diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index d06ab5c023..e539f5c45a 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/invokeai/backend/model_manager/probe.py
@@ -1,4 +1,5 @@
 import json
+import os
 import re
 from pathlib import Path
 from typing import Any, Dict, Literal, Optional, Union
@@ -648,12 +649,13 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return ModelFormat.EmbeddingFolder
 
     def get_base_type(self) -> BaseModelType:
-        path = self.model_path / "learned_embeds.bin"
-        if not path.exists():
+        files = os.scandir(self.model_path)
+        files = [Path(f.path) for f in files if f.is_file() and f.name.endswith((".ckpt", ".pt", ".pth", ".bin", ".safetensors"))]
+        if len(files) != 1:
             raise InvalidModelConfigException(
-                f"{self.model_path.as_posix()} does not contain expected 'learned_embeds.bin' file"
+                f"Unable to determine base type for {self.model_path}: expected exactly one valid model file, found {[f.name for f in files]}."
             )
-        return TextualInversionCheckpointProbe(path).get_base_type()
+        return TextualInversionCheckpointProbe(files.pop()).get_base_type()
 
 
 class ONNXFolderProbe(PipelineFolderProbe):
@@ -702,11 +704,13 @@ class ControlNetFolderProbe(FolderProbeBase):
 class LoRAFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         model_file = None
-        for suffix in ["safetensors", "bin"]:
-            base_file = self.model_path / f"pytorch_lora_weights.{suffix}"
-            if base_file.exists():
-                model_file = base_file
-                break
+        files = os.scandir(self.model_path)
+        files = [Path(f.path) for f in files if f.is_file() and f.name.endswith((".bin", ".safetensors"))]
+        if len(files) != 1:
+            raise InvalidModelConfigException(
+                f"Unable to determine base type for {self.model_path}: expected exactly one valid model file, found {[f.name for f in files]}."
+            )
+        model_file = files.pop()
         if not model_file:
             raise InvalidModelConfigException("Unknown LoRA format encountered")
         return LoRACheckpointProbe(model_file).get_base_type()