diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py
index c3dfc923f0d6..51db6bb99f22 100644
--- a/examples/dreambooth/train_dreambooth_lora_sdxl.py
+++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py
@@ -62,6 +62,7 @@
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr
 from diffusers.utils import (
+    _collate_lora_metadata,
     check_min_version,
     convert_all_state_dict_to_peft,
     convert_state_dict_to_diffusers,
@@ -659,6 +660,12 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    
+    parser.add_argument(
+        "--lora_alpha",
+        type=int,
+        default=4,
+        help="LoRA alpha to be used for additional scaling.",
 
     parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
 
@@ -1202,10 +1209,10 @@ def main(args):
             text_encoder_one.gradient_checkpointing_enable()
             text_encoder_two.gradient_checkpointing_enable()
 
-    def get_lora_config(rank, dropout, use_dora, target_modules):
+    def get_lora_config(rank, lora_alpha, dropout, use_dora, target_modules):
         base_config = {
             "r": rank,
-            "lora_alpha": rank,
+            "lora_alpha":lora_alpha,
             "lora_dropout": dropout,
             "init_lora_weights": "gaussian",
             "target_modules": target_modules,
@@ -1224,6 +1231,7 @@ def get_lora_config(rank, dropout, use_dora, target_modules):
     unet_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
     unet_lora_config = get_lora_config(
         rank=args.rank,
+        lora_alpha=args.lora_alpha,
         dropout=args.lora_dropout,
         use_dora=args.use_dora,
         target_modules=unet_target_modules,
@@ -1236,6 +1244,7 @@ def get_lora_config(rank, dropout, use_dora, target_modules):
         text_target_modules = ["q_proj", "k_proj", "v_proj", "out_proj"]
         text_lora_config = get_lora_config(
             rank=args.rank,
+            lora_alpha=args.lora_alpha,
             dropout=args.lora_dropout,
             use_dora=args.use_dora,
             target_modules=text_target_modules,
@@ -1256,10 +1265,12 @@ def save_model_hook(models, weights, output_dir):
             unet_lora_layers_to_save = None
             text_encoder_one_lora_layers_to_save = None
             text_encoder_two_lora_layers_to_save = None
+            modules_to_save = {}
 
             for model in models:
                 if isinstance(model, type(unwrap_model(unet))):
                     unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))
+                    modules_to_save["transformer"] = model
                 elif isinstance(model, type(unwrap_model(text_encoder_one))):
                     text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
                         get_peft_model_state_dict(model)
@@ -1279,6 +1290,7 @@ def save_model_hook(models, weights, output_dir):
                 unet_lora_layers=unet_lora_layers_to_save,
                 text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
                 text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
+                **_collate_lora_metadata(modules_to_save),
             )
 
     def load_model_hook(models, input_dir):
@@ -1945,6 +1957,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
     # Save the lora layers
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
+        modules_to_save = {}
         unet = unwrap_model(unet)
         unet = unet.to(torch.float32)
         unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))
@@ -1967,6 +1980,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
             unet_lora_layers=unet_lora_layers,
             text_encoder_lora_layers=text_encoder_lora_layers,
             text_encoder_2_lora_layers=text_encoder_2_lora_layers,
+            **_collate_lora_metadata(modules_to_save),
         )
         if args.output_kohya_format:
             lora_state_dict = load_file(f"{args.output_dir}/pytorch_lora_weights.safetensors")