> Namespace:
         new_opts = Namespace()
 
         for attr in [
             "ram",
             "vram",
+            "convert_cache",
             "outdir",
         ]:
             if hasattr(self, attr):
                 setattr(new_opts, attr, getattr(self, attr).value)
 
         for attr in self.autoimport_dirs:
+            if not self.autoimport_dirs[attr].value:
+                continue
             directory = Path(self.autoimport_dirs[attr].value)
             if directory.is_relative_to(config.root_path):
                 directory = directory.relative_to(config.root_path)
@@ -615,13 +669,14 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
 
 
 class EditOptApplication(npyscreen.NPSAppManaged):
-    def __init__(self, program_opts: Namespace, invokeai_opts: Namespace):
+    def __init__(self, program_opts: Namespace, invokeai_opts: InvokeAIAppConfig, install_helper: InstallHelper):
         super().__init__()
         self.program_opts = program_opts
         self.invokeai_opts = invokeai_opts
         self.user_cancelled = False
         self.autoload_pending = True
-        self.install_selections = default_user_selections(program_opts)
+        self.install_helper = install_helper
+        self.install_selections = default_user_selections(program_opts, install_helper)
 
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
@@ -640,16 +695,10 @@ class EditOptApplication(npyscreen.NPSAppManaged):
                 cycle_widgets=False,
             )
 
-    def new_opts(self):
+    def new_opts(self) -> Namespace:
         return self.options.marshall_arguments()
 
 
-def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Namespace:
-    editApp = EditOptApplication(program_opts, invokeai_opts)
-    editApp.run()
-    return editApp.new_opts()
-
-
 def default_ramcache() -> float:
     """Run a heuristic for the default RAM cache based on installed RAM."""
 
@@ -660,27 +709,18 @@ def default_ramcache() -> float:
     )  # 2.1 is just large enough for sd 1.5 ;-)
 
 
-def default_startup_options(init_file: Path) -> Namespace:
+def default_startup_options(init_file: Path) -> InvokeAIAppConfig:
     opts = InvokeAIAppConfig.get_config()
-    opts.ram = opts.ram or default_ramcache()
+    opts.ram = default_ramcache()
     return opts
 
 
-def default_user_selections(program_opts: Namespace) -> InstallSelections:
-    try:
-        installer = ModelInstall(config)
-    except omegaconf.errors.ConfigKeyError:
-        logger.warning("Your models.yaml file is corrupt or out of date. Reinitializing")
-        initialize_rootdir(config.root_path, True)
-        installer = ModelInstall(config)
-
-    models = installer.all_models()
+def default_user_selections(program_opts: Namespace, install_helper: InstallHelper) -> InstallSelections:
+    default_model = install_helper.default_model()
+    assert default_model is not None
+    default_models = [default_model] if program_opts.default_only else install_helper.recommended_models()
     return InstallSelections(
-        install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
-        if program_opts.default_only
-        else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
-        if program_opts.yes_to_all
-        else [],
+        install_models=default_models if program_opts.yes_to_all else [],
     )
 
 
@@ -716,21 +756,10 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
     path.mkdir(parents=True, exist_ok=True)
 
 
-def maybe_create_models_yaml(root: Path):
-    models_yaml = root / "configs" / "models.yaml"
-    if models_yaml.exists():
-        if OmegaConf.load(models_yaml).get("__metadata__"):  # up to date
-            return
-        else:
-            logger.info("Creating new models.yaml, original saved as models.yaml.orig")
-            models_yaml.rename(models_yaml.parent / "models.yaml.orig")
-
-    with open(models_yaml, "w") as yaml_file:
-        yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
-
-
 # -------------------------------------
-def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
+def run_console_ui(
+    program_opts: Namespace, initfile: Path, install_helper: InstallHelper
+) -> Tuple[Optional[Namespace], Optional[InstallSelections]]:
     invokeai_opts = default_startup_options(initfile)
     invokeai_opts.root = program_opts.root
 
@@ -739,22 +768,16 @@ def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace
             "Could not increase terminal size. Try running again with a larger window or smaller font size."
         )
 
-    # the install-models application spawns a subprocess to install
-    # models, and will crash unless this is set before running.
-    import torch
-
-    torch.multiprocessing.set_start_method("spawn")
-
-    editApp = EditOptApplication(program_opts, invokeai_opts)
+    editApp = EditOptApplication(program_opts, invokeai_opts, install_helper)
     editApp.run()
     if editApp.user_cancelled:
         return (None, None)
     else:
-        return (editApp.new_opts, editApp.install_selections)
+        return (editApp.new_opts(), editApp.install_selections)
 
 
 # -------------------------------------
-def write_opts(opts: Namespace, init_file: Path):
+def write_opts(opts: InvokeAIAppConfig, init_file: Path) -> None:
     """
     Update the invokeai.yaml file with values from current settings.
     """
@@ -762,7 +785,7 @@ def write_opts(opts: Namespace, init_file: Path):
     new_config = InvokeAIAppConfig.get_config()
     new_config.root = config.root
 
-    for key, value in opts.__dict__.items():
+    for key, value in opts.model_dump().items():
         if hasattr(new_config, key):
             setattr(new_config, key, value)
 
@@ -779,7 +802,7 @@ def default_output_dir() -> Path:
 
 
 # -------------------------------------
-def write_default_options(program_opts: Namespace, initfile: Path):
+def write_default_options(program_opts: Namespace, initfile: Path) -> None:
     opt = default_startup_options(initfile)
     write_opts(opt, initfile)
 
@@ -789,16 +812,11 @@ def write_default_options(program_opts: Namespace, initfile: Path):
 # the legacy Args object in order to parse
 # the old init file and write out the new
 # yaml format.
-def migrate_init_file(legacy_format: Path):
+def migrate_init_file(legacy_format: Path) -> None:
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    fields = [
-        x
-        for x, y in InvokeAIAppConfig.model_fields.items()
-        if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED"
-    ]
-    for attr in fields:
+    for attr in InvokeAIAppConfig.model_fields.keys():
         if hasattr(old, attr):
             try:
                 setattr(new, attr, getattr(old, attr))
@@ -819,7 +837,7 @@ def migrate_init_file(legacy_format: Path):
 
 
 # -------------------------------------
-def migrate_models(root: Path):
+def migrate_models(root: Path) -> None:
     from invokeai.backend.install.migrate_to_3 import do_migrate
 
     do_migrate(root, root)
@@ -838,7 +856,9 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
         ):
             logger.info("** Migrating invokeai.init to invokeai.yaml")
             migrate_init_file(old_init_file)
-            config.parse_args(argv=[], conf=OmegaConf.load(new_init_file))
+            omegaconf = OmegaConf.load(new_init_file)
+            assert isinstance(omegaconf, DictConfig)
+            config.parse_args(argv=[], conf=omegaconf)
 
             if old_hub.exists():
                 migrate_models(config.root_path)
@@ -849,7 +869,7 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
 
 
 # -------------------------------------
-def main() -> None:
+def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
     parser.add_argument(
         "--skip-sd-weights",
@@ -908,6 +928,7 @@ def main() -> None:
     if opt.full_precision:
         invoke_args.extend(["--precision", "float32"])
     config.parse_args(invoke_args)
+    config.precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
     logger = InvokeAILogger().get_logger(config=config)
 
     errors = set()
@@ -921,14 +942,18 @@ def main() -> None:
         # run this unconditionally in case new directories need to be added
         initialize_rootdir(config.root_path, opt.yes_to_all)
 
-        models_to_download = default_user_selections(opt)
+        # this will initialize the models.yaml file if not present
+        install_helper = InstallHelper(config, logger)
+
+        models_to_download = default_user_selections(opt, install_helper)
         new_init_file = config.root_path / "invokeai.yaml"
 
         if opt.yes_to_all:
             write_default_options(opt, new_init_file)
             init_options = Namespace(precision="float32" if opt.full_precision else "float16")
+
         else:
-            init_options, models_to_download = run_console_ui(opt, new_init_file)
+            init_options, models_to_download = run_console_ui(opt, new_init_file, install_helper)
             if init_options:
                 write_opts(init_options, new_init_file)
             else:
@@ -943,10 +968,12 @@ def main() -> None:
 
         if opt.skip_sd_weights:
             logger.warning("Skipping diffusion weights download per user request")
+
         elif models_to_download:
-            process_and_execute(opt, models_to_download)
+            install_helper.add_or_delete(models_to_download)
 
         postscript(errors=errors)
+
         if not opt.yes_to_all:
             input("Press any key to continue...")
     except WindowTooSmallException as e:
diff --git a/invokeai/backend/model_manager/load/load_default.py b/invokeai/backend/model_manager/load/load_default.py
index 2192c88ac2..c1dfe729af 100644
--- a/invokeai/backend/model_manager/load/load_default.py
+++ b/invokeai/backend/model_manager/load/load_default.py
@@ -19,7 +19,7 @@ from invokeai.backend.model_manager import (
 )
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats, ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data, calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import choose_torch_device, torch_dtype
diff --git a/invokeai/backend/util/devices.py b/invokeai/backend/util/devices.py
index b4f24d8483..a83d1045f7 100644
--- a/invokeai/backend/util/devices.py
+++ b/invokeai/backend/util/devices.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from contextlib import nullcontext
-from typing import Optional, Union
+from typing import Literal, Optional, Union
 
 import torch
 from torch import autocast
@@ -31,7 +31,9 @@ def choose_torch_device() -> torch.device:
 
 # We are in transition here from using a single global AppConfig to allowing multiple
 # configurations. It is strongly recommended to pass the app_config to this function.
-def choose_precision(device: torch.device, app_config: Optional[InvokeAIAppConfig] = None) -> str:
+def choose_precision(
+    device: torch.device, app_config: Optional[InvokeAIAppConfig] = None
+) -> Literal["float32", "float16", "bfloat16"]:
     """Return an appropriate precision for the given torch device."""
     app_config = app_config or config
     if device.type == "cuda":
diff --git a/invokeai/configs/INITIAL_MODELS.yaml b/invokeai/configs/INITIAL_MODELS.yaml
index c230665e3a..ca2283ab81 100644
--- a/invokeai/configs/INITIAL_MODELS.yaml
+++ b/invokeai/configs/INITIAL_MODELS.yaml
@@ -1,153 +1,157 @@
 # This file predefines a few models that the user may want to install.
 sd-1/main/stable-diffusion-v1-5:
    description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
-   repo_id: runwayml/stable-diffusion-v1-5
+   source: runwayml/stable-diffusion-v1-5
    recommended: True
    default: True
 sd-1/main/stable-diffusion-v1-5-inpainting:
    description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
-   repo_id: runwayml/stable-diffusion-inpainting
+   source: runwayml/stable-diffusion-inpainting
    recommended: True
 sd-2/main/stable-diffusion-2-1:
    description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-1
+   source: stabilityai/stable-diffusion-2-1
    recommended: False
 sd-2/main/stable-diffusion-2-inpainting:
    description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-inpainting
+   source: stabilityai/stable-diffusion-2-inpainting
    recommended: False
 sdxl/main/stable-diffusion-xl-base-1-0:
    description: Stable Diffusion XL base model (12 GB)
-   repo_id: stabilityai/stable-diffusion-xl-base-1.0
+   source: stabilityai/stable-diffusion-xl-base-1.0
    recommended: True
 sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
    description: Stable Diffusion XL refiner model (12 GB)
-   repo_id: stabilityai/stable-diffusion-xl-refiner-1.0
+   source: stabilityai/stable-diffusion-xl-refiner-1.0
    recommended: False
-sdxl/vae/sdxl-1-0-vae-fix:
-   description: Fine tuned version of the SDXL-1.0 VAE
-   repo_id: madebyollin/sdxl-vae-fp16-fix
+sdxl/vae/sdxl-vae-fp16-fix:
+   description: Version of the SDXL-1.0 VAE that works in half precision mode
+   source: madebyollin/sdxl-vae-fp16-fix
    recommended: True
 sd-1/main/Analog-Diffusion:
    description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
-   repo_id: wavymulder/Analog-Diffusion
+   source: wavymulder/Analog-Diffusion
    recommended: False
-sd-1/main/Deliberate_v5:
+sd-1/main/Deliberate:
    description: Versatile model that produces detailed images up to 768px (4.27 GB)
-   path: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors
+   source: XpucT/Deliberate
    recommended: False
 sd-1/main/Dungeons-and-Diffusion:
    description: Dungeons & Dragons characters (2.13 GB)
-   repo_id: 0xJustin/Dungeons-and-Diffusion
+   source: 0xJustin/Dungeons-and-Diffusion
    recommended: False
 sd-1/main/dreamlike-photoreal-2:
    description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
-   repo_id: dreamlike-art/dreamlike-photoreal-2.0
+   source: dreamlike-art/dreamlike-photoreal-2.0
    recommended: False
 sd-1/main/Inkpunk-Diffusion:
    description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
-   repo_id: Envvi/Inkpunk-Diffusion
+   source: Envvi/Inkpunk-Diffusion
    recommended: False
 sd-1/main/openjourney:
    description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
-   repo_id: prompthero/openjourney
+   source: prompthero/openjourney
    recommended: False
 sd-1/main/seek.art_MEGA:
-   repo_id: coreco/seek.art_MEGA
+   source: coreco/seek.art_MEGA
    description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
    recommended: False
 sd-1/main/trinart_stable_diffusion_v2:
    description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
-   repo_id: naclbit/trinart_stable_diffusion_v2
+   source: naclbit/trinart_stable_diffusion_v2
    recommended: False
 sd-1/controlnet/qrcode_monster:
-   repo_id: monster-labs/control_v1p_sd15_qrcode_monster
+   source: monster-labs/control_v1p_sd15_qrcode_monster
    subfolder: v2
 sd-1/controlnet/canny:
-   repo_id: lllyasviel/control_v11p_sd15_canny
+   source: lllyasviel/control_v11p_sd15_canny
    recommended: True
 sd-1/controlnet/inpaint:
-   repo_id: lllyasviel/control_v11p_sd15_inpaint
+   source: lllyasviel/control_v11p_sd15_inpaint
 sd-1/controlnet/mlsd:
-   repo_id: lllyasviel/control_v11p_sd15_mlsd
+   source: lllyasviel/control_v11p_sd15_mlsd
 sd-1/controlnet/depth:
-   repo_id: lllyasviel/control_v11f1p_sd15_depth
+   source: lllyasviel/control_v11f1p_sd15_depth
    recommended: True
 sd-1/controlnet/normal_bae:
-   repo_id: lllyasviel/control_v11p_sd15_normalbae
+   source: lllyasviel/control_v11p_sd15_normalbae
 sd-1/controlnet/seg:
-   repo_id: lllyasviel/control_v11p_sd15_seg
+   source: lllyasviel/control_v11p_sd15_seg
 sd-1/controlnet/lineart:
-   repo_id: lllyasviel/control_v11p_sd15_lineart
+   source: lllyasviel/control_v11p_sd15_lineart
    recommended: True
 sd-1/controlnet/lineart_anime:
-   repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
+   source: lllyasviel/control_v11p_sd15s2_lineart_anime
 sd-1/controlnet/openpose:
-   repo_id: lllyasviel/control_v11p_sd15_openpose
+   source: lllyasviel/control_v11p_sd15_openpose
    recommended: True
 sd-1/controlnet/scribble:
-   repo_id: lllyasviel/control_v11p_sd15_scribble
+   source: lllyasviel/control_v11p_sd15_scribble
    recommended: False
 sd-1/controlnet/softedge:
-   repo_id: lllyasviel/control_v11p_sd15_softedge
+   source: lllyasviel/control_v11p_sd15_softedge
 sd-1/controlnet/shuffle:
-   repo_id: lllyasviel/control_v11e_sd15_shuffle
+   source: lllyasviel/control_v11e_sd15_shuffle
 sd-1/controlnet/tile:
-   repo_id: lllyasviel/control_v11f1e_sd15_tile
+   source: lllyasviel/control_v11f1e_sd15_tile
 sd-1/controlnet/ip2p:
-   repo_id: lllyasviel/control_v11e_sd15_ip2p
+   source: lllyasviel/control_v11e_sd15_ip2p
 sd-1/t2i_adapter/canny-sd15:
-   repo_id: TencentARC/t2iadapter_canny_sd15v2
+   source: TencentARC/t2iadapter_canny_sd15v2
 sd-1/t2i_adapter/sketch-sd15:
-   repo_id: TencentARC/t2iadapter_sketch_sd15v2
+   source: TencentARC/t2iadapter_sketch_sd15v2
 sd-1/t2i_adapter/depth-sd15:
-   repo_id: TencentARC/t2iadapter_depth_sd15v2
+   source: TencentARC/t2iadapter_depth_sd15v2
 sd-1/t2i_adapter/zoedepth-sd15:
-   repo_id: TencentARC/t2iadapter_zoedepth_sd15v1
+   source: TencentARC/t2iadapter_zoedepth_sd15v1
 sdxl/t2i_adapter/canny-sdxl:
-   repo_id: TencentARC/t2i-adapter-canny-sdxl-1.0
+   source: TencentARC/t2i-adapter-canny-sdxl-1.0
 sdxl/t2i_adapter/zoedepth-sdxl:
-   repo_id: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
+   source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
 sdxl/t2i_adapter/lineart-sdxl:
-   repo_id: TencentARC/t2i-adapter-lineart-sdxl-1.0
+   source: TencentARC/t2i-adapter-lineart-sdxl-1.0
 sdxl/t2i_adapter/sketch-sdxl:
-   repo_id: TencentARC/t2i-adapter-sketch-sdxl-1.0
+   source: TencentARC/t2i-adapter-sketch-sdxl-1.0
 sd-1/embedding/EasyNegative:
-   path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
+   source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
    recommended: True
-sd-1/embedding/ahx-beta-453407d:
-   repo_id: sd-concepts-library/ahx-beta-453407d
+   description: A textual inversion to use in the negative prompt to reduce bad anatomy
+sd-1/lora/FlatColor:
+   source: https://civitai.com/models/6433/loraflatcolor
+   recommended: True
+   description: A LoRA that generates scenery using solid blocks of color
 sd-1/lora/Ink scenery:
-   path: https://civitai.com/api/download/models/83390
+   source: https://civitai.com/api/download/models/83390
+   description: Generate india ink-like landscapes
 sd-1/ip_adapter/ip_adapter_sd15:
-   repo_id: InvokeAI/ip_adapter_sd15
+   source: InvokeAI/ip_adapter_sd15
    recommended: True
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: IP-Adapter for SD 1.5 models
 sd-1/ip_adapter/ip_adapter_plus_sd15:
-   repo_id: InvokeAI/ip_adapter_plus_sd15
+   source: InvokeAI/ip_adapter_plus_sd15
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: Refined IP-Adapter for SD 1.5 models
 sd-1/ip_adapter/ip_adapter_plus_face_sd15:
-   repo_id: InvokeAI/ip_adapter_plus_face_sd15
+   source: InvokeAI/ip_adapter_plus_face_sd15
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: Refined IP-Adapter for SD 1.5 models, adapted for faces
 sdxl/ip_adapter/ip_adapter_sdxl:
-   repo_id: InvokeAI/ip_adapter_sdxl
+   source: InvokeAI/ip_adapter_sdxl
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sdxl_image_encoder
    description: IP-Adapter for SDXL models
 any/clip_vision/ip_adapter_sd_image_encoder:
-   repo_id: InvokeAI/ip_adapter_sd_image_encoder
+   source: InvokeAI/ip_adapter_sd_image_encoder
    recommended: False
    description: Required model for using IP-Adapters with SD-1/2 models
 any/clip_vision/ip_adapter_sdxl_image_encoder:
-   repo_id: InvokeAI/ip_adapter_sdxl_image_encoder
+   source: InvokeAI/ip_adapter_sdxl_image_encoder
    recommended: False
    description: Required model for using IP-Adapters with SDXL models
diff --git a/invokeai/configs/INITIAL_MODELS2.yaml b/invokeai/configs/INITIAL_MODELS.yaml.OLD
similarity index 59%
rename from invokeai/configs/INITIAL_MODELS2.yaml
rename to invokeai/configs/INITIAL_MODELS.yaml.OLD
index ca2283ab81..c230665e3a 100644
--- a/invokeai/configs/INITIAL_MODELS2.yaml
+++ b/invokeai/configs/INITIAL_MODELS.yaml.OLD
@@ -1,157 +1,153 @@
 # This file predefines a few models that the user may want to install.
 sd-1/main/stable-diffusion-v1-5:
    description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
-   source: runwayml/stable-diffusion-v1-5
+   repo_id: runwayml/stable-diffusion-v1-5
    recommended: True
    default: True
 sd-1/main/stable-diffusion-v1-5-inpainting:
    description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
-   source: runwayml/stable-diffusion-inpainting
+   repo_id: runwayml/stable-diffusion-inpainting
    recommended: True
 sd-2/main/stable-diffusion-2-1:
    description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
-   source: stabilityai/stable-diffusion-2-1
+   repo_id: stabilityai/stable-diffusion-2-1
    recommended: False
 sd-2/main/stable-diffusion-2-inpainting:
    description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
-   source: stabilityai/stable-diffusion-2-inpainting
+   repo_id: stabilityai/stable-diffusion-2-inpainting
    recommended: False
 sdxl/main/stable-diffusion-xl-base-1-0:
    description: Stable Diffusion XL base model (12 GB)
-   source: stabilityai/stable-diffusion-xl-base-1.0
+   repo_id: stabilityai/stable-diffusion-xl-base-1.0
    recommended: True
 sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
    description: Stable Diffusion XL refiner model (12 GB)
-   source: stabilityai/stable-diffusion-xl-refiner-1.0
+   repo_id: stabilityai/stable-diffusion-xl-refiner-1.0
    recommended: False
-sdxl/vae/sdxl-vae-fp16-fix:
-   description: Version of the SDXL-1.0 VAE that works in half precision mode
-   source: madebyollin/sdxl-vae-fp16-fix
+sdxl/vae/sdxl-1-0-vae-fix:
+   description: Fine tuned version of the SDXL-1.0 VAE
+   repo_id: madebyollin/sdxl-vae-fp16-fix
    recommended: True
 sd-1/main/Analog-Diffusion:
    description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
-   source: wavymulder/Analog-Diffusion
+   repo_id: wavymulder/Analog-Diffusion
    recommended: False
-sd-1/main/Deliberate:
+sd-1/main/Deliberate_v5:
    description: Versatile model that produces detailed images up to 768px (4.27 GB)
-   source: XpucT/Deliberate
+   path: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors
    recommended: False
 sd-1/main/Dungeons-and-Diffusion:
    description: Dungeons & Dragons characters (2.13 GB)
-   source: 0xJustin/Dungeons-and-Diffusion
+   repo_id: 0xJustin/Dungeons-and-Diffusion
    recommended: False
 sd-1/main/dreamlike-photoreal-2:
    description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
-   source: dreamlike-art/dreamlike-photoreal-2.0
+   repo_id: dreamlike-art/dreamlike-photoreal-2.0
    recommended: False
 sd-1/main/Inkpunk-Diffusion:
    description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
-   source: Envvi/Inkpunk-Diffusion
+   repo_id: Envvi/Inkpunk-Diffusion
    recommended: False
 sd-1/main/openjourney:
    description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
-   source: prompthero/openjourney
+   repo_id: prompthero/openjourney
    recommended: False
 sd-1/main/seek.art_MEGA:
-   source: coreco/seek.art_MEGA
+   repo_id: coreco/seek.art_MEGA
    description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
    recommended: False
 sd-1/main/trinart_stable_diffusion_v2:
    description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
-   source: naclbit/trinart_stable_diffusion_v2
+   repo_id: naclbit/trinart_stable_diffusion_v2
    recommended: False
 sd-1/controlnet/qrcode_monster:
-   source: monster-labs/control_v1p_sd15_qrcode_monster
+   repo_id: monster-labs/control_v1p_sd15_qrcode_monster
    subfolder: v2
 sd-1/controlnet/canny:
-   source: lllyasviel/control_v11p_sd15_canny
+   repo_id: lllyasviel/control_v11p_sd15_canny
    recommended: True
 sd-1/controlnet/inpaint:
-   source: lllyasviel/control_v11p_sd15_inpaint
+   repo_id: lllyasviel/control_v11p_sd15_inpaint
 sd-1/controlnet/mlsd:
-   source: lllyasviel/control_v11p_sd15_mlsd
+   repo_id: lllyasviel/control_v11p_sd15_mlsd
 sd-1/controlnet/depth:
-   source: lllyasviel/control_v11f1p_sd15_depth
+   repo_id: lllyasviel/control_v11f1p_sd15_depth
    recommended: True
 sd-1/controlnet/normal_bae:
-   source: lllyasviel/control_v11p_sd15_normalbae
+   repo_id: lllyasviel/control_v11p_sd15_normalbae
 sd-1/controlnet/seg:
-   source: lllyasviel/control_v11p_sd15_seg
+   repo_id: lllyasviel/control_v11p_sd15_seg
 sd-1/controlnet/lineart:
-   source: lllyasviel/control_v11p_sd15_lineart
+   repo_id: lllyasviel/control_v11p_sd15_lineart
    recommended: True
 sd-1/controlnet/lineart_anime:
-   source: lllyasviel/control_v11p_sd15s2_lineart_anime
+   repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
 sd-1/controlnet/openpose:
-   source: lllyasviel/control_v11p_sd15_openpose
+   repo_id: lllyasviel/control_v11p_sd15_openpose
    recommended: True
 sd-1/controlnet/scribble:
-   source: lllyasviel/control_v11p_sd15_scribble
+   repo_id: lllyasviel/control_v11p_sd15_scribble
    recommended: False
 sd-1/controlnet/softedge:
-   source: lllyasviel/control_v11p_sd15_softedge
+   repo_id: lllyasviel/control_v11p_sd15_softedge
 sd-1/controlnet/shuffle:
-   source: lllyasviel/control_v11e_sd15_shuffle
+   repo_id: lllyasviel/control_v11e_sd15_shuffle
 sd-1/controlnet/tile:
-   source: lllyasviel/control_v11f1e_sd15_tile
+   repo_id: lllyasviel/control_v11f1e_sd15_tile
 sd-1/controlnet/ip2p:
-   source: lllyasviel/control_v11e_sd15_ip2p
+   repo_id: lllyasviel/control_v11e_sd15_ip2p
 sd-1/t2i_adapter/canny-sd15:
-   source: TencentARC/t2iadapter_canny_sd15v2
+   repo_id: TencentARC/t2iadapter_canny_sd15v2
 sd-1/t2i_adapter/sketch-sd15:
-   source: TencentARC/t2iadapter_sketch_sd15v2
+   repo_id: TencentARC/t2iadapter_sketch_sd15v2
 sd-1/t2i_adapter/depth-sd15:
-   source: TencentARC/t2iadapter_depth_sd15v2
+   repo_id: TencentARC/t2iadapter_depth_sd15v2
 sd-1/t2i_adapter/zoedepth-sd15:
-   source: TencentARC/t2iadapter_zoedepth_sd15v1
+   repo_id: TencentARC/t2iadapter_zoedepth_sd15v1
 sdxl/t2i_adapter/canny-sdxl:
-   source: TencentARC/t2i-adapter-canny-sdxl-1.0
+   repo_id: TencentARC/t2i-adapter-canny-sdxl-1.0
 sdxl/t2i_adapter/zoedepth-sdxl:
-   source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
+   repo_id: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
 sdxl/t2i_adapter/lineart-sdxl:
-   source: TencentARC/t2i-adapter-lineart-sdxl-1.0
+   repo_id: TencentARC/t2i-adapter-lineart-sdxl-1.0
 sdxl/t2i_adapter/sketch-sdxl:
-   source: TencentARC/t2i-adapter-sketch-sdxl-1.0
+   repo_id: TencentARC/t2i-adapter-sketch-sdxl-1.0
 sd-1/embedding/EasyNegative:
-   source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
+   path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
    recommended: True
-   description: A textual inversion to use in the negative prompt to reduce bad anatomy
-sd-1/lora/FlatColor:
-   source: https://civitai.com/models/6433/loraflatcolor
-   recommended: True
-   description: A LoRA that generates scenery using solid blocks of color
+sd-1/embedding/ahx-beta-453407d:
+   repo_id: sd-concepts-library/ahx-beta-453407d
 sd-1/lora/Ink scenery:
-   source: https://civitai.com/api/download/models/83390
-   description: Generate india ink-like landscapes
+   path: https://civitai.com/api/download/models/83390
 sd-1/ip_adapter/ip_adapter_sd15:
-   source: InvokeAI/ip_adapter_sd15
+   repo_id: InvokeAI/ip_adapter_sd15
    recommended: True
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: IP-Adapter for SD 1.5 models
 sd-1/ip_adapter/ip_adapter_plus_sd15:
-   source: InvokeAI/ip_adapter_plus_sd15
+   repo_id: InvokeAI/ip_adapter_plus_sd15
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: Refined IP-Adapter for SD 1.5 models
 sd-1/ip_adapter/ip_adapter_plus_face_sd15:
-   source: InvokeAI/ip_adapter_plus_face_sd15
+   repo_id: InvokeAI/ip_adapter_plus_face_sd15
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sd_image_encoder
    description: Refined IP-Adapter for SD 1.5 models, adapted for faces
 sdxl/ip_adapter/ip_adapter_sdxl:
-   source: InvokeAI/ip_adapter_sdxl
+   repo_id: InvokeAI/ip_adapter_sdxl
    recommended: False
    requires:
       - InvokeAI/ip_adapter_sdxl_image_encoder
    description: IP-Adapter for SDXL models
 any/clip_vision/ip_adapter_sd_image_encoder:
-   source: InvokeAI/ip_adapter_sd_image_encoder
+   repo_id: InvokeAI/ip_adapter_sd_image_encoder
    recommended: False
    description: Required model for using IP-Adapters with SD-1/2 models
 any/clip_vision/ip_adapter_sdxl_image_encoder:
-   source: InvokeAI/ip_adapter_sdxl_image_encoder
+   repo_id: InvokeAI/ip_adapter_sdxl_image_encoder
    recommended: False
    description: Required model for using IP-Adapters with SDXL models
diff --git a/invokeai/frontend/install/model_install.py b/invokeai/frontend/install/model_install.py
index e23538ffd6..22b132370e 100644
--- a/invokeai/frontend/install/model_install.py
+++ b/invokeai/frontend/install/model_install.py
@@ -6,47 +6,45 @@
 
 """
 This is the npyscreen frontend to the model installation application.
-The work is actually done in backend code in model_install_backend.py.
+It is currently named model_install2.py, but will ultimately replace model_install.py.
 """
 
 import argparse
 import curses
-import logging
 import sys
-import textwrap
 import traceback
+import warnings
 from argparse import Namespace
-from multiprocessing import Process
-from multiprocessing.connection import Connection, Pipe
-from pathlib import Path
 from shutil import get_terminal_size
-from typing import Optional
+from typing import Any, Dict, List, Optional, Set
 
 import npyscreen
 import torch
 from npyscreen import widget
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, SchedulerPredictionType
-from invokeai.backend.model_management import ModelManager, ModelType
+from invokeai.app.services.model_install import ModelInstallServiceBase
+from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
+from invokeai.backend.model_manager import ModelType
 from invokeai.backend.util import choose_precision, choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.widgets import (
     MIN_COLS,
     MIN_LINES,
-    BufferBox,
     CenteredTitleText,
     CyclingForm,
     MultiSelectColumns,
     SingleSelectColumns,
     TextBox,
     WindowTooSmallException,
-    select_stable_diffusion_config_file,
     set_min_terminal_size,
 )
 
+warnings.filterwarnings("ignore", category=UserWarning)  # noqa: E402
 config = InvokeAIAppConfig.get_config()
-logger = InvokeAILogger.get_logger()
+logger = InvokeAILogger.get_logger("ModelInstallService")
+logger.setLevel("WARNING")
+# logger.setLevel('DEBUG')
 
 # build a table mapping all non-printable characters to None
 # for stripping control characters
@@ -58,44 +56,42 @@ MAX_OTHER_MODELS = 72
 
 
 def make_printable(s: str) -> str:
-    """Replace non-printable characters in a string"""
+    """Replace non-printable characters in a string."""
     return s.translate(NOPRINT_TRANS_TABLE)
 
 
 class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
+    """Main form for interactive TUI."""
+
     # for responsive resizing set to False, but this seems to cause a crash!
     FIX_MINIMUM_SIZE_WHEN_CREATED = True
 
     # for persistence
     current_tab = 0
 
-    def __init__(self, parentApp, name, multipage=False, *args, **keywords):
+    def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
         self.multipage = multipage
         self.subprocess = None
-        super().__init__(parentApp=parentApp, name=name, *args, **keywords)  # noqa: B026 # TODO: maybe this is bad?
+        super().__init__(parentApp=parentApp, name=name, **keywords)
 
-    def create(self):
+    def create(self) -> None:
+        self.installer = self.parentApp.install_helper.installer
+        self.model_labels = self._get_model_labels()
         self.keypress_timeout = 10
         self.counter = 0
         self.subprocess_connection = None
 
-        if not config.model_conf_path.exists():
-            with open(config.model_conf_path, "w") as file:
-                print("# InvokeAI model configuration file", file=file)
-        self.installer = ModelInstall(config)
-        self.all_models = self.installer.all_models()
-        self.starter_models = self.installer.starter_models()
-        self.model_labels = self._get_model_labels()
         window_width, window_height = get_terminal_size()
 
-        self.nextrely -= 1
+        # npyscreen has no typing hints
+        self.nextrely -= 1  # type: ignore
         self.add_widget_intelligent(
             npyscreen.FixedText,
             value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
             editable=False,
             color="CAUTION",
         )
-        self.nextrely += 1
+        self.nextrely += 1  # type: ignore
         self.tabs = self.add_widget_intelligent(
             SingleSelectColumns,
             values=[
@@ -115,9 +111,9 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         )
         self.tabs.on_changed = self._toggle_tables
 
-        top_of_table = self.nextrely
+        top_of_table = self.nextrely  # type: ignore
         self.starter_pipelines = self.add_starter_pipelines()
-        bottom_of_table = self.nextrely
+        bottom_of_table = self.nextrely  # type: ignore
 
         self.nextrely = top_of_table
         self.pipeline_models = self.add_pipeline_widgets(
@@ -162,15 +158,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         self.nextrely = bottom_of_table + 1
 
-        self.monitor = self.add_widget_intelligent(
-            BufferBox,
-            name="Log Messages",
-            editable=False,
-            max_height=6,
-        )
-
         self.nextrely += 1
-        done_label = "APPLY CHANGES"
         back_label = "BACK"
         cancel_label = "CANCEL"
         current_position = self.nextrely
@@ -186,14 +174,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
             )
             self.nextrely = current_position
-            self.ok_button = self.add_widget_intelligent(
-                npyscreen.ButtonPress,
-                name=done_label,
-                relx=(window_width - len(done_label)) // 2,
-                when_pressed_function=self.on_execute,
-            )
 
-        label = "APPLY CHANGES & EXIT"
+        label = "APPLY CHANGES"
         self.nextrely = current_position
         self.done = self.add_widget_intelligent(
             npyscreen.ButtonPress,
@@ -210,17 +192,16 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
     ############# diffusers tab ##########
     def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
         """Add widgets responsible for selecting diffusers models"""
-        widgets = {}
-        models = self.all_models
-        starters = self.starter_models
-        starter_model_labels = self.model_labels
+        widgets: Dict[str, npyscreen.widget] = {}
 
-        self.installed_models = sorted([x for x in starters if models[x].installed])
+        all_models = self.all_models  # master dict of all models, indexed by key
+        model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
+        model_labels = [self.model_labels[x] for x in model_list]
 
         widgets.update(
             label1=self.add_widget_intelligent(
                 CenteredTitleText,
-                name="Select from a starter set of Stable Diffusion models from HuggingFace.",
+                name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
                 editable=False,
                 labelColor="CAUTION",
             )
@@ -230,23 +211,24 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         # if user has already installed some initial models, then don't patronize them
         # by showing more recommendations
         show_recommended = len(self.installed_models) == 0
-        keys = [x for x in models.keys() if x in starters]
+
+        checked = [
+            model_list.index(x)
+            for x in model_list
+            if (show_recommended and all_models[x].recommended) or all_models[x].installed
+        ]
         widgets.update(
             models_selected=self.add_widget_intelligent(
                 MultiSelectColumns,
                 columns=1,
                 name="Install Starter Models",
-                values=[starter_model_labels[x] for x in keys],
-                value=[
-                    keys.index(x)
-                    for x in keys
-                    if (show_recommended and models[x].recommended) or (x in self.installed_models)
-                ],
-                max_height=len(starters) + 1,
+                values=model_labels,
+                value=checked,
+                max_height=len(model_list) + 1,
                 relx=4,
                 scroll_exit=True,
             ),
-            models=keys,
+            models=model_list,
         )
 
         self.nextrely += 1
@@ -257,14 +239,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self,
         model_type: ModelType,
         window_width: int = 120,
-        install_prompt: str = None,
-        exclude: set = None,
+        install_prompt: Optional[str] = None,
+        exclude: Optional[Set[str]] = None,
     ) -> dict[str, npyscreen.widget]:
         """Generic code to create model selection widgets"""
         if exclude is None:
             exclude = set()
-        widgets = {}
-        model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and x not in exclude]
+        widgets: Dict[str, npyscreen.widget] = {}
+        all_models = self.all_models
+        model_list = sorted(
+            [x for x in all_models if all_models[x].type == model_type and x not in exclude],
+            key=lambda x: all_models[x].name or "",
+        )
         model_labels = [self.model_labels[x] for x in model_list]
 
         show_recommended = len(self.installed_models) == 0
@@ -300,7 +286,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                     value=[
                         model_list.index(x)
                         for x in model_list
-                        if (show_recommended and self.all_models[x].recommended) or self.all_models[x].installed
+                        if (show_recommended and all_models[x].recommended) or all_models[x].installed
                     ],
                     max_height=len(model_list) // columns + 1,
                     relx=4,
@@ -324,7 +310,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             download_ids=self.add_widget_intelligent(
                 TextBox,
                 name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
-                max_height=4,
+                max_height=6,
                 scroll_exit=True,
                 editable=True,
             )
@@ -349,13 +335,13 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         return widgets
 
-    def resize(self):
+    def resize(self) -> None:
         super().resize()
         if s := self.starter_pipelines.get("models_selected"):
-            keys = [x for x in self.all_models.keys() if x in self.starter_models]
-            s.values = [self.model_labels[x] for x in keys]
+            if model_list := self.starter_pipelines.get("models"):
+                s.values = [self.model_labels[x] for x in model_list]
 
-    def _toggle_tables(self, value=None):
+    def _toggle_tables(self, value: List[int]) -> None:
         selected_tab = value[0]
         widgets = [
             self.starter_pipelines,
@@ -385,17 +371,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.display()
 
     def _get_model_labels(self) -> dict[str, str]:
+        """Return a list of trimmed labels for all models."""
         window_width, window_height = get_terminal_size()
         checkbox_width = 4
         spacing_width = 2
+        result = {}
 
         models = self.all_models
-        label_width = max([len(models[x].name) for x in models])
+        label_width = max([len(models[x].name or "") for x in self.starter_models])
         description_width = window_width - label_width - checkbox_width - spacing_width
 
-        result = {}
-        for x in models.keys():
-            description = models[x].description
+        for key in self.all_models:
+            description = models[key].description
             description = (
                 description[0 : description_width - 3] + "..."
                 if description and len(description) > description_width
@@ -403,7 +390,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 if description
                 else ""
             )
-            result[x] = f"%-{label_width}s %s" % (models[x].name, description)
+            result[key] = f"%-{label_width}s %s" % (models[key].name, description)
+
         return result
 
     def _get_columns(self) -> int:
@@ -413,50 +401,40 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
     def confirm_deletions(self, selections: InstallSelections) -> bool:
         remove_models = selections.remove_models
-        if len(remove_models) > 0:
-            mods = "\n".join([ModelManager.parse_key(x)[0] for x in remove_models])
-            return npyscreen.notify_ok_cancel(
+        if remove_models:
+            model_names = [self.all_models[x].name or "" for x in remove_models]
+            mods = "\n".join(model_names)
+            is_ok = npyscreen.notify_ok_cancel(
                 f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
             )
+            assert isinstance(is_ok, bool)  # npyscreen doesn't have return type annotations
+            return is_ok
         else:
             return True
 
-    def on_execute(self):
-        self.marshall_arguments()
-        app = self.parentApp
-        if not self.confirm_deletions(app.install_selections):
-            return
+    @property
+    def all_models(self) -> Dict[str, UnifiedModelInfo]:
+        # npyscreen doesn't having typing hints
+        return self.parentApp.install_helper.all_models  # type: ignore
 
-        self.monitor.entry_widget.buffer(["Processing..."], scroll_end=True)
-        self.ok_button.hidden = True
-        self.display()
+    @property
+    def starter_models(self) -> List[str]:
+        return self.parentApp.install_helper._starter_models  # type: ignore
 
-        # TO DO: Spawn a worker thread, not a subprocess
-        parent_conn, child_conn = Pipe()
-        p = Process(
-            target=process_and_execute,
-            kwargs={
-                "opt": app.program_opts,
-                "selections": app.install_selections,
-                "conn_out": child_conn,
-            },
-        )
-        p.start()
-        child_conn.close()
-        self.subprocess_connection = parent_conn
-        self.subprocess = p
-        app.install_selections = InstallSelections()
+    @property
+    def installed_models(self) -> List[str]:
+        return self.parentApp.install_helper._installed_models  # type: ignore
 
-    def on_back(self):
+    def on_back(self) -> None:
         self.parentApp.switchFormPrevious()
         self.editing = False
 
-    def on_cancel(self):
+    def on_cancel(self) -> None:
         self.parentApp.setNextForm(None)
         self.parentApp.user_cancelled = True
         self.editing = False
 
-    def on_done(self):
+    def on_done(self) -> None:
         self.marshall_arguments()
         if not self.confirm_deletions(self.parentApp.install_selections):
             return
@@ -464,77 +442,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.parentApp.user_cancelled = False
         self.editing = False
 
-    ########## This routine monitors the child process that is performing model installation and removal #####
-    def while_waiting(self):
-        """Called during idle periods. Main task is to update the Log Messages box with messages
-        from the child process that does the actual installation/removal"""
-        c = self.subprocess_connection
-        if not c:
-            return
-
-        monitor_widget = self.monitor.entry_widget
-        while c.poll():
-            try:
-                data = c.recv_bytes().decode("utf-8")
-                data.strip("\n")
-
-                # processing child is requesting user input to select the
-                # right configuration file
-                if data.startswith("*need v2 config"):
-                    _, model_path, *_ = data.split(":", 2)
-                    self._return_v2_config(model_path)
-
-                # processing child is done
-                elif data == "*done*":
-                    self._close_subprocess_and_regenerate_form()
-                    break
-
-                # update the log message box
-                else:
-                    data = make_printable(data)
-                    data = data.replace("[A", "")
-                    monitor_widget.buffer(
-                        textwrap.wrap(
-                            data,
-                            width=monitor_widget.width,
-                            subsequent_indent="   ",
-                        ),
-                        scroll_end=True,
-                    )
-                    self.display()
-            except (EOFError, OSError):
-                self.subprocess_connection = None
-
-    def _return_v2_config(self, model_path: str):
-        c = self.subprocess_connection
-        model_name = Path(model_path).name
-        message = select_stable_diffusion_config_file(model_name=model_name)
-        c.send_bytes(message.encode("utf-8"))
-
-    def _close_subprocess_and_regenerate_form(self):
-        app = self.parentApp
-        self.subprocess_connection.close()
-        self.subprocess_connection = None
-        self.monitor.entry_widget.buffer(["** Action Complete **"])
-        self.display()
-
-        # rebuild the form, saving and restoring some of the fields that need to be preserved.
-        saved_messages = self.monitor.entry_widget.values
-
-        app.main_form = app.addForm(
-            "MAIN",
-            addModelsForm,
-            name="Install Stable Diffusion Models",
-            multipage=self.multipage,
-        )
-        app.switchForm("MAIN")
-
-        app.main_form.monitor.entry_widget.values = saved_messages
-        app.main_form.monitor.entry_widget.buffer([""], scroll_end=True)
-        # app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
-        # app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
-
-    def marshall_arguments(self):
+    def marshall_arguments(self) -> None:
         """
         Assemble arguments and store as attributes of the application:
         .starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
@@ -564,46 +472,24 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             models_to_install = [x for x in selected if not self.all_models[x].installed]
             models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
             selections.remove_models.extend(models_to_remove)
-            selections.install_models.extend(
-                all_models[x].path or all_models[x].repo_id
-                for x in models_to_install
-                if all_models[x].path or all_models[x].repo_id
-            )
+            selections.install_models.extend([all_models[x] for x in models_to_install])
 
         # models located in the 'download_ids" section
         for section in ui_sections:
             if downloads := section.get("download_ids"):
-                selections.install_models.extend(downloads.value.split())
-
-        # NOT NEEDED - DONE IN BACKEND NOW
-        # # special case for the ipadapter_models. If any of the adapters are
-        # # chosen, then we add the corresponding encoder(s) to the install list.
-        # section = self.ipadapter_models
-        # if section.get("models_selected"):
-        #     selected_adapters = [
-        #         self.all_models[section["models"][x]].name for x in section.get("models_selected").value
-        #     ]
-        #     encoders = []
-        #     if any(["sdxl" in x for x in selected_adapters]):
-        #         encoders.append("ip_adapter_sdxl_image_encoder")
-        #     if any(["sd15" in x for x in selected_adapters]):
-        #         encoders.append("ip_adapter_sd_image_encoder")
-        #     for encoder in encoders:
-        #         key = f"any/clip_vision/{encoder}"
-        #         repo_id = f"InvokeAI/{encoder}"
-        #         if key not in self.all_models:
-        #             selections.install_models.append(repo_id)
+                models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
+                selections.install_models.extend(models)
 
 
-class AddModelApplication(npyscreen.NPSAppManaged):
-    def __init__(self, opt):
+class AddModelApplication(npyscreen.NPSAppManaged):  # type: ignore
+    def __init__(self, opt: Namespace, install_helper: InstallHelper):
         super().__init__()
         self.program_opts = opt
         self.user_cancelled = False
-        # self.autoload_pending = True
         self.install_selections = InstallSelections()
+        self.install_helper = install_helper
 
-    def onStart(self):
+    def onStart(self) -> None:
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
         self.main_form = self.addForm(
             "MAIN",
@@ -613,138 +499,62 @@ class AddModelApplication(npyscreen.NPSAppManaged):
         )
 
 
-class StderrToMessage:
-    def __init__(self, connection: Connection):
-        self.connection = connection
-
-    def write(self, data: str):
-        self.connection.send_bytes(data.encode("utf-8"))
-
-    def flush(self):
-        pass
+def list_models(installer: ModelInstallServiceBase, model_type: ModelType):
+    """Print out all models of type model_type."""
+    models = installer.record_store.search_by_attr(model_type=model_type)
+    print(f"Installed models of type `{model_type}`:")
+    for model in models:
+        path = (config.models_path / model.path).resolve()
+        print(f"{model.name:40}{model.base.value:5}{model.type.value:8}{model.format.value:12}{path}")
 
 
 # --------------------------------------------------------
-def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None) -> SchedulerPredictionType:
-    if tui_conn:
-        logger.debug("Waiting for user response...")
-        return _ask_user_for_pt_tui(model_path, tui_conn)
-    else:
-        return _ask_user_for_pt_cmdline(model_path)
-
-
-def _ask_user_for_pt_cmdline(model_path: Path) -> Optional[SchedulerPredictionType]:
-    choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
-    print(
-        f"""
-Please select the scheduler prediction type of the checkpoint named {model_path.name}:
-[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
-[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
-[3] Accept the best guess;  you can fix it in the Web UI later
-"""
-    )
-    choice = None
-    ok = False
-    while not ok:
-        try:
-            choice = input("select [3]> ").strip()
-            if not choice:
-                return None
-            choice = choices[int(choice) - 1]
-            ok = True
-        except (ValueError, IndexError):
-            print(f"{choice} is not a valid choice")
-        except EOFError:
-            return
-    return choice
-
-
-def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
-    tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
-    # note that we don't do any status checking here
-    response = tui_conn.recv_bytes().decode("utf-8")
-    if response is None:
-        return None
-    elif response == "epsilon":
-        return SchedulerPredictionType.epsilon
-    elif response == "v":
-        return SchedulerPredictionType.VPrediction
-    elif response == "guess":
-        return None
-    else:
-        return None
-
-
-# --------------------------------------------------------
-def process_and_execute(
-    opt: Namespace,
-    selections: InstallSelections,
-    conn_out: Connection = None,
-):
-    # need to reinitialize config in subprocess
-    config = InvokeAIAppConfig.get_config()
-    args = ["--root", opt.root] if opt.root else []
-    config.parse_args(args)
-
-    # set up so that stderr is sent to conn_out
-    if conn_out:
-        translator = StderrToMessage(conn_out)
-        sys.stderr = translator
-        sys.stdout = translator
-        logger = InvokeAILogger.get_logger()
-        logger.handlers.clear()
-        logger.addHandler(logging.StreamHandler(translator))
-
-    installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x, conn_out))
-    installer.install(selections)
-
-    if conn_out:
-        conn_out.send_bytes("*done*".encode("utf-8"))
-        conn_out.close()
-
-
-# --------------------------------------------------------
-def select_and_download_models(opt: Namespace):
+def select_and_download_models(opt: Namespace) -> None:
+    """Prompt user for install/delete selections and execute."""
     precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
-    config.precision = precision
-    installer = ModelInstall(config, prediction_type_helper=ask_user_for_prediction_type)
+    # unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
+    config.precision = precision  # type: ignore
+    install_helper = InstallHelper(config, logger)
+    installer = install_helper.installer
+
     if opt.list_models:
-        installer.list_models(opt.list_models)
+        list_models(installer, opt.list_models)
+
     elif opt.add or opt.delete:
-        selections = InstallSelections(install_models=opt.add or [], remove_models=opt.delete or [])
-        installer.install(selections)
+        selections = InstallSelections(
+            install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
+        )
+        install_helper.add_or_delete(selections)
+
     elif opt.default_only:
-        selections = InstallSelections(install_models=installer.default_model())
-        installer.install(selections)
+        default_model = install_helper.default_model()
+        assert default_model is not None
+        selections = InstallSelections(install_models=[default_model])
+        install_helper.add_or_delete(selections)
+
     elif opt.yes_to_all:
-        selections = InstallSelections(install_models=installer.recommended_models())
-        installer.install(selections)
+        selections = InstallSelections(install_models=install_helper.recommended_models())
+        install_helper.add_or_delete(selections)
 
     # this is where the TUI is called
     else:
-        # needed to support the probe() method running under a subprocess
-        torch.multiprocessing.set_start_method("spawn")
-
         if not set_min_terminal_size(MIN_COLS, MIN_LINES):
             raise WindowTooSmallException(
                 "Could not increase terminal size. Try running again with a larger window or smaller font size."
             )
 
-        installApp = AddModelApplication(opt)
+        installApp = AddModelApplication(opt, install_helper)
         try:
             installApp.run()
-        except KeyboardInterrupt as e:
-            if hasattr(installApp, "main_form"):
-                if installApp.main_form.subprocess and installApp.main_form.subprocess.is_alive():
-                    logger.info("Terminating subprocesses")
-                    installApp.main_form.subprocess.terminate()
-                    installApp.main_form.subprocess = None
-            raise e
-        process_and_execute(opt, installApp.install_selections)
+        except KeyboardInterrupt:
+            print("Aborted...")
+            sys.exit(-1)
+
+        install_helper.add_or_delete(installApp.install_selections)
 
 
 # -------------------------------------
-def main():
+def main() -> None:
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
     parser.add_argument(
         "--add",
@@ -754,7 +564,7 @@ def main():
     parser.add_argument(
         "--delete",
         nargs="*",
-        help="List of names of models to idelete",
+        help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
     )
     parser.add_argument(
         "--full-precision",
@@ -781,14 +591,6 @@ def main():
         choices=[x.value for x in ModelType],
         help="list installed models",
     )
-    parser.add_argument(
-        "--config_file",
-        "-c",
-        dest="config_file",
-        type=str,
-        default=None,
-        help="path to configuration file to create",
-    )
     parser.add_argument(
         "--root_dir",
         dest="root",
diff --git a/invokeai/frontend/install/model_install2.py b/invokeai/frontend/install/model_install.py.OLD
similarity index 57%
rename from invokeai/frontend/install/model_install2.py
rename to invokeai/frontend/install/model_install.py.OLD
index 22b132370e..e23538ffd6 100644
--- a/invokeai/frontend/install/model_install2.py
+++ b/invokeai/frontend/install/model_install.py.OLD
@@ -6,45 +6,47 @@
 
 """
 This is the npyscreen frontend to the model installation application.
-It is currently named model_install2.py, but will ultimately replace model_install.py.
+The work is actually done in backend code in model_install_backend.py.
 """
 
 import argparse
 import curses
+import logging
 import sys
+import textwrap
 import traceback
-import warnings
 from argparse import Namespace
+from multiprocessing import Process
+from multiprocessing.connection import Connection, Pipe
+from pathlib import Path
 from shutil import get_terminal_size
-from typing import Any, Dict, List, Optional, Set
+from typing import Optional
 
 import npyscreen
 import torch
 from npyscreen import widget
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_install import ModelInstallServiceBase
-from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
-from invokeai.backend.model_manager import ModelType
+from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, SchedulerPredictionType
+from invokeai.backend.model_management import ModelManager, ModelType
 from invokeai.backend.util import choose_precision, choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.widgets import (
     MIN_COLS,
     MIN_LINES,
+    BufferBox,
     CenteredTitleText,
     CyclingForm,
     MultiSelectColumns,
     SingleSelectColumns,
     TextBox,
     WindowTooSmallException,
+    select_stable_diffusion_config_file,
     set_min_terminal_size,
 )
 
-warnings.filterwarnings("ignore", category=UserWarning)  # noqa: E402
 config = InvokeAIAppConfig.get_config()
-logger = InvokeAILogger.get_logger("ModelInstallService")
-logger.setLevel("WARNING")
-# logger.setLevel('DEBUG')
+logger = InvokeAILogger.get_logger()
 
 # build a table mapping all non-printable characters to None
 # for stripping control characters
@@ -56,42 +58,44 @@ MAX_OTHER_MODELS = 72
 
 
 def make_printable(s: str) -> str:
-    """Replace non-printable characters in a string."""
+    """Replace non-printable characters in a string"""
     return s.translate(NOPRINT_TRANS_TABLE)
 
 
 class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
-    """Main form for interactive TUI."""
-
     # for responsive resizing set to False, but this seems to cause a crash!
     FIX_MINIMUM_SIZE_WHEN_CREATED = True
 
     # for persistence
     current_tab = 0
 
-    def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
+    def __init__(self, parentApp, name, multipage=False, *args, **keywords):
         self.multipage = multipage
         self.subprocess = None
-        super().__init__(parentApp=parentApp, name=name, **keywords)
+        super().__init__(parentApp=parentApp, name=name, *args, **keywords)  # noqa: B026 # TODO: maybe this is bad?
 
-    def create(self) -> None:
-        self.installer = self.parentApp.install_helper.installer
-        self.model_labels = self._get_model_labels()
+    def create(self):
         self.keypress_timeout = 10
         self.counter = 0
         self.subprocess_connection = None
 
+        if not config.model_conf_path.exists():
+            with open(config.model_conf_path, "w") as file:
+                print("# InvokeAI model configuration file", file=file)
+        self.installer = ModelInstall(config)
+        self.all_models = self.installer.all_models()
+        self.starter_models = self.installer.starter_models()
+        self.model_labels = self._get_model_labels()
         window_width, window_height = get_terminal_size()
 
-        # npyscreen has no typing hints
-        self.nextrely -= 1  # type: ignore
+        self.nextrely -= 1
         self.add_widget_intelligent(
             npyscreen.FixedText,
             value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
             editable=False,
             color="CAUTION",
         )
-        self.nextrely += 1  # type: ignore
+        self.nextrely += 1
         self.tabs = self.add_widget_intelligent(
             SingleSelectColumns,
             values=[
@@ -111,9 +115,9 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         )
         self.tabs.on_changed = self._toggle_tables
 
-        top_of_table = self.nextrely  # type: ignore
+        top_of_table = self.nextrely
         self.starter_pipelines = self.add_starter_pipelines()
-        bottom_of_table = self.nextrely  # type: ignore
+        bottom_of_table = self.nextrely
 
         self.nextrely = top_of_table
         self.pipeline_models = self.add_pipeline_widgets(
@@ -158,7 +162,15 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         self.nextrely = bottom_of_table + 1
 
+        self.monitor = self.add_widget_intelligent(
+            BufferBox,
+            name="Log Messages",
+            editable=False,
+            max_height=6,
+        )
+
         self.nextrely += 1
+        done_label = "APPLY CHANGES"
         back_label = "BACK"
         cancel_label = "CANCEL"
         current_position = self.nextrely
@@ -174,8 +186,14 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
             )
             self.nextrely = current_position
+            self.ok_button = self.add_widget_intelligent(
+                npyscreen.ButtonPress,
+                name=done_label,
+                relx=(window_width - len(done_label)) // 2,
+                when_pressed_function=self.on_execute,
+            )
 
-        label = "APPLY CHANGES"
+        label = "APPLY CHANGES & EXIT"
         self.nextrely = current_position
         self.done = self.add_widget_intelligent(
             npyscreen.ButtonPress,
@@ -192,16 +210,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
     ############# diffusers tab ##########
     def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
         """Add widgets responsible for selecting diffusers models"""
-        widgets: Dict[str, npyscreen.widget] = {}
+        widgets = {}
+        models = self.all_models
+        starters = self.starter_models
+        starter_model_labels = self.model_labels
 
-        all_models = self.all_models  # master dict of all models, indexed by key
-        model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
-        model_labels = [self.model_labels[x] for x in model_list]
+        self.installed_models = sorted([x for x in starters if models[x].installed])
 
         widgets.update(
             label1=self.add_widget_intelligent(
                 CenteredTitleText,
-                name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
+                name="Select from a starter set of Stable Diffusion models from HuggingFace.",
                 editable=False,
                 labelColor="CAUTION",
             )
@@ -211,24 +230,23 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         # if user has already installed some initial models, then don't patronize them
         # by showing more recommendations
         show_recommended = len(self.installed_models) == 0
-
-        checked = [
-            model_list.index(x)
-            for x in model_list
-            if (show_recommended and all_models[x].recommended) or all_models[x].installed
-        ]
+        keys = [x for x in models.keys() if x in starters]
         widgets.update(
             models_selected=self.add_widget_intelligent(
                 MultiSelectColumns,
                 columns=1,
                 name="Install Starter Models",
-                values=model_labels,
-                value=checked,
-                max_height=len(model_list) + 1,
+                values=[starter_model_labels[x] for x in keys],
+                value=[
+                    keys.index(x)
+                    for x in keys
+                    if (show_recommended and models[x].recommended) or (x in self.installed_models)
+                ],
+                max_height=len(starters) + 1,
                 relx=4,
                 scroll_exit=True,
             ),
-            models=model_list,
+            models=keys,
         )
 
         self.nextrely += 1
@@ -239,18 +257,14 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self,
         model_type: ModelType,
         window_width: int = 120,
-        install_prompt: Optional[str] = None,
-        exclude: Optional[Set[str]] = None,
+        install_prompt: str = None,
+        exclude: set = None,
     ) -> dict[str, npyscreen.widget]:
         """Generic code to create model selection widgets"""
         if exclude is None:
             exclude = set()
-        widgets: Dict[str, npyscreen.widget] = {}
-        all_models = self.all_models
-        model_list = sorted(
-            [x for x in all_models if all_models[x].type == model_type and x not in exclude],
-            key=lambda x: all_models[x].name or "",
-        )
+        widgets = {}
+        model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and x not in exclude]
         model_labels = [self.model_labels[x] for x in model_list]
 
         show_recommended = len(self.installed_models) == 0
@@ -286,7 +300,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                     value=[
                         model_list.index(x)
                         for x in model_list
-                        if (show_recommended and all_models[x].recommended) or all_models[x].installed
+                        if (show_recommended and self.all_models[x].recommended) or self.all_models[x].installed
                     ],
                     max_height=len(model_list) // columns + 1,
                     relx=4,
@@ -310,7 +324,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             download_ids=self.add_widget_intelligent(
                 TextBox,
                 name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
-                max_height=6,
+                max_height=4,
                 scroll_exit=True,
                 editable=True,
             )
@@ -335,13 +349,13 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         return widgets
 
-    def resize(self) -> None:
+    def resize(self):
         super().resize()
         if s := self.starter_pipelines.get("models_selected"):
-            if model_list := self.starter_pipelines.get("models"):
-                s.values = [self.model_labels[x] for x in model_list]
+            keys = [x for x in self.all_models.keys() if x in self.starter_models]
+            s.values = [self.model_labels[x] for x in keys]
 
-    def _toggle_tables(self, value: List[int]) -> None:
+    def _toggle_tables(self, value=None):
         selected_tab = value[0]
         widgets = [
             self.starter_pipelines,
@@ -371,18 +385,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.display()
 
     def _get_model_labels(self) -> dict[str, str]:
-        """Return a list of trimmed labels for all models."""
         window_width, window_height = get_terminal_size()
         checkbox_width = 4
         spacing_width = 2
-        result = {}
 
         models = self.all_models
-        label_width = max([len(models[x].name or "") for x in self.starter_models])
+        label_width = max([len(models[x].name) for x in models])
         description_width = window_width - label_width - checkbox_width - spacing_width
 
-        for key in self.all_models:
-            description = models[key].description
+        result = {}
+        for x in models.keys():
+            description = models[x].description
             description = (
                 description[0 : description_width - 3] + "..."
                 if description and len(description) > description_width
@@ -390,8 +403,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 if description
                 else ""
             )
-            result[key] = f"%-{label_width}s %s" % (models[key].name, description)
-
+            result[x] = f"%-{label_width}s %s" % (models[x].name, description)
         return result
 
     def _get_columns(self) -> int:
@@ -401,40 +413,50 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
     def confirm_deletions(self, selections: InstallSelections) -> bool:
         remove_models = selections.remove_models
-        if remove_models:
-            model_names = [self.all_models[x].name or "" for x in remove_models]
-            mods = "\n".join(model_names)
-            is_ok = npyscreen.notify_ok_cancel(
+        if len(remove_models) > 0:
+            mods = "\n".join([ModelManager.parse_key(x)[0] for x in remove_models])
+            return npyscreen.notify_ok_cancel(
                 f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
             )
-            assert isinstance(is_ok, bool)  # npyscreen doesn't have return type annotations
-            return is_ok
         else:
             return True
 
-    @property
-    def all_models(self) -> Dict[str, UnifiedModelInfo]:
-        # npyscreen doesn't having typing hints
-        return self.parentApp.install_helper.all_models  # type: ignore
+    def on_execute(self):
+        self.marshall_arguments()
+        app = self.parentApp
+        if not self.confirm_deletions(app.install_selections):
+            return
 
-    @property
-    def starter_models(self) -> List[str]:
-        return self.parentApp.install_helper._starter_models  # type: ignore
+        self.monitor.entry_widget.buffer(["Processing..."], scroll_end=True)
+        self.ok_button.hidden = True
+        self.display()
 
-    @property
-    def installed_models(self) -> List[str]:
-        return self.parentApp.install_helper._installed_models  # type: ignore
+        # TO DO: Spawn a worker thread, not a subprocess
+        parent_conn, child_conn = Pipe()
+        p = Process(
+            target=process_and_execute,
+            kwargs={
+                "opt": app.program_opts,
+                "selections": app.install_selections,
+                "conn_out": child_conn,
+            },
+        )
+        p.start()
+        child_conn.close()
+        self.subprocess_connection = parent_conn
+        self.subprocess = p
+        app.install_selections = InstallSelections()
 
-    def on_back(self) -> None:
+    def on_back(self):
         self.parentApp.switchFormPrevious()
         self.editing = False
 
-    def on_cancel(self) -> None:
+    def on_cancel(self):
         self.parentApp.setNextForm(None)
         self.parentApp.user_cancelled = True
         self.editing = False
 
-    def on_done(self) -> None:
+    def on_done(self):
         self.marshall_arguments()
         if not self.confirm_deletions(self.parentApp.install_selections):
             return
@@ -442,7 +464,77 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.parentApp.user_cancelled = False
         self.editing = False
 
-    def marshall_arguments(self) -> None:
+    ########## This routine monitors the child process that is performing model installation and removal #####
+    def while_waiting(self):
+        """Called during idle periods. Main task is to update the Log Messages box with messages
+        from the child process that does the actual installation/removal"""
+        c = self.subprocess_connection
+        if not c:
+            return
+
+        monitor_widget = self.monitor.entry_widget
+        while c.poll():
+            try:
+                data = c.recv_bytes().decode("utf-8")
+                data.strip("\n")
+
+                # processing child is requesting user input to select the
+                # right configuration file
+                if data.startswith("*need v2 config"):
+                    _, model_path, *_ = data.split(":", 2)
+                    self._return_v2_config(model_path)
+
+                # processing child is done
+                elif data == "*done*":
+                    self._close_subprocess_and_regenerate_form()
+                    break
+
+                # update the log message box
+                else:
+                    data = make_printable(data)
+                    data = data.replace("[A", "")
+                    monitor_widget.buffer(
+                        textwrap.wrap(
+                            data,
+                            width=monitor_widget.width,
+                            subsequent_indent="   ",
+                        ),
+                        scroll_end=True,
+                    )
+                    self.display()
+            except (EOFError, OSError):
+                self.subprocess_connection = None
+
+    def _return_v2_config(self, model_path: str):
+        c = self.subprocess_connection
+        model_name = Path(model_path).name
+        message = select_stable_diffusion_config_file(model_name=model_name)
+        c.send_bytes(message.encode("utf-8"))
+
+    def _close_subprocess_and_regenerate_form(self):
+        app = self.parentApp
+        self.subprocess_connection.close()
+        self.subprocess_connection = None
+        self.monitor.entry_widget.buffer(["** Action Complete **"])
+        self.display()
+
+        # rebuild the form, saving and restoring some of the fields that need to be preserved.
+        saved_messages = self.monitor.entry_widget.values
+
+        app.main_form = app.addForm(
+            "MAIN",
+            addModelsForm,
+            name="Install Stable Diffusion Models",
+            multipage=self.multipage,
+        )
+        app.switchForm("MAIN")
+
+        app.main_form.monitor.entry_widget.values = saved_messages
+        app.main_form.monitor.entry_widget.buffer([""], scroll_end=True)
+        # app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
+        # app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
+
+    def marshall_arguments(self):
         """
         Assemble arguments and store as attributes of the application:
         .starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
@@ -472,24 +564,46 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             models_to_install = [x for x in selected if not self.all_models[x].installed]
             models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
             selections.remove_models.extend(models_to_remove)
-            selections.install_models.extend([all_models[x] for x in models_to_install])
+            selections.install_models.extend(
+                all_models[x].path or all_models[x].repo_id
+                for x in models_to_install
+                if all_models[x].path or all_models[x].repo_id
+            )
 
         # models located in the 'download_ids" section
         for section in ui_sections:
             if downloads := section.get("download_ids"):
-                models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
-                selections.install_models.extend(models)
+                selections.install_models.extend(downloads.value.split())
+
+        # NOT NEEDED - DONE IN BACKEND NOW
+        # # special case for the ipadapter_models. If any of the adapters are
+        # # chosen, then we add the corresponding encoder(s) to the install list.
+        # section = self.ipadapter_models
+        # if section.get("models_selected"):
+        #     selected_adapters = [
+        #         self.all_models[section["models"][x]].name for x in section.get("models_selected").value
+        #     ]
+        #     encoders = []
+        #     if any(["sdxl" in x for x in selected_adapters]):
+        #         encoders.append("ip_adapter_sdxl_image_encoder")
+        #     if any(["sd15" in x for x in selected_adapters]):
+        #         encoders.append("ip_adapter_sd_image_encoder")
+        #     for encoder in encoders:
+        #         key = f"any/clip_vision/{encoder}"
+        #         repo_id = f"InvokeAI/{encoder}"
+        #         if key not in self.all_models:
+        #             selections.install_models.append(repo_id)
 
 
-class AddModelApplication(npyscreen.NPSAppManaged):  # type: ignore
-    def __init__(self, opt: Namespace, install_helper: InstallHelper):
+class AddModelApplication(npyscreen.NPSAppManaged):
+    def __init__(self, opt):
         super().__init__()
         self.program_opts = opt
         self.user_cancelled = False
+        # self.autoload_pending = True
         self.install_selections = InstallSelections()
-        self.install_helper = install_helper
 
-    def onStart(self) -> None:
+    def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
         self.main_form = self.addForm(
             "MAIN",
@@ -499,62 +613,138 @@ class AddModelApplication(npyscreen.NPSAppManaged):  # type: ignore
         )
 
 
-def list_models(installer: ModelInstallServiceBase, model_type: ModelType):
-    """Print out all models of type model_type."""
-    models = installer.record_store.search_by_attr(model_type=model_type)
-    print(f"Installed models of type `{model_type}`:")
-    for model in models:
-        path = (config.models_path / model.path).resolve()
-        print(f"{model.name:40}{model.base.value:5}{model.type.value:8}{model.format.value:12}{path}")
+class StderrToMessage:
+    def __init__(self, connection: Connection):
+        self.connection = connection
+
+    def write(self, data: str):
+        self.connection.send_bytes(data.encode("utf-8"))
+
+    def flush(self):
+        pass
 
 
 # --------------------------------------------------------
-def select_and_download_models(opt: Namespace) -> None:
-    """Prompt user for install/delete selections and execute."""
+def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None) -> SchedulerPredictionType:
+    if tui_conn:
+        logger.debug("Waiting for user response...")
+        return _ask_user_for_pt_tui(model_path, tui_conn)
+    else:
+        return _ask_user_for_pt_cmdline(model_path)
+
+
+def _ask_user_for_pt_cmdline(model_path: Path) -> Optional[SchedulerPredictionType]:
+    choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
+    print(
+        f"""
+Please select the scheduler prediction type of the checkpoint named {model_path.name}:
+[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
+[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
+[3] Accept the best guess;  you can fix it in the Web UI later
+"""
+    )
+    choice = None
+    ok = False
+    while not ok:
+        try:
+            choice = input("select [3]> ").strip()
+            if not choice:
+                return None
+            choice = choices[int(choice) - 1]
+            ok = True
+        except (ValueError, IndexError):
+            print(f"{choice} is not a valid choice")
+        except EOFError:
+            return
+    return choice
+
+
+def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
+    tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
+    # note that we don't do any status checking here
+    response = tui_conn.recv_bytes().decode("utf-8")
+    if response is None:
+        return None
+    elif response == "epsilon":
+        return SchedulerPredictionType.epsilon
+    elif response == "v":
+        return SchedulerPredictionType.VPrediction
+    elif response == "guess":
+        return None
+    else:
+        return None
+
+
+# --------------------------------------------------------
+def process_and_execute(
+    opt: Namespace,
+    selections: InstallSelections,
+    conn_out: Connection = None,
+):
+    # need to reinitialize config in subprocess
+    config = InvokeAIAppConfig.get_config()
+    args = ["--root", opt.root] if opt.root else []
+    config.parse_args(args)
+
+    # set up so that stderr is sent to conn_out
+    if conn_out:
+        translator = StderrToMessage(conn_out)
+        sys.stderr = translator
+        sys.stdout = translator
+        logger = InvokeAILogger.get_logger()
+        logger.handlers.clear()
+        logger.addHandler(logging.StreamHandler(translator))
+
+    installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x, conn_out))
+    installer.install(selections)
+
+    if conn_out:
+        conn_out.send_bytes("*done*".encode("utf-8"))
+        conn_out.close()
+
+
+# --------------------------------------------------------
+def select_and_download_models(opt: Namespace):
     precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
-    # unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
-    config.precision = precision  # type: ignore
-    install_helper = InstallHelper(config, logger)
-    installer = install_helper.installer
-
+    config.precision = precision
+    installer = ModelInstall(config, prediction_type_helper=ask_user_for_prediction_type)
     if opt.list_models:
-        list_models(installer, opt.list_models)
-
+        installer.list_models(opt.list_models)
     elif opt.add or opt.delete:
-        selections = InstallSelections(
-            install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
-        )
-        install_helper.add_or_delete(selections)
-
+        selections = InstallSelections(install_models=opt.add or [], remove_models=opt.delete or [])
+        installer.install(selections)
     elif opt.default_only:
-        default_model = install_helper.default_model()
-        assert default_model is not None
-        selections = InstallSelections(install_models=[default_model])
-        install_helper.add_or_delete(selections)
-
+        selections = InstallSelections(install_models=installer.default_model())
+        installer.install(selections)
     elif opt.yes_to_all:
-        selections = InstallSelections(install_models=install_helper.recommended_models())
-        install_helper.add_or_delete(selections)
+        selections = InstallSelections(install_models=installer.recommended_models())
+        installer.install(selections)
 
     # this is where the TUI is called
     else:
+        # needed to support the probe() method running under a subprocess
+        torch.multiprocessing.set_start_method("spawn")
+
         if not set_min_terminal_size(MIN_COLS, MIN_LINES):
             raise WindowTooSmallException(
                 "Could not increase terminal size. Try running again with a larger window or smaller font size."
             )
 
-        installApp = AddModelApplication(opt, install_helper)
+        installApp = AddModelApplication(opt)
         try:
             installApp.run()
-        except KeyboardInterrupt:
-            print("Aborted...")
-            sys.exit(-1)
-
-        install_helper.add_or_delete(installApp.install_selections)
+        except KeyboardInterrupt as e:
+            if hasattr(installApp, "main_form"):
+                if installApp.main_form.subprocess and installApp.main_form.subprocess.is_alive():
+                    logger.info("Terminating subprocesses")
+                    installApp.main_form.subprocess.terminate()
+                    installApp.main_form.subprocess = None
+            raise e
+        process_and_execute(opt, installApp.install_selections)
 
 
 # -------------------------------------
-def main() -> None:
+def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
     parser.add_argument(
         "--add",
@@ -564,7 +754,7 @@ def main() -> None:
     parser.add_argument(
         "--delete",
         nargs="*",
-        help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
+        help="List of names of models to idelete",
     )
     parser.add_argument(
         "--full-precision",
@@ -591,6 +781,14 @@ def main() -> None:
         choices=[x.value for x in ModelType],
         help="list installed models",
     )
+    parser.add_argument(
+        "--config_file",
+        "-c",
+        dest="config_file",
+        type=str,
+        default=None,
+        help="path to configuration file to create",
+    )
     parser.add_argument(
         "--root_dir",
         dest="root",
diff --git a/invokeai/frontend/install/widgets.py b/invokeai/frontend/install/widgets.py
index 5905ae29da..4dbc6349a0 100644
--- a/invokeai/frontend/install/widgets.py
+++ b/invokeai/frontend/install/widgets.py
@@ -267,6 +267,17 @@ class SingleSelectWithChanged(npyscreen.SelectOne):
             self.on_changed(self.value)
 
 
+class CheckboxWithChanged(npyscreen.Checkbox):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.on_changed = None
+
+    def whenToggled(self):
+        super().whenToggled()
+        if self.on_changed:
+            self.on_changed(self.value)
+
+
 class SingleSelectColumnsSimple(SelectColumnBase, SingleSelectWithChanged):
     """Row of radio buttons. Spacebar to select."""
 
diff --git a/invokeai/frontend/merge/merge_diffusers2.py b/invokeai/frontend/merge/merge_diffusers.py.OLD
similarity index 100%
rename from invokeai/frontend/merge/merge_diffusers2.py
rename to invokeai/frontend/merge/merge_diffusers.py.OLD
diff --git a/pyproject.toml b/pyproject.toml
index 8b28375e29..2958e3629a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -136,8 +136,7 @@ dependencies = [
 
 # full commands
 "invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
-"invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
-"invokeai-merge2" = "invokeai.frontend.merge.merge_diffusers2:main"
+"invokeai-merge" = "invokeai.frontend.merge.merge_diffusers:main"
 "invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
 "invokeai-model-install" = "invokeai.frontend.install.model_install:main"
 "invokeai-model-install2" = "invokeai.frontend.install.model_install2:main"              # will eventually be renamed to invokeai-model-install
diff --git a/tests/test_model_manager.py b/tests/test_model_manager.py
deleted file mode 100644
index 3e48c7ed6f..0000000000
--- a/tests/test_model_manager.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from pathlib import Path
-
-import pytest
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend import BaseModelType, ModelManager, ModelType, SubModelType
-
-BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
-VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
-VAE_NULL_OVERRIDE_MODEL_NAME = ("SDXL with empty VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
-
-
-@pytest.fixture
-def model_manager(datadir) -> ModelManager:
-    InvokeAIAppConfig.get_config(root=datadir)
-    return ModelManager(datadir / "configs" / "relative_sub.models.yaml")
-
-
-def test_get_model_names(model_manager: ModelManager):
-    names = model_manager.model_names()
-    assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME]
-
-
-def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path):
-    model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2])
-    top_model_path, is_override = model_manager._get_model_path(model_config)
-    expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0"
-    assert top_model_path == expected_model_path
-    assert not is_override
-
-
-def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path):
-    model_config = model_manager._get_model_config(
-        VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2]
-    )
-    vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
-    expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
-    assert vae_model_path == expected_vae_path
-    assert is_override
-
-
-def test_get_model_path_for_null_overridden_vae(model_manager: ModelManager, datadir: Path):
-    model_config = model_manager._get_model_config(
-        VAE_NULL_OVERRIDE_MODEL_NAME[1], VAE_NULL_OVERRIDE_MODEL_NAME[0], VAE_NULL_OVERRIDE_MODEL_NAME[2]
-    )
-    vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
-    assert not is_override