diff --git a/fine_tune.py b/fine_tune.py
index 13241bc6..b368d1ea 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -165,6 +165,16 @@ def train(args):
       raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです")
     print("use Lion optimizer")
     optimizer_class = lion_pytorch.Lion
+  elif args.use_dadaptation_optimizer:
+    try:
+      import dadaptation
+    except ImportError:
+      raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
+    print("use dadaptation optimizer")
+    optimizer_class = dadaptation.DAdaptAdam
+    if args.learning_rate <= 0.1:
+      print('learning rate is too low. If using dadaptaion, set learning rate around 1.0.')
+      print('recommend option: lr=1.0')
   else:
     optimizer_class = torch.optim.AdamW
 
diff --git a/library/train_util.py b/library/train_util.py
index 63868f98..2a15fa05 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -1391,6 +1391,8 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
                       help="use 8bit Adam optimizer (requires bitsandbytes) / 8bit Adamオプティマイザを使う（bitsandbytesのインストールが必要）")
   parser.add_argument("--use_lion_optimizer", action="store_true",
                       help="use Lion optimizer (requires lion-pytorch) / Lionオプティマイザを使う（ lion-pytorch のインストールが必要）")
+  parser.add_argument("--use_dadaptation_optimizer", action="store_true",
+                      help="use dadaptation optimizer (requires dadaptation) / dadaptaionオプティマイザを使う（ dadaptation のインストールが必要）")
   parser.add_argument("--mem_eff_attn", action="store_true",
                       help="use memory efficient attention for CrossAttention / CrossAttentionに省メモリ版attentionを使う")
   parser.add_argument("--xformers", action="store_true",
diff --git a/train_network.py b/train_network.py
index 1489691d..9bf07d84 100644
--- a/train_network.py
+++ b/train_network.py
@@ -37,6 +37,9 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
     logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
     logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]          # may be same to textencoder
 
+  if args.use_dadaptation_optimizer: # tracking d*lr value of unet.
+    logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]['d']*lr_scheduler.optimizers[-1].param_groups[0]['lr']
+
   return logs
 
 
@@ -223,6 +226,18 @@ def train(args):
       raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです")
     print("use Lion optimizer")
     optimizer_class = lion_pytorch.Lion
+  elif args.use_dadaptation_optimizer:
+    try:
+      import dadaptation
+    except ImportError:
+      raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
+    print("use dadaptation optimizer")
+    optimizer_class = dadaptation.DAdaptAdam
+    if args.network_dim > args.network_alpha:
+      print('network dimension is greater than network alpha. It possibly makes network blow up.')
+    if args.learning_rate <= 0.1 or args.text_encoder_lr <= 0.1 or args.unet_lr <= 0.1:
+      print('learning rate is too low. If using dadaptaion, set learning rate around 1.0.')
+      print('recommend option: lr=1.0, unet_lr=1.0, txtencoder_lr=0.5')
   else:
     optimizer_class = torch.optim.AdamW