diff --git a/README.md b/README.md
index 3425f0f..a85f8fe 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,14 @@ Then redo the installation instruction within the kohya_ss venv.
 
 ## Change history
 
+* 2023/02/09 (v20.7.1)
+    - Caption dropout is supported in ``train_db.py``, ``fine_tune.py`` and ``train_network.py``. Thanks to forestsource!
+        - ``--caption_dropout_rate`` option specifies the dropout rate for captions (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the image is trained with the empty caption. Default is 0 (no dropout).
+        - ``--caption_dropout_every_n_epochs`` option specifies how many epochs to drop captions. If ``3`` is specified, in epoch 3, 6, 9 ..., images are trained with all captions empty. Default is None (no dropout).
+        - ``--caption_tag_dropout_rate`` option specified the dropout rate for tags (comma separated tokens) (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the tag is removed from the caption. If ``--keep_tokens`` option is set, these tokens (tags) are not dropped. Default is 0 (no droupout).
+        - The bulk image downsampling script is added. Documentation is [here](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E7%94%BB%E5%83%8F%E3%83%AA%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%97%E3%83%88) (in Jpanaese). Thanks to bmaltais!
+        - Typo check is added. Thanks to shirayu!
+    - Add option to autolaunch the GUI in a browser and set the server_port. USe either `gui.ps1 --inbrowser --server_port 3456`or `gui.cmd -inbrowser -server_port 3456`
 * 2023/02/06 (v20.7.0)
     - ``--bucket_reso_steps`` and ``--bucket_no_upscale`` options are added to training scripts (fine tuning, DreamBooth, LoRA and Textual Inversion) and ``prepare_buckets_latents.py``.
     - ``--bucket_reso_steps`` takes the steps for buckets in aspect ratio bucketing. Default is 64, same as before.
diff --git a/dreambooth_gui.py b/dreambooth_gui.py
index cdcb85b..2e9cfdb 100644
--- a/dreambooth_gui.py
+++ b/dreambooth_gui.py
@@ -88,6 +88,7 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -177,6 +178,7 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -250,6 +252,7 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -416,6 +419,8 @@ def train_model(
         bucket_no_upscale=bucket_no_upscale,
         random_crop=random_crop,
         bucket_reso_steps=bucket_reso_steps,
+        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
+        caption_dropout_rate=caption_dropout_rate,
     )
 
     print(run_cmd)
@@ -627,6 +632,7 @@ def dreambooth_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
+                caption_dropout_every_n_epochs, caption_dropout_rate,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -695,6 +701,7 @@ def dreambooth_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
+        caption_dropout_every_n_epochs, caption_dropout_rate,
     ]
 
     button_open_config.click(
diff --git a/fine_tune.py b/fine_tune.py
index 6a95886..5292153 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -36,6 +36,10 @@ def train(args):
                                                args.bucket_reso_steps, args.bucket_no_upscale,
                                                args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
                                                args.dataset_repeats, args.debug_dataset)
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
@@ -226,6 +230,8 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
+
     for m in training_models:
       m.train()
 
@@ -332,7 +338,7 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, False, True)
+  train_util.add_dataset_arguments(parser, False, True, True)
   train_util.add_training_arguments(parser, False)
   train_util.add_sd_saving_arguments(parser)
 
diff --git a/finetune_gui.py b/finetune_gui.py
index 80c887f..f5aad9d 100644
--- a/finetune_gui.py
+++ b/finetune_gui.py
@@ -84,6 +84,7 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -179,6 +180,7 @@ def open_config_file(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -259,6 +261,7 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # create caption json file
     if generate_caption_database:
@@ -405,6 +408,8 @@ def train_model(
         bucket_no_upscale=bucket_no_upscale,
         random_crop=random_crop,
         bucket_reso_steps=bucket_reso_steps,
+        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
+        caption_dropout_rate=caption_dropout_rate,
     )
 
     print(run_cmd)
@@ -614,6 +619,7 @@ def finetune_tab():
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
+                caption_dropout_every_n_epochs, caption_dropout_rate,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -678,6 +684,7 @@ def finetune_tab():
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
+        caption_dropout_every_n_epochs, caption_dropout_rate,
     ]
 
     button_run.click(train_model, inputs=settings_list)
diff --git a/gui.bat b/gui.bat
index fbf5101..978abf2 100644
--- a/gui.bat
+++ b/gui.bat
@@ -1,10 +1,23 @@
 @echo off
 
-set VENV_DIR=.\venv
-set PYTHON=python
+REM Use this batch file with the following options:
+REM -inbrowser - To launch the program in the browser
+REM -server_port [port number] - To specify the server port
 
-call %VENV_DIR%\Scripts\activate.bat
+set inbrowserOption=
+set serverPortOption=
 
-%PYTHON% kohya_gui.py
+if "%1" == "-server_port" (
+  set serverPortOption=--server_port %2
+  if "%3" == "-inbrowser" (
+    set inbrowserOption=--inbrowser
+  )
+) else if "%1" == "-inbrowser" (
+  set inbrowserOption=--inbrowser
+  if "%2" == "-server_port" (
+    set serverPortOption=--server_port %3
+  )
+)
 
-pause
\ No newline at end of file
+call .\venv\Scripts\activate.bat
+python.exe kohya_gui.py %inbrowserOption% %serverPortOption%
diff --git a/gui.ps1 b/gui.ps1
index 4f799a1..e09df2e 100644
--- a/gui.ps1
+++ b/gui.ps1
@@ -1,2 +1,11 @@
+# Example command: .\gui.ps1 -server_port 8000 -inbrowser
+
+param([string]$username="", [string]$password="", [switch]$inbrowser, [int]$server_port)
 .\venv\Scripts\activate
-python.exe kohya_gui.py
\ No newline at end of file
+
+if ($server_port -le 0 -and $inbrowser -eq $false) {
+    Write-Host "Error: You must provide either the --server_port or --inbrowser argument."
+    exit 1
+}
+
+python.exe kohya_gui.py --username $username --password $password --server_port $server_port --inbrowser
\ No newline at end of file
diff --git a/kohya_gui.py b/kohya_gui.py
index fa51fd6..b44c652 100644
--- a/kohya_gui.py
+++ b/kohya_gui.py
@@ -10,7 +10,7 @@ from library.merge_lora_gui import gradio_merge_lora_tab
 from lora_gui import lora_tab
 
 
-def UI(username, password):
+def UI(username, password, inbrowser, server_port):
 
     css = ''
 
@@ -47,11 +47,13 @@ def UI(username, password):
             gradio_merge_lora_tab()
 
     # Show the interface
-    if not username == '':
-        interface.launch(auth=(username, password))
-    else:
-        interface.launch()
-
+    kwargs = {}
+    if username:
+        kwargs["auth"] = (username, password)
+    if server_port > 0:
+        kwargs["server_port"] = server_port
+    kwargs["inbrowser"] = inbrowser
+    interface.launch(**kwargs)
 
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
@@ -62,7 +64,11 @@ if __name__ == '__main__':
     parser.add_argument(
         '--password', type=str, default='', help='Password for authentication'
     )
+    parser.add_argument(
+        '--server_port', type=int, default=0, help='Port to run the server listener on'
+    )
+    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password)
+    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
diff --git a/library/common_gui.py b/library/common_gui.py
index c93b04e..a78532d 100644
--- a/library/common_gui.py
+++ b/library/common_gui.py
@@ -563,6 +563,15 @@ def gradio_advanced_training():
         random_crop = gr.Checkbox(
             label='Random crop instead of center crop', value=False
         )
+    with gr.Row():
+        caption_dropout_every_n_epochs = gr.Number(
+            label="Dropout caption every n epochs",
+            value=0
+        )
+        caption_dropout_rate = gr.Number(
+            label="Rate of caption dropout",
+            value=0
+        )
     with gr.Row():
         save_state = gr.Checkbox(label='Save training state', value=False)
         resume = gr.Textbox(
@@ -599,6 +608,7 @@ def gradio_advanced_training():
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
+        caption_dropout_every_n_epochs, caption_dropout_rate,
     )
 
 
@@ -622,6 +632,12 @@ def run_cmd_advanced_training(**kwargs):
         f' --keep_tokens="{kwargs.get("keep_tokens", "")}"'
         if int(kwargs.get('keep_tokens', 0)) > 0
         else '',
+        f' --caption_dropout_every_n_epochs="{kwargs.get("caption_dropout_every_n_epochs", "")}"'
+        if int(kwargs.get('caption_dropout_every_n_epochs', 0)) > 0
+        else '',
+        f' --caption_dropout_rate="{kwargs.get("caption_dropout_rate", "")}"'
+        if float(kwargs.get('caption_dropout_rate', 0)) > 0
+        else '',
         
         f' --bucket_reso_steps={int(kwargs.get("bucket_reso_steps", 1))}'
         if int(kwargs.get('bucket_reso_steps', 64)) >= 1
diff --git a/library/train_util.py b/library/train_util.py
index 379b0b8..df6e24e 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -113,7 +113,7 @@ class BucketManager():
     # 規定サイズから選ぶ場合の解像度、aspect ratioの情報を格納しておく
     self.predefined_resos = resos.copy()
     self.predefined_resos_set = set(resos)
-    self.predifined_aspect_ratios = np.array([w / h for w, h in resos])
+    self.predefined_aspect_ratios = np.array([w / h for w, h in resos])
 
   def add_if_new_reso(self, reso):
     if reso not in self.reso_to_id:
@@ -135,7 +135,7 @@ class BucketManager():
       if reso in self.predefined_resos_set:
         pass
       else:
-        ar_errors = self.predifined_aspect_ratios - aspect_ratio
+        ar_errors = self.predefined_aspect_ratios - aspect_ratio
         predefined_bucket_id = np.abs(ar_errors).argmin()          # 当該解像度以外でaspect ratio errorが最も少ないもの
         reso = self.predefined_resos[predefined_bucket_id]
 
@@ -223,6 +223,10 @@ class BaseDataset(torch.utils.data.Dataset):
 
     self.tokenizer_max_length = self.tokenizer.model_max_length if max_token_length is None else max_token_length + 2
 
+    self.current_epoch: int = 0            # インスタンスがepochごとに新しく作られるようなので外側から渡さないとダメ
+    self.dropout_rate: float = 0
+    self.dropout_every_n_epochs: int = None
+
     # augmentation
     flip_p = 0.5 if flip_aug else 0.0
     if color_aug:
@@ -247,6 +251,15 @@ class BaseDataset(torch.utils.data.Dataset):
 
     self.replacements = {}
 
+  def set_current_epoch(self, epoch):
+    self.current_epoch = epoch
+
+  def set_caption_dropout(self, dropout_rate, dropout_every_n_epochs, tag_dropout_rate):
+    # コンストラクタで渡さないのはTextual Inversionで意識したくないから（ということにしておく）
+    self.dropout_rate = dropout_rate
+    self.dropout_every_n_epochs = dropout_every_n_epochs
+    self.tag_dropout_rate = tag_dropout_rate
+
   def set_tag_frequency(self, dir_name, captions):
     frequency_for_dir = self.tag_frequency.get(dir_name, {})
     self.tag_frequency[dir_name] = frequency_for_dir
@@ -264,27 +277,47 @@ class BaseDataset(torch.utils.data.Dataset):
     self.replacements[str_from] = str_to
 
   def process_caption(self, caption):
-    if self.shuffle_caption:
-      tokens = caption.strip().split(",")
-      if self.shuffle_keep_tokens is None:
-        random.shuffle(tokens)
-      else:
-        if len(tokens) > self.shuffle_keep_tokens:
-          keep_tokens = tokens[:self.shuffle_keep_tokens]
-          tokens = tokens[self.shuffle_keep_tokens:]
-          random.shuffle(tokens)
-          tokens = keep_tokens + tokens
-      caption = ",".join(tokens).strip()
+    # dropoutの決定：tag dropがこのメソッド内にあるのでここで行うのが良い
+    is_drop_out = self.dropout_rate > 0 and random.random() < self.dropout_rate
+    is_drop_out = is_drop_out or self.dropout_every_n_epochs and self.current_epoch % self.dropout_every_n_epochs == 0
 
-    for str_from, str_to in self.replacements.items():
-      if str_from == "":
-        # replace all
-        if type(str_to) == list:
-          caption = random.choice(str_to)
+    if is_drop_out:
+      caption = ""
+    else:
+      if self.shuffle_caption:
+        def dropout_tags(tokens):
+          if self.tag_dropout_rate <= 0:
+            return tokens
+          l = []
+          for token in tokens:
+            if random.random() >= self.tag_dropout_rate:
+              l.append(token)
+          return l
+
+        tokens = [t.strip() for t in caption.strip().split(",")]
+        if self.shuffle_keep_tokens is None:
+          random.shuffle(tokens)
+          tokens = dropout_tags(tokens)
         else:
-          caption = str_to
-      else:
-        caption = caption.replace(str_from, str_to)
+          if len(tokens) > self.shuffle_keep_tokens:
+            keep_tokens = tokens[:self.shuffle_keep_tokens]
+            tokens = tokens[self.shuffle_keep_tokens:]
+            random.shuffle(tokens)
+            tokens = dropout_tags(tokens)
+
+            tokens = keep_tokens + tokens
+        caption = ", ".join(tokens)
+
+      # textual inversion対応
+      for str_from, str_to in self.replacements.items():
+        if str_from == "":
+          # replace all
+          if type(str_to) == list:
+            caption = random.choice(str_to)
+          else:
+            caption = str_to
+        else:
+          caption = caption.replace(str_from, str_to)
 
     return caption
 
@@ -907,6 +940,8 @@ class FineTuningDataset(BaseDataset):
 def debug_dataset(train_dataset, show_input_ids=False):
   print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}")
   print("Escape for exit. / Escキーで中断、終了します")
+
+  train_dataset.set_current_epoch(1)
   k = 0
   for i, example in enumerate(train_dataset):
     if example['latents'] is not None:
@@ -1377,7 +1412,7 @@ def verify_training_args(args: argparse.Namespace):
     print("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
 
-def add_dataset_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool):
+def add_dataset_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool):
   # dataset common
   parser.add_argument("--train_data_dir", type=str, default=None, help="directory for train images / 学習画像データのディレクトリ")
   parser.add_argument("--shuffle_caption", action="store_true",
@@ -1408,6 +1443,16 @@ def add_dataset_arguments(parser: argparse.ArgumentParser, support_dreambooth: b
   parser.add_argument("--bucket_no_upscale", action="store_true",
                       help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します")
 
+  if support_caption_dropout:
+    # Textual Inversion はcaptionのdropoutをsupportしない
+    # いわゆるtensorのDropoutと紛らわしいのでprefixにcaptionを付けておく　every_n_epochsは他と平仄を合わせてdefault Noneに
+    parser.add_argument("--caption_dropout_rate", type=float, default=0,
+                        help="Rate out dropout caption(0.0~1.0) / captionをdropoutする割合")
+    parser.add_argument("--caption_dropout_every_n_epochs", type=int, default=None,
+                        help="Dropout all captions every N epochs / captionを指定エポックごとにdropoutする")
+    parser.add_argument("--caption_tag_dropout_rate", type=float, default=0,
+                        help="Rate out dropout comma separated tokens(0.0~1.0) / カンマ区切りのタグをdropoutする割合")
+
   if support_dreambooth:
     # DreamBooth dataset
     parser.add_argument("--reg_data_dir", type=str, default=None, help="directory for regularization images / 正則化画像データのディレクトリ")
@@ -1718,4 +1763,4 @@ class ImageLoadingDataset(torch.utils.data.Dataset):
     return (tensor_pil, img_path)
 
 
-# endregion
\ No newline at end of file
+# endregion
diff --git a/lora_gui.py b/lora_gui.py
index 04c0cc1..d48fb5a 100644
--- a/lora_gui.py
+++ b/lora_gui.py
@@ -99,6 +99,7 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -195,6 +196,7 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -275,7 +277,8 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-):
+    caption_dropout_every_n_epochs, caption_dropout_rate,
+):  
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
         return
@@ -380,7 +383,7 @@ def train_model(
 
     run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_network.py"'
 
-    run_cmd += f' --bucket_reso_steps=1 --bucket_no_upscale'   # --random_crop'
+    # run_cmd += f' --caption_dropout_rate="0.1" --caption_dropout_every_n_epochs=1'   # --random_crop'
 
     if v2:
         run_cmd += ' --v2'
@@ -440,7 +443,7 @@ def train_model(
     else:
         run_cmd += f' --lr_scheduler_num_cycles="{epoch}"'
     if not lr_scheduler_power == '':
-        run_cmd += f' --output_name="{lr_scheduler_power}"'
+        run_cmd += f' --lr_scheduler_power="{lr_scheduler_power}"'
 
     run_cmd += run_cmd_training(
         learning_rate=learning_rate,
@@ -476,6 +479,8 @@ def train_model(
         bucket_no_upscale=bucket_no_upscale,
         random_crop=random_crop,
         bucket_reso_steps=bucket_reso_steps,
+        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
+        caption_dropout_rate=caption_dropout_rate,
     )
 
     print(run_cmd)
@@ -725,6 +730,7 @@ def lora_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
+                caption_dropout_every_n_epochs, caption_dropout_rate,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -805,6 +811,7 @@ def lora_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
+        caption_dropout_every_n_epochs, caption_dropout_rate,
     ]
 
     button_open_config.click(
diff --git a/networks/lora.py b/networks/lora.py
index 174feda..a1f38c1 100644
--- a/networks/lora.py
+++ b/networks/lora.py
@@ -5,6 +5,7 @@
 
 import math
 import os
+from typing import List
 import torch
 
 from library import train_util
@@ -98,7 +99,7 @@ class LoRANetwork(torch.nn.Module):
     self.alpha = alpha
 
     # create module instances
-    def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]:
+    def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
       loras = []
       for name, module in root_module.named_modules():
         if module.__class__.__name__ in target_replace_modules:
diff --git a/requirements.txt b/requirements.txt
index eeb0bdc..a8bcefb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,24 +1,26 @@
 accelerate==0.15.0
 transformers==4.26.0
-ftfy
-albumentations
-opencv-python
-einops
+ftfy==6.1.1
+albumentations==1.3.0
+opencv-python==4.7.0.68
+einops==0.6.0
 diffusers[torch]==0.10.2
-pytorch_lightning
+pytorch-lightning==1.9.0
 bitsandbytes==0.35.0
-tensorboard
+tensorboard==2.10.1
 safetensors==0.2.6
 gradio==3.16.2
-altair
-easygui
-tk
+altair==4.2.2
+easygui==0.98.3
+tk==0.1.0
 # for BLIP captioning
-requests
-timm
-fairscale
+requests==2.28.2
+timm==0.6.12
+fairscale==0.4.13
 # for WD14 captioning
-tensorflow<2.11
-huggingface-hub
+# tensorflow<2.11
+tensorflow==2.10.1
+huggingface-hub==0.12.0
+xformers @ https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl
 # for kohya_ss library
 .
\ No newline at end of file
diff --git a/textual_inversion_gui.py b/textual_inversion_gui.py
index b34ca6d..d7b86ef 100644
--- a/textual_inversion_gui.py
+++ b/textual_inversion_gui.py
@@ -94,6 +94,7 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -193,6 +194,7 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -272,6 +274,7 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
+    caption_dropout_every_n_epochs, caption_dropout_rate,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -453,6 +456,8 @@ def train_model(
         bucket_no_upscale=bucket_no_upscale,
         random_crop=random_crop,
         bucket_reso_steps=bucket_reso_steps,
+        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
+        caption_dropout_rate=caption_dropout_rate,
     )
     run_cmd += f' --token_string="{token_string}"'
     run_cmd += f' --init_word="{init_word}"'
@@ -709,6 +714,7 @@ def ti_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
+                caption_dropout_every_n_epochs, caption_dropout_rate,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -783,6 +789,7 @@ def ti_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
+        caption_dropout_every_n_epochs, caption_dropout_rate,
     ]
 
     button_open_config.click(
diff --git a/tools/resize_images_to_resolution.py b/tools/resize_images_to_resolution.py
new file mode 100644
index 0000000..0876a4d
--- /dev/null
+++ b/tools/resize_images_to_resolution.py
@@ -0,0 +1,113 @@
+import glob
+import os
+import cv2
+import argparse
+import shutil
+import math
+
+
+def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divisible_by=2, interpolation=None, save_as_png=False, copy_associated_files=False):
+  # Split the max_resolution string by "," and strip any whitespaces
+  max_resolutions = [res.strip() for res in max_resolution.split(',')]
+
+  # # Calculate max_pixels from max_resolution string
+  # max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1])
+
+  # Create destination folder if it does not exist
+  if not os.path.exists(dst_img_folder):
+    os.makedirs(dst_img_folder)
+
+  # Select interpolation method
+  if interpolation == 'lanczos4':
+    cv2_interpolation = cv2.INTER_LANCZOS4
+  elif interpolation == 'cubic':
+    cv2_interpolation = cv2.INTER_CUBIC
+  else:
+    cv2_interpolation = cv2.INTER_AREA
+
+  # Iterate through all files in src_img_folder
+  img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")                   # copy from train_util.py
+  for filename in os.listdir(src_img_folder):
+    # Check if the image is png, jpg or webp etc...
+    if not filename.endswith(img_exts):
+      # Copy the file to the destination folder if not png, jpg or webp etc (.txt or .caption or etc.)
+      shutil.copy(os.path.join(src_img_folder, filename), os.path.join(dst_img_folder, filename))
+      continue
+
+    # Load image
+    img = cv2.imread(os.path.join(src_img_folder, filename))
+
+    base, _ = os.path.splitext(filename)
+    for max_resolution in max_resolutions:
+      # Calculate max_pixels from max_resolution string
+      max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1])
+
+      # Calculate current number of pixels
+      current_pixels = img.shape[0] * img.shape[1]
+
+      # Check if the image needs resizing
+      if current_pixels > max_pixels:
+        # Calculate scaling factor
+        scale_factor = max_pixels / current_pixels
+
+        # Calculate new dimensions
+        new_height = int(img.shape[0] * math.sqrt(scale_factor))
+        new_width = int(img.shape[1] * math.sqrt(scale_factor))
+
+        # Resize image
+        img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+      else:
+        new_height, new_width = img.shape[0:2]
+
+      # Calculate the new height and width that are divisible by divisible_by (with/without resizing)
+      new_height = new_height if new_height % divisible_by == 0 else new_height - new_height % divisible_by
+      new_width = new_width if new_width % divisible_by == 0 else new_width - new_width % divisible_by
+
+      # Center crop the image to the calculated dimensions
+      y = int((img.shape[0] - new_height) / 2)
+      x = int((img.shape[1] - new_width) / 2)
+      img = img[y:y + new_height, x:x + new_width]
+
+      # Split filename into base and extension
+      new_filename = base + '+' + max_resolution + ('.png' if save_as_png else '.jpg')
+
+      # Save resized image in dst_img_folder
+      cv2.imwrite(os.path.join(dst_img_folder, new_filename), img, [cv2.IMWRITE_JPEG_QUALITY, 100])
+      proc = "Resized" if current_pixels > max_pixels else "Saved"
+      print(f"{proc} image: {filename} with size {img.shape[0]}x{img.shape[1]} as {new_filename}")
+
+    # If other files with same basename, copy them with resolution suffix
+    if copy_associated_files:
+      asoc_files = glob.glob(os.path.join(src_img_folder, base + ".*"))
+      for asoc_file in asoc_files:
+        ext = os.path.splitext(asoc_file)[1]
+        if ext in img_exts:
+          continue
+        for max_resolution in max_resolutions:
+          new_asoc_file = base + '+' + max_resolution + ext
+          print(f"Copy {asoc_file} as {new_asoc_file}")
+          shutil.copy(os.path.join(src_img_folder, asoc_file), os.path.join(dst_img_folder, new_asoc_file))
+
+
+def main():
+  parser = argparse.ArgumentParser(
+      description='Resize images in a folder to a specified max resolution(s) / 指定されたフォルダ内の画像を指定した最大画像サイズ（面積）以下にアスペクト比を維持したままリサイズします')
+  parser.add_argument('src_img_folder', type=str, help='Source folder containing the images / 元画像のフォルダ')
+  parser.add_argument('dst_img_folder', type=str, help='Destination folder to save the resized images / リサイズ後の画像を保存するフォルダ')
+  parser.add_argument('--max_resolution', type=str,
+                      help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128")
+  parser.add_argument('--divisible_by', type=int,
+                      help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1)
+  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'],
+                      default='area', help='Interpolation method for resizing / リサイズ時の補完方法')
+  parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存')
+  parser.add_argument('--copy_associated_files', action='store_true',
+                      help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする')
+
+  args = parser.parse_args()
+  resize_images(args.src_img_folder, args.dst_img_folder, args.max_resolution,
+                args.divisible_by, args.interpolation, args.save_as_png, args.copy_associated_files)
+
+
+if __name__ == '__main__':
+  main()
diff --git a/tools/resize_images_to_resolutions.py b/tools/resize_images_to_resolutions.py
index 5492f1c..3e6f87d 100644
--- a/tools/resize_images_to_resolutions.py
+++ b/tools/resize_images_to_resolutions.py
@@ -4,13 +4,10 @@ import argparse
 import shutil
 import math
 
-def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divisible_by=2):
+def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divisible_by=1, caption_extension=''):
     # Split the max_resolution string by "," and strip any whitespaces
     max_resolutions = [res.strip() for res in max_resolution.split(',')]
     
-    # # Calculate max_pixels from max_resolution string
-    # max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1])
-    
     # Create destination folder if it does not exist
     if not os.path.exists(dst_img_folder):
         os.makedirs(dst_img_folder)
@@ -20,7 +17,7 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
         # Check if the image is png, jpg or webp
         if not filename.endswith(('.png', '.jpg', '.webp')):
             # Copy the file to the destination folder if not png, jpg or webp
-            shutil.copy(os.path.join(src_img_folder, filename), os.path.join(dst_img_folder, filename))
+            # shutil.copy(os.path.join(src_img_folder, filename), os.path.join(dst_img_folder, filename))
             continue
 
         # Load image
@@ -42,8 +39,8 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
                 new_height = int(img.shape[0] * math.sqrt(scale_factor))
                 new_width = int(img.shape[1] * math.sqrt(scale_factor))
 
-                # Resize image
-                img = cv2.resize(img, (new_width, new_height))
+                # Resize image using area interpolation (best when downsampling)
+                img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)
 
                 # Calculate the new height and width that are divisible by divisible_by
                 new_height = new_height if new_height % divisible_by == 0 else new_height - new_height % divisible_by
@@ -57,7 +54,11 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
             # Split filename into base and extension
             base, ext = os.path.splitext(filename)
             new_filename = base + '+' + max_resolution + '.jpg'
-
+            
+            # copy caption file with right name if one exist
+            if os.path.exists(os.path.join(src_img_folder, base + caption_extension)):
+                shutil.copy(os.path.join(src_img_folder, base + caption_extension), os.path.join(dst_img_folder, new_filename + caption_extension))
+            
             # Save resized image in dst_img_folder
             cv2.imwrite(os.path.join(dst_img_folder, new_filename), img, [cv2.IMWRITE_JPEG_QUALITY, 100])
             print(f"Resized image: {filename} with size {img.shape[0]}x{img.shape[1]} as {new_filename}")
@@ -69,8 +70,9 @@ def main():
     parser.add_argument('dst_img_folder', type=str, help='Destination folder to save the resized images')
     parser.add_argument('--max_resolution', type=str, help='Maximum resolution(s) in the format "512x512,448x448,384x384, etc, etc"', default="512x512,448x448,384x384")
     parser.add_argument('--divisible_by', type=int, help='Ensure new dimensions are divisible by this value', default=1)
+    parser.add_argument('--caption_extension', type=str, help='Extension of caption files to copy with resized images"', default=".txt")
     args = parser.parse_args()
-    resize_images(args.src_img_folder, args.dst_img_folder, args.max_resolution)
+    resize_images(args.src_img_folder, args.dst_img_folder, args.max_resolution, args.divisible_by, args.caption_extension)
 
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/train_db.py b/train_db.py
index d1bbc07..c210767 100644
--- a/train_db.py
+++ b/train_db.py
@@ -38,8 +38,13 @@ def train(args):
                                     args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
                                     args.bucket_reso_steps, args.bucket_no_upscale,
                                     args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)
+
   if args.no_token_padding:
     train_dataset.disable_token_padding()
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
@@ -203,6 +208,7 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
 
     # 指定したステップ数までText Encoderを学習する：epoch最初の状態
     unet.train()
@@ -327,7 +333,7 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, True, False)
+  train_util.add_dataset_arguments(parser, True, False, True)
   train_util.add_training_arguments(parser, True)
   train_util.add_sd_saving_arguments(parser)
 
diff --git a/train_network.py b/train_network.py
index 3e8f4e7..bb3159f 100644
--- a/train_network.py
+++ b/train_network.py
@@ -120,18 +120,22 @@ def train(args):
     print("Use DreamBooth method.")
     train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
                                       tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
-                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso, 
-                                      args.bucket_reso_steps, args.bucket_no_upscale, 
-                                      args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range, 
+                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
+                                      args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range,
                                       args.random_crop, args.debug_dataset)
   else:
     print("Train with captions.")
     train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
                                       tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
                                       args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
-                                      args.bucket_reso_steps, args.bucket_no_upscale, 
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
                                       args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
                                       args.dataset_repeats, args.debug_dataset)
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
@@ -376,6 +380,8 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
+
     metadata["ss_epoch"] = str(epoch+1)
 
     network.on_epoch_start(text_encoder, unet)
@@ -509,7 +515,7 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, True, True)
+  train_util.add_dataset_arguments(parser, True, True, True)
   train_util.add_training_arguments(parser, True)
 
   parser.add_argument("--no_metadata", action='store_true', help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 7a8370c..ba2e714 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -235,7 +235,7 @@ def train(args):
       text_encoder, optimizer, train_dataloader, lr_scheduler)
 
   index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
-  print(len(index_no_updates), torch.sum(index_no_updates))
+  # print(len(index_no_updates), torch.sum(index_no_updates))
   orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
 
   # Freeze all parameters except for the token embeddings in text encoder
@@ -296,6 +296,7 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
 
     text_encoder.train()
 
@@ -383,8 +384,8 @@ def train(args):
     accelerator.wait_for_everyone()
 
     updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
-    d = updated_embs - bef_epo_embs
-    print(bef_epo_embs.size(), updated_embs.size(), d.mean(), d.min())
+    # d = updated_embs - bef_epo_embs
+    # print(bef_epo_embs.size(), updated_embs.size(), d.mean(), d.min())
 
     if args.save_every_n_epochs is not None:
       model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
@@ -478,7 +479,7 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, True, True)
+  train_util.add_dataset_arguments(parser, True, True, False)
   train_util.add_training_arguments(parser, True)
 
   parser.add_argument("--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],