diff --git a/invokeai/backend/model_manager/load/model_loaders/vae.py b/invokeai/backend/model_manager/load/model_loaders/vae.py
index f8a03f5670..11f165c65a 100644
--- a/invokeai/backend/model_manager/load/model_loaders/vae.py
+++ b/invokeai/backend/model_manager/load/model_loaders/vae.py
@@ -13,6 +13,7 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.config import CheckpointConfigBase
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
 
 from .. import ModelLoaderRegistry
@@ -26,7 +27,7 @@ class VaeLoader(GenericDiffusersLoader):
     """Class to load VAE models."""
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if config.format != ModelFormat.Checkpoint:
+        if not isinstance(config, CheckpointConfigBase):
             return False
         elif (
             dest_path.exists()
@@ -38,13 +39,12 @@ class VaeLoader(GenericDiffusersLoader):
             return True
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
-        # TO DO: check whether sdxl VAE models convert.
+        # TODO(MM2): check whether sdxl VAE models convert.
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
-            raise Exception(f"Vae conversion not supported for model type: {config.base}")
+            raise Exception(f"VAE conversion not supported for model type: {config.base}")
         else:
-            config_file = (
-                "v1-inference.yaml" if config.base == BaseModelType.StableDiffusion1 else "v2-inference-v.yaml"
-            )
+            assert isinstance(config, CheckpointConfigBase)
+            config_file = config.config
 
         if model_path.suffix == ".safetensors":
             checkpoint = safetensors_load_file(model_path, device="cpu")
@@ -55,7 +55,7 @@ class VaeLoader(GenericDiffusersLoader):
         if "state_dict" in checkpoint:
             checkpoint = checkpoint["state_dict"]
 
-        ckpt_config = OmegaConf.load(self._app_config.legacy_conf_path / config_file)
+        ckpt_config = OmegaConf.load(self._app_config.root_path / config_file)
         assert isinstance(ckpt_config, DictConfig)
 
         vae_model = convert_ldm_vae_to_diffusers(
diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index a7250f33d1..608d9e3c59 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/invokeai/backend/model_manager/probe.py
@@ -137,7 +137,7 @@ class ModelProbe(object):
         format_type = ModelFormat.Diffusers if model_path.is_dir() else ModelFormat.Checkpoint
         model_info = None
         model_type = None
-        if format_type == "diffusers":
+        if format_type is ModelFormat.Diffusers:
             model_type = cls.get_model_type_from_folder(model_path)
         else:
             model_type = cls.get_model_type_from_checkpoint(model_path)
@@ -168,7 +168,7 @@ class ModelProbe(object):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models
-        if fields["type"] in [ModelType.Main, ModelType.ControlNet] and fields["format"] == ModelFormat.Checkpoint:
+        if fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.Vae] and fields["format"] == ModelFormat.Checkpoint:
             fields["config"] = cls._get_checkpoint_config_path(
                 model_path,
                 model_type=fields["type"],
@@ -285,13 +285,21 @@ class ModelProbe(object):
         if possible_conf.exists():
             return possible_conf.absolute()
 
-        if model_type == ModelType.Main:
+        if model_type is ModelType.Main:
             config_file = LEGACY_CONFIGS[base_type][variant_type]
             if isinstance(config_file, dict):  # need another tier for sd-2.x models
                 config_file = config_file[prediction_type]
-        elif model_type == ModelType.ControlNet:
+        elif model_type is ModelType.ControlNet:
             config_file = (
-                "../controlnet/cldm_v15.yaml" if base_type == BaseModelType("sd-1") else "../controlnet/cldm_v21.yaml"
+                "../controlnet/cldm_v15.yaml"
+                if base_type is BaseModelType.StableDiffusion1
+                else "../controlnet/cldm_v21.yaml"
+            )
+        elif model_type is ModelType.Vae:
+            config_file = (
+                "../stable-diffusion/v1-inference.yaml"
+                if base_type is BaseModelType.StableDiffusion1
+                else "../stable-diffusion/v2-inference.yaml"
             )
         else:
             raise InvalidModelConfigException(