diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py
index 04d8a6442ea3..0d7876db9509 100644
--- a/examples/dreambooth/train_dreambooth_lora_sdxl.py
+++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py
@@ -647,6 +647,15 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument(
+        "--use_dora",
+        action="store_true",
+        default=False,
+        help=(
+            "Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
+            "Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`"
+        ),
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -1147,6 +1156,7 @@ def main(args):
     # now we will add new LoRA weights to the attention layers
     unet_lora_config = LoraConfig(
         r=args.rank,
+        use_dora=args.use_dora,
         lora_alpha=args.rank,
         init_lora_weights="gaussian",
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
@@ -1158,6 +1168,7 @@ def main(args):
     if args.train_text_encoder:
         text_lora_config = LoraConfig(
             r=args.rank,
+            use_dora=args.use_dora,
             lora_alpha=args.rank,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],