diff --git a/examples/dreambooth/train_dreambooth_lora.py b/examples/dreambooth/train_dreambooth_lora.py
index 16adfe4b83fc..bfbf3603e8d0 100644
--- a/examples/dreambooth/train_dreambooth_lora.py
+++ b/examples/dreambooth/train_dreambooth_lora.py
@@ -31,7 +31,7 @@
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, model_info, upload_folder
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from PIL import Image
 from torch.utils.data import Dataset
@@ -589,16 +589,6 @@ def __getitem__(self, index):
         return example
 
 
-def model_has_vae(args):
-    config_file_name = os.path.join("vae", AutoencoderKL.config_name)
-    if os.path.isdir(args.pretrained_model_name_or_path):
-        config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)
-        return os.path.isfile(config_file_name)
-    else:
-        files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings
-        return any(file.rfilename == config_file_name for file in files_in_repo)
-
-
 def tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):
     if tokenizer_max_length is not None:
         max_length = tokenizer_max_length
@@ -753,11 +743,13 @@ def main(args):
     text_encoder = text_encoder_cls.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
-    if model_has_vae(args):
+    try:
         vae = AutoencoderKL.from_pretrained(
             args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
         )
-    else:
+    except OSError:
+        # IF does not have a VAE so let's just set it to None
+        # We don't have to error out here
         vae = None
 
     unet = UNet2DConditionModel.from_pretrained(