diff --git a/fine_tune.py b/fine_tune.py
index a3588c37..96aa362b 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -149,7 +149,7 @@ def train(args):
 
   # 学習に必要なクラスを準備する
   print("prepare optimizer, data loader etc.")
-  optimizer_name, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
+  _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
 
   # dataloaderを準備する
   # DataLoaderのプロセス数：0はメインプロセスになる
@@ -163,8 +163,10 @@ def train(args):
     print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+  lr_scheduler = train_util.get_scheduler_fix(
+      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+      num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+      num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -284,8 +286,11 @@ def train(args):
       current_loss = loss.detach().item()        # 平均なのでbatch sizeは関係ないはず
       if args.logging_dir is not None:
         logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
+        if args.optimizer_type == "DAdaptation".lower(): # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
         accelerator.log(logs, step=global_step)
 
+      # TODO moving averageにする
       loss_total += current_loss
       avr_loss = loss_total / (step+1)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
@@ -295,7 +300,7 @@ def train(args):
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": loss_total / len(train_dataloader)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
diff --git a/library/train_util.py b/library/train_util.py
index 1a28c39a..329b27fc 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -5,6 +5,7 @@ import json
 import shutil
 import time
 from typing import Dict, List, NamedTuple, Tuple
+from typing import Optional, Union
 from accelerate import Accelerator
 from torch.autograd.function import Function
 import glob
@@ -17,9 +18,11 @@ from io import BytesIO
 
 from tqdm import tqdm
 import torch
+from torch.optim import Optimizer
 from torchvision import transforms
 from transformers import CLIPTokenizer
 import diffusers
+from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
 from diffusers import DDPMScheduler, StableDiffusionPipeline
 import albumentations as albu
 import numpy as np
@@ -1368,12 +1371,18 @@ def add_sd_models_arguments(parser: argparse.ArgumentParser):
 
 def add_optimizer_arguments(parser: argparse.ArgumentParser):
   parser.add_argument("--optimizer_type", type=str, default="AdamW",
-                      help="Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit")
+                      help="Optimizer to use / オプティマイザの種類: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation")
+  parser.add_argument("--use_8bit_adam", action="store_true",
+                      help="use 8bit AdamW 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("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率")
   parser.add_argument("--optimizer_momentum", type=float, default=0.9,
-                      help="Momentum value for optimizers")
-  parser.add_argument("--optimizer_weightdecay", type=float, default=0.01,
+                      help="Momentum value for optimizers for SGD optimizers")
+  parser.add_argument("--optimizer_weight_decay", type=float, default=0.01,
                       help="Weight decay for optimizers")
   parser.add_argument("--optimizer_beta1", type=float, default=0.9,
                       help="beta1 parameter for Adam optimizers")
@@ -1407,12 +1416,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
   parser.add_argument("--train_batch_size", type=int, default=1, help="batch size for training / 学習時のバッチサイズ")
   parser.add_argument("--max_token_length", type=int, default=None, choices=[None, 150, 225],
                       help="max token length of text encoder (default for 75, 150 or 225) / text encoderのトークンの最大長（未指定で75、150または225が指定可）")
-  # parser.add_argument("--use_8bit_adam", action="store_true",
-  #                     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",
@@ -1520,14 +1523,19 @@ def add_sd_saving_arguments(parser: argparse.ArgumentParser):
 
 # region utils
 
-# "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, dadaption"
 
 def get_optimizer(args, trainable_params):
-  # Prepare optimizer/学習に必要なクラスを準備する
-  optimizer_type = args.optimizer_type.lower()
+  # "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, dadaptation"
+
+  optimizer_type = args.optimizer_type
+  if args.use_8bit_adam:
+    optimizer_type = "AdamW8bit"
+  elif args.use_lion_optimizer:
+    optimizer_type = "Lion"
+  optimizer_type = optimizer_type.lower()
 
   betas = (args.optimizer_beta1, args.optimizer_beta2)
-  weight_decay = args.optimizer_weightdecay
+  weight_decay = args.optimizer_weight_decay
   momentum = args.optimizer_momentum
   lr = args.learning_rate
 
@@ -1563,17 +1571,18 @@ def get_optimizer(args, trainable_params):
     optimizer_class = torch.optim.SGD
     optimizer = optimizer_class(trainable_params, lr=lr, momentum=momentum, weight_decay=weight_decay, nesterov=True)
 
-  elif optimizer_type == "dadaptation".lower():
+  elif optimizer_type == "DAdaptation".lower():
     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(f"use dadaptation optimizer")
+    print(f"use D-Adaptation Adam optimizer | betas: {betas}, Weight Decay: {weight_decay}")
     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')
-    optimizer = optimizer_class(trainable_params, lr=lr)
+    if lr <= 0.1:
+      print(f'learning rate is too low. If using dadaptation, set learning rate around 1.0 / 学習率が低すぎるようです。1.0前後の値を指定してください: {lr}')
+      print('recommend option: lr=1.0 / 推奨は1.0です')
+    optimizer = optimizer_class(trainable_params, lr=lr, betas=betas, weight_decay=weight_decay)
+
   else:
     print(f"use AdamW optimizer | betas: {betas}, Weight Decay: {weight_decay}")
     optimizer_class = torch.optim.AdamW
@@ -1584,6 +1593,69 @@ def get_optimizer(args, trainable_params):
   return optimizer_name, optimizer
 
 
+# Monkeypatch newer get_scheduler() function overridng current version of diffusers.optimizer.get_scheduler
+# code is taken from https://github.com/huggingface/diffusers diffusers.optimizer, commit d87cc15977b87160c30abaace3894e802ad9e1e6
+# Which is a newer release of diffusers than currently packaged with sd-scripts
+# This code can be removed when newer diffusers version (v0.12.1 or greater) is tested and implemented to sd-scripts
+
+
+def get_scheduler_fix(
+    name: Union[str, SchedulerType],
+    optimizer: Optimizer,
+    num_warmup_steps: Optional[int] = None,
+    num_training_steps: Optional[int] = None,
+    num_cycles: int = 1,
+    power: float = 1.0,
+):
+  """
+  Unified API to get any scheduler from its name.
+  Args:
+      name (`str` or `SchedulerType`):
+          The name of the scheduler to use.
+      optimizer (`torch.optim.Optimizer`):
+          The optimizer that will be used during training.
+      num_warmup_steps (`int`, *optional*):
+          The number of warmup steps to do. This is not required by all schedulers (hence the argument being
+          optional), the function will raise an error if it's unset and the scheduler type requires it.
+      num_training_steps (`int``, *optional*):
+          The number of training steps to do. This is not required by all schedulers (hence the argument being
+          optional), the function will raise an error if it's unset and the scheduler type requires it.
+      num_cycles (`int`, *optional*):
+          The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.
+      power (`float`, *optional*, defaults to 1.0):
+          Power factor. See `POLYNOMIAL` scheduler
+      last_epoch (`int`, *optional*, defaults to -1):
+          The index of the last epoch when resuming training.
+  """
+  name = SchedulerType(name)
+  schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
+  if name == SchedulerType.CONSTANT:
+    return schedule_func(optimizer)
+
+  # All other schedulers require `num_warmup_steps`
+  if num_warmup_steps is None:
+    raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
+
+  if name == SchedulerType.CONSTANT_WITH_WARMUP:
+    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
+
+  # All other schedulers require `num_training_steps`
+  if num_training_steps is None:
+    raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
+
+  if name == SchedulerType.COSINE_WITH_RESTARTS:
+    return schedule_func(
+        optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles
+    )
+
+  if name == SchedulerType.POLYNOMIAL:
+    return schedule_func(
+        optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power
+    )
+
+  return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
+
+
 def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool):
   # backward compatibility
   if args.caption_extention is not None:
diff --git a/train_db.py b/train_db.py
index 51e588fc..268d90a1 100644
--- a/train_db.py
+++ b/train_db.py
@@ -120,7 +120,7 @@ def train(args):
   else:
     trainable_params = unet.parameters()
 
-  optimizer_name, optimizer = train_util.get_optimizer(args, trainable_params)
+  _, optimizer = train_util.get_optimizer(args, trainable_params)
 
   # dataloaderを準備する
   # DataLoaderのプロセス数：0はメインプロセスになる
@@ -136,9 +136,11 @@ def train(args):
   if args.stop_text_encoder_training is None:
     args.stop_text_encoder_training = args.max_train_steps + 1                # do not stop until end
 
-  # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps)
+  # lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
+  lr_scheduler = train_util.get_scheduler_fix(
+      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+      num_training_steps=args.max_train_steps,
+      num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -280,6 +282,8 @@ def train(args):
       current_loss = loss.detach().item()
       if args.logging_dir is not None:
         logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
+        if args.optimizer_type == "DAdaptation".lower(): # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
         accelerator.log(logs, step=global_step)
 
       if epoch == 0:
diff --git a/train_network.py b/train_network.py
index df987325..3c4fb8d9 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1,8 +1,5 @@
-from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
-from torch.optim import Optimizer
 from torch.cuda.amp import autocast
 from torch.nn.parallel import DistributedDataParallel as DDP
-from typing import Optional, Union
 import importlib
 import argparse
 import gc
@@ -26,6 +23,7 @@ def collate_fn(examples):
   return examples[0]
 
 
+# TODO 他のスクリプトと共通化する
 def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
   logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
@@ -37,75 +35,12 @@ 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.
+  if args.optimizer_type == "DAdaptation".lower():  # 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
 
 
-# Monkeypatch newer get_scheduler() function overridng current version of diffusers.optimizer.get_scheduler
-# code is taken from https://github.com/huggingface/diffusers diffusers.optimizer, commit d87cc15977b87160c30abaace3894e802ad9e1e6
-# Which is a newer release of diffusers than currently packaged with sd-scripts
-# This code can be removed when newer diffusers version (v0.12.1 or greater) is tested and implemented to sd-scripts
-
-
-def get_scheduler_fix(
-    name: Union[str, SchedulerType],
-    optimizer: Optimizer,
-    num_warmup_steps: Optional[int] = None,
-    num_training_steps: Optional[int] = None,
-    num_cycles: int = 1,
-    power: float = 1.0,
-):
-  """
-  Unified API to get any scheduler from its name.
-  Args:
-      name (`str` or `SchedulerType`):
-          The name of the scheduler to use.
-      optimizer (`torch.optim.Optimizer`):
-          The optimizer that will be used during training.
-      num_warmup_steps (`int`, *optional*):
-          The number of warmup steps to do. This is not required by all schedulers (hence the argument being
-          optional), the function will raise an error if it's unset and the scheduler type requires it.
-      num_training_steps (`int``, *optional*):
-          The number of training steps to do. This is not required by all schedulers (hence the argument being
-          optional), the function will raise an error if it's unset and the scheduler type requires it.
-      num_cycles (`int`, *optional*):
-          The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.
-      power (`float`, *optional*, defaults to 1.0):
-          Power factor. See `POLYNOMIAL` scheduler
-      last_epoch (`int`, *optional*, defaults to -1):
-          The index of the last epoch when resuming training.
-  """
-  name = SchedulerType(name)
-  schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
-  if name == SchedulerType.CONSTANT:
-    return schedule_func(optimizer)
-
-  # All other schedulers require `num_warmup_steps`
-  if num_warmup_steps is None:
-    raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
-
-  if name == SchedulerType.CONSTANT_WITH_WARMUP:
-    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
-
-  # All other schedulers require `num_training_steps`
-  if num_training_steps is None:
-    raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
-
-  if name == SchedulerType.COSINE_WITH_RESTARTS:
-    return schedule_func(
-        optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles
-    )
-
-  if name == SchedulerType.POLYNOMIAL:
-    return schedule_func(
-        optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power
-    )
-
-  return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
-
-
 def train(args):
   session_id = random.randint(0, 2**32)
   training_started_at = time.time()
@@ -164,7 +99,7 @@ def train(args):
   if args.lowram:
     text_encoder.to("cuda")
     unet.to("cuda")
-  
+
   # モデルに xformers とか memory efficient attention を組み込む
   train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
 
@@ -226,8 +161,7 @@ def train(args):
     print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
-  # lr_scheduler = diffusers.optimization.get_scheduler(
-  lr_scheduler = get_scheduler_fix(
+  lr_scheduler = train_util.get_scheduler_fix(
       args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
       num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
       num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 1913da7e..07dcc199 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -199,7 +199,7 @@ def train(args):
   # 学習に必要なクラスを準備する
   print("prepare optimizer, data loader etc.")
   trainable_params = text_encoder.get_input_embeddings().parameters()
-  optimizer_name, optimizer = train_util.get_optimizer(args, trainable_params)
+  _, optimizer = train_util.get_optimizer(args, trainable_params)
 
   # dataloaderを準備する
   # DataLoaderのプロセス数：0はメインプロセスになる
@@ -213,8 +213,10 @@ def train(args):
     print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+  lr_scheduler = train_util.get_scheduler_fix(
+      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+      num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+      num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # acceleratorがなんかよろしくやってくれるらしい
   text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
@@ -356,6 +358,8 @@ def train(args):
       current_loss = loss.detach().item()
       if args.logging_dir is not None:
         logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
+        if args.optimizer_type == "DAdaptation".lower(): # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
         accelerator.log(logs, step=global_step)
 
       loss_total += current_loss