diff --git a/flux_train_control_net.py b/flux_train_control_net.py
index 744c265b5..c9d38afbb 100644
--- a/flux_train_control_net.py
+++ b/flux_train_control_net.py
@@ -30,6 +30,7 @@
 init_ipex()
 
 from accelerate.utils import set_seed
+from diffusers.utils.torch_utils import is_compiled_module
 
 import library.train_util as train_util
 from library import (
@@ -173,6 +174,11 @@ def train(args):
     logger.info("prepare accelerator")
     accelerator = train_util.prepare_accelerator(args)
 
+    def unwrap_model(model):
+        model = accelerator.unwrap_model(model)
+        model = model._orig_mod if is_compiled_module(model) else model
+        return model
+
     # mixed precisionに対応した型を用意しておき適宜castする
     weight_dtype, save_dtype = train_util.prepare_dtype(args)
 
@@ -748,7 +754,7 @@ def grad_hook(parameter: torch.Tensor):
                             epoch,
                             num_train_epochs,
                             global_step,
-                            accelerator.unwrap_model(controlnet),
+                            unwrap_model(controlnet),
                         )
                 optimizer_train_fn()
 
@@ -784,7 +790,7 @@ def grad_hook(parameter: torch.Tensor):
                     epoch,
                     num_train_epochs,
                     global_step,
-                    accelerator.unwrap_model(controlnet),
+                    unwrap_model(controlnet),
                 )
 
         flux_train_utils.sample_images(
@@ -794,7 +800,7 @@ def grad_hook(parameter: torch.Tensor):
 
     is_main_process = accelerator.is_main_process
     # if is_main_process:
-    controlnet = accelerator.unwrap_model(controlnet)
+    controlnet = unwrap_model(controlnet)
 
     accelerator.end_training()
     optimizer_eval_fn()
diff --git a/library/train_util.py b/library/train_util.py
index b78732ca1..f32fef06f 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -5092,6 +5092,9 @@ def get_optimizer(args, trainable_params, model=None) -> tuple[str, str, object]
         elif optimizer_type == "SGDScheduleFree".lower():
             optimizer_class = sf.SGDScheduleFree
             logger.info(f"use SGDScheduleFree optimizer | {optimizer_kwargs}")
+        elif optimizer_type == "RAdamScheduleFree".lower():
+            optimizer_class = sf.RAdamScheduleFree
+            logger.info(f"use RAdamScheduleFree optimizer | {optimizer_kwargs}")
         else:
             optimizer_class = None