diff --git a/api/onnx_web/convert/utils.py b/api/onnx_web/convert/utils.py
index b98640db..aaff53d2 100644
--- a/api/onnx_web/convert/utils.py
+++ b/api/onnx_web/convert/utils.py
@@ -198,35 +198,53 @@ def remove_prefix(name: str, prefix: str) -> str:
     return name
 
 
-def load_tensor(name: str, map_location=None):
+def load_torch(name: str, map_location=None) -> Optional[Dict]:
+    try:
+        logger.debug(
+            "loading tensor with Torch JIT: %s", name
+        )
+        checkpoint = torch.jit.load(name)
+    except Exception:
+        logger.exception(
+            "error loading with Torch JIT, falling back to Torch: %s", name
+        )
+        checkpoint = torch.load(name, map_location=map_location)
+
+    return checkpoint
+
+
+def load_tensor(name: str, map_location=None) -> Optional[Dict]:
+    if not path.exists(name):
+        logger.warning("tensor does not exist: %s", name)
+        return None
+
     logger.debug("loading tensor: %s", name)
     _, extension = path.splitext(name)
     extension = extension[1:].lower()
 
-    if extension == "safetensors":
+    if extension == "":
+        # if no extension was intentional, do not search for others
+        if path.exists(name):
+            logger.debug("loading anonymous tensor")
+            checkpoint = torch.load(name, map_location=map_location)
+        else:
+            logger.debug("searching for tensors with known extensions")
+            for try_extension in ["safetensors", "ckpt", "pt", "bin"]:
+                checkpoint = load_tensor(f"{name}.{try_extension}", map_location=map_location)
+                if checkpoint is not None:
+                    break
+    elif extension == "safetensors":
         environ["SAFETENSORS_FAST_GPU"] = "1"
-        try:
-            logger.debug("loading safetensors")
-            checkpoint = safetensors.torch.load_file(name, device="cpu")
-        except Exception as e:
-            try:
-                logger.warning(
-                    "failed to load as safetensors file, falling back to Torch JIT: %s", e
-                )
-                checkpoint = torch.jit.load(name)
-            except Exception as e:
-                logger.warning(
-                    "failed to load with Torch JIT, falling back to PyTorch: %s", e
-                )
-                checkpoint = torch.load(name, map_location=map_location)
-    elif extension in ["", "bin", "ckpt", "pt"]:
-        logger.debug("loading ckpt")
+        logger.debug("loading safetensors")
+        checkpoint = safetensors.torch.load_file(name, device="cpu")
+    elif extension in ["bin", "ckpt", "pt"]:
+        logger.debug("loading pickle tensor")
         checkpoint = torch.load(name, map_location=map_location)
     elif extension in ["onnx", "pt"]:
-        logger.warning("unknown tensor extension, may be ONNX model: %s", extension)
+        logger.warning("tensor has ONNX extension, falling back to PyTorch: %s", extension)
         checkpoint = torch.load(name, map_location=map_location)
     else:
-        logger.warning("unknown tensor extension: %s", extension)
+        logger.warning("unknown tensor type, falling back to PyTorch: %s", extension)
         checkpoint = torch.load(name, map_location=map_location)
 
     if "state_dict" in checkpoint:
diff --git a/api/onnx_web/diffusers/load.py b/api/onnx_web/diffusers/load.py
index c96bb31c..474d300a 100644
--- a/api/onnx_web/diffusers/load.py
+++ b/api/onnx_web/diffusers/load.py
@@ -188,7 +188,7 @@ def load_pipeline(
             inversion_names, inversion_weights = zip(*inversions)
 
             inversion_models = [
-                path.join(server.model_path, "inversion", f"{name}.ckpt")
+                path.join(server.model_path, "inversion", name)
                 for name in inversion_names
             ]
             text_encoder = load_model(path.join(model, "text_encoder", "model.onnx"))
@@ -226,7 +226,7 @@ def load_pipeline(
         if loras is not None and len(loras) > 0:
             lora_names, lora_weights = zip(*loras)
             lora_models = [
-                path.join(server.model_path, "lora", f"{name}.safetensors")
+                path.join(server.model_path, "lora", name)
                 for name in lora_names
             ]
             logger.info(