diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py
index 443f2d25a788..4448d784c7a5 100644
--- a/examples/dreambooth/train_dreambooth_lora_sdxl.py
+++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py
@@ -402,6 +402,12 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
     )
+    parser.add_argument(
+        "--rank",
+        type=int,
+        default=4,
+        help=("The dimension of the LoRA update matrices."),
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -767,7 +773,9 @@ def main(args):
         lora_attn_processor_class = (
             LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
         )
-        module = lora_attn_processor_class(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
+        module = lora_attn_processor_class(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.rank
+        )
         unet_lora_attn_procs[name] = module
         unet_lora_parameters.extend(module.parameters())
 
@@ -777,8 +785,12 @@ def main(args):
     # So, instead, we monkey-patch the forward calls of its attention-blocks.
     if args.train_text_encoder:
         # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
-        text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder(text_encoder_one, dtype=torch.float32)
-        text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder(text_encoder_two, dtype=torch.float32)
+        text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder(
+            text_encoder_one, dtype=torch.float32, rank=args.rank
+        )
+        text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder(
+            text_encoder_two, dtype=torch.float32, rank=args.rank
+        )
 
     # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
     def save_model_hook(models, weights, output_dir):