fix relative model paths to be against config.models_path, not root

invokeai/app/services/config.py

@@ -171,7 +171,6 @@ from pydantic import BaseSettings, Field, parse_obj_as
 from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
 
 INIT_FILE = Path("invokeai.yaml")
-MODEL_CORE = Path("models/core")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
 
@@ -357,7 +356,7 @@ def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
         root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
-    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
+    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
         root = (venv.parent).resolve()
     else:
         root = Path("~/invokeai").expanduser().resolve()

invokeai/backend/install/invokeai_configure.py

@@ -181,7 +181,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
 
 
 def download_conversion_models():
-    target_dir = config.root_path / "models/core/convert"
+    target_dir = config.models_path / "core/convert"
     kwargs = dict()  # for future use
     try:
         logger.info("Downloading core tokenizers and text encoders")

invokeai/backend/install/model_install_backend.py

@@ -103,6 +103,7 @@ class ModelInstall(object):
         access_token: str = None,
     ):
         self.config = config
+        # force model manager to be a singleton
         self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
         self.prediction_helper = prediction_type_helper
@@ -273,6 +274,7 @@ class ModelInstall(object):
                 logger.error(f"Unable to download {url}. Skipping.")
             info = ModelProbe().heuristic_probe(location)
             dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
+            dest.parent.mkdir(parents=True, exist_ok=True)
             models_path = shutil.move(location, dest)
 
         # staged version will be garbage-collected at this time
@@ -346,7 +348,7 @@ class ModelInstall(object):
             if key in self.datasets:
                 description = self.datasets[key].get("description") or description
 
-        rel_path = self.relative_to_root(path)
+        rel_path = self.relative_to_root(path,self.config.models_path)
 
         attributes = dict(
             path=str(rel_path),
@@ -386,8 +388,8 @@ class ModelInstall(object):
             attributes.update(dict(config=str(legacy_conf)))
         return attributes
 
-    def relative_to_root(self, path: Path) -> Path:
+    def relative_to_root(self, path: Path, root: None) -> Path:
-        root = self.config.root_path
+        root = root or self.config.root_path
         if path.is_relative_to(root):
             return path.relative_to(root)
         else:

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -63,7 +63,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
 
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
@@ -81,7 +81,7 @@ if is_accelerate_available():
     from accelerate.utils import set_module_tensor_to_device
 
 logger = InvokeAILogger.getLogger(__name__)
-CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
+CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core/convert"
 
 
 def shave_segments(path, n_shave_prefix_segments=1):
@@ -1281,7 +1281,7 @@ def download_from_original_stable_diffusion_ckpt(
     original_config = OmegaConf.load(original_config_file)
     if (
         model_version == BaseModelType.StableDiffusion2
-        and original_config["model"]["params"]["parameterization"] == "v"
+        and original_config["model"]["params"].get("parameterization") == "v"
     ):
         prediction_type = "v_prediction"
         upcast_attention = True

invokeai/backend/model_management/model_manager.py

@@ -456,7 +456,7 @@ class ModelManager(object):
                 raise ModelNotFoundException(f"Model not found - {model_key}")
 
         model_config = self.models[model_key]
-        model_path = self.app_config.root_path / model_config.path
+        model_path = self.app_config.models_path / model_config.path
 
         if not model_path.exists():
             if model_class.save_to_config:
@@ -623,7 +623,7 @@ class ModelManager(object):
             self.cache.uncache_model(cache_id)
 
         # if model inside invoke models folder - delete files
-        model_path = self.app_config.root_path / model_cfg.path
+        model_path = self.app_config.models_path / model_cfg.path
         cache_path = self._get_model_cache_path(model_path)
         if cache_path.exists():
             rmtree(str(cache_path))
@@ -656,8 +656,8 @@ class ModelManager(object):
         """
         # relativize paths as they go in - this makes it easier to move the root directory around
         if path := model_attributes.get("path"):
-            if Path(path).is_relative_to(self.app_config.root_path):
+            if Path(path).is_relative_to(self.app_config.models_path):
-                model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
+                model_attributes["path"] = str(Path(path).relative_to(self.app_config.models_path))
 
         model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
@@ -732,7 +732,7 @@ class ModelManager(object):
                 / new_name
             )
             move(old_path, new_path)
-            model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
+            model_cfg.path = str(new_path.relative_to(self.app_config.models_path))
 
         # clean up caches
         old_model_cache = self._get_model_cache_path(old_path)
@@ -795,7 +795,7 @@ class ModelManager(object):
             info["path"] = (
                 str(new_diffusers_path)
                 if dest_directory
-                else str(new_diffusers_path.relative_to(self.app_config.root_path))
+                else str(new_diffusers_path.relative_to(self.app_config.models_path))
             )
             info.pop("config")
 
@@ -883,10 +883,17 @@ class ModelManager(object):
         new_models_found = False
 
         self.logger.info(f"Scanning {self.app_config.models_path} for new models")
-        with Chdir(self.app_config.root_path):
+        with Chdir(self.app_config.models_path):
             for model_key, model_config in list(self.models.items()):
                 model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
-                model_path = self.app_config.root_path.absolute() / model_config.path
+                
+                # Patch for relative path bug in older models.yaml - paths should not
+                # be starting with a hard-coded 'models'. This will also fix up
+                # models.yaml when committed.
+                if model_config.path.startswith('models'):
+                    model_config.path = str(Path(*Path(model_config.path).parts[1:]))
+                
+                model_path = self.app_config.models_path.absolute() / model_config.path
                 if not model_path.exists():
                     model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
                     if model_class.save_to_config:
@@ -919,8 +926,8 @@ class ModelManager(object):
                                 if model_key in self.models:
                                     raise DuplicateModelException(f"Model with key {model_key} added twice")
 
-                                if model_path.is_relative_to(self.app_config.root_path):
+                                if model_path.is_relative_to(self.app_config.models_path):
-                                    model_path = model_path.relative_to(self.app_config.root_path)
+                                    model_path = model_path.relative_to(self.app_config.models_path)
 
                                 model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
@@ -971,7 +978,7 @@ class ModelManager(object):
         # LS: hacky
         # Patch in the SD VAE from core so that it is available for use by the UI
         try:
-            self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
+            self.heuristic_import({config.models_path / "core/convert/sd-vae-ft-mse"})
         except:
             pass
 

invokeai/backend/model_management/models/stable_diffusion.py

@@ -259,7 +259,7 @@ def _convert_ckpt_and_cache(
     """
     app_config = InvokeAIAppConfig.get_config()
 
-    weights = app_config.root_path / model_config.path
+    weights = app_config.models_path / model_config.path
     config_file = app_config.root_path / model_config.config
     output_path = Path(output_path)
 

invokeai/frontend/install/model_install.py

@@ -153,7 +153,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=8,
+            max_height=15,
         )
 
         self.nextrely += 1
@@ -399,7 +399,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.ok_button.hidden = True
         self.display()
 
-        # for communication with the subprocess
+        # TO DO: Spawn a worker thread, not a subprocess
         parent_conn, child_conn = Pipe()
         p = Process(
             target=process_and_execute,
@@ -414,7 +414,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.subprocess_connection = parent_conn
         self.subprocess = p
         app.install_selections = InstallSelections()
-        # process_and_execute(app.opt, app.install_selections)
 
     def on_back(self):
         self.parentApp.switchFormPrevious()
@@ -489,8 +488,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         # rebuild the form, saving and restoring some of the fields that need to be preserved.
         saved_messages = self.monitor.entry_widget.values
-        # autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-        # autoscan = self.pipeline_models['autoscan_on_startup'].value
 
         app.main_form = app.addForm(
             "MAIN",
@@ -544,13 +541,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             if downloads := section.get("download_ids"):
                 selections.install_models.extend(downloads.value.split())
 
-        # load directory and whether to scan on startup
-        # if self.parentApp.autoload_pending:
-        #     selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-        #     self.parentApp.autoload_pending = False
-        # selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
-
-
 class AddModelApplication(npyscreen.NPSAppManaged):
     def __init__(self, opt):
         super().__init__()
@@ -639,6 +629,10 @@ def process_and_execute(
         selections: InstallSelections,
         conn_out: Connection = None,
 ):
+    # need to reinitialize config in subprocess
+    config = InvokeAIAppConfig.get_config()
+    config.parse_args()
+
     # set up so that stderr is sent to conn_out
     if conn_out:
         translator = StderrToMessage(conn_out)
@@ -685,9 +679,6 @@ def select_and_download_models(opt: Namespace):
     precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
     config.precision = precision
     helper = lambda x: ask_user_for_prediction_type(x)
-    # if do_listings(opt):
-    # pass
-
     installer = ModelInstall(config, prediction_type_helper=helper)
     if opt.list_models:
         installer.list_models(opt.list_models)
@@ -706,8 +697,6 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        # the third argument is needed in the Windows 11 environment in
-        # order to launch and resize a console window running this program
         set_min_terminal_size(MIN_COLS, MIN_LINES)
         installApp = AddModelApplication(opt)
         try: