diff --git a/api/onnx_web/convert/diffusion_original.py b/api/onnx_web/convert/diffusion_original.py
index 7c7dcf3b..9dd80a51 100644
--- a/api/onnx_web/convert/diffusion_original.py
+++ b/api/onnx_web/convert/diffusion_original.py
@@ -55,7 +55,7 @@ from transformers import (
 )
 
 from .diffusion_stable import convert_diffusion_stable
-from .utils import ConversionContext, ModelDict, load_yaml, sanitize_name
+from .utils import ConversionContext, ModelDict, load_tensor, load_yaml, sanitize_name
 
 logger = getLogger(__name__)
 
@@ -634,13 +634,13 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     return new_checkpoint, has_ema
 
 
-def convert_ldm_vae_checkpoint(checkpoint, config, use_key=True):
+def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
     # extract state dict for VAE
     vae_state_dict = {}
     vae_key = "first_stage_model."
     keys = list(checkpoint.keys())
     for key in keys:
-        if use_key:
+        if first_stage:
             if key.startswith(vae_key):
                 vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
         else:
@@ -1190,24 +1190,11 @@ def extract_checkpoint(
     original_config_file = None
 
     try:
-        checkpoint = None
         map_location = torch.device("cpu")
 
         # Try to determine if v1 or v2 model if we have a ckpt
         logger.info("loading model from checkpoint")
-        _, extension = os.path.splitext(checkpoint_file)
-        if extension.lower() == ".safetensors":
-            os.environ["SAFETENSORS_FAST_GPU"] = "1"
-            try:
-                logger.debug("loading safetensors")
-                checkpoint = safetensors.torch.load_file(checkpoint_file, device="cpu")
-            except Exception as e:
-                logger.warn("Failed to load as safetensors file, falling back to torch...", e)
-                checkpoint = torch.jit.load(checkpoint_file)
-        else:
-            logger.debug("loading ckpt")
-            checkpoint = torch.load(checkpoint_file, map_location=map_location)
-            checkpoint = checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
+        checkpoint = load_tensor(checkpoint_file, map_location=map_location)
 
         rev_keys = ["db_global_step", "global_step"]
         epoch_keys = ["db_epoch", "epoch"]
@@ -1315,8 +1302,8 @@ def extract_checkpoint(
             converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
         else:
             vae_file = os.path.join(ctx.model_path, vae_file)
-            vae_checkpoint = safetensors.torch.load_file(vae_file, device="cpu")
-            converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_checkpoint, vae_config, use_key=False)
+            vae_checkpoint = load_tensor(vae_file, map_location=map_location)
+            converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_checkpoint, vae_config, first_stage=False)
 
         vae = AutoencoderKL(**vae_config)
         vae.load_state_dict(converted_vae_checkpoint)
diff --git a/api/onnx_web/convert/utils.py b/api/onnx_web/convert/utils.py
index 1062737a..a150b1b6 100644
--- a/api/onnx_web/convert/utils.py
+++ b/api/onnx_web/convert/utils.py
@@ -6,6 +6,7 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple, Union
 
 import requests
+import safetensors
 import torch
 from tqdm.auto import tqdm
 from yaml import safe_load
@@ -199,3 +200,24 @@ def remove_prefix(name, prefix):
         return name[len(prefix) :]
 
     return name
+
+
+def load_tensor(name: str, map_location=None):
+    logger.info("loading model from checkpoint")
+    _, extension = path.splitext(name)
+    if extension.lower() == ".safetensors":
+        environ["SAFETENSORS_FAST_GPU"] = "1"
+        try:
+            logger.debug("loading safetensors")
+            checkpoint = safetensors.torch.load_file(name, device="cpu")
+        except Exception as e:
+            logger.warning(
+                "failed to load as safetensors file, falling back to torch", e
+            )
+            checkpoint = torch.jit.load(name)
+    else:
+        logger.debug("loading ckpt")
+        checkpoint = torch.load(name, map_location=map_location)
+        checkpoint = (
+            checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
+        )