diff --git a/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py b/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py
index de59cb1f0be9..7a158f5d0d2d 100644
--- a/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py
+++ b/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py
@@ -52,6 +52,7 @@
 from diffusers.training_utils import EMAModel
 from diffusers.utils import check_min_version, deprecate, is_wandb_available, load_image
 from diffusers.utils.import_utils import is_xformers_available
+from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -531,6 +532,11 @@ def main():
         else:
             raise ValueError("xformers is not available. Make sure it is installed correctly")
 
+    def unwrap_model(model):
+        model = accelerator.unwrap_model(model)
+        model = model._orig_mod if is_compiled_module(model) else model
+        return model
+
     # `accelerate` 0.16.0 will have better support for customized saving
     if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
         # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
@@ -1044,8 +1050,12 @@ def collate_fn(examples):
                 added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
 
                 model_pred = unet(
-                    concatenated_noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-                ).sample
+                    concatenated_noisy_latents,
+                    timesteps,
+                    encoder_hidden_states,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
                 loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
 
                 # Gather the losses across all processes for logging (if we use distributed training).
@@ -1115,7 +1125,7 @@ def collate_fn(examples):
                     # The models need unwrapping because for compatibility in distributed training mode.
                     pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(
                         args.pretrained_model_name_or_path,
-                        unet=accelerator.unwrap_model(unet),
+                        unet=unwrap_model(unet),
                         text_encoder=text_encoder_1,
                         text_encoder_2=text_encoder_2,
                         tokenizer=tokenizer_1,
@@ -1177,7 +1187,7 @@ def collate_fn(examples):
     # Create the pipeline using the trained modules and save it.
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        unet = accelerator.unwrap_model(unet)
+        unet = unwrap_model(unet)
         if args.use_ema:
             ema_unet.copy_to(unet.parameters())