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`

fine_tune.py

@@ -38,7 +38,7 @@ def train(args):
                                                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)
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
 
   train_dataset.make_buckets()
 
@@ -230,8 +230,7 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
-
-    train_dataset.epoch_current = epoch + 1
+    train_dataset.set_current_epoch(epoch + 1)
 
     for m in training_models:
       m.train()

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
+call .\venv\Scripts\activate.bat
+python.exe kohya_gui.py %inbrowserOption% %serverPortOption%

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
+
+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

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)

library/train_util.py

@@ -223,8 +223,7 @@ 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
 
-    # TODO 外から渡したほうが安心だが自動で計算したほうが呼ぶ側に余分なコードがいらないのでよさそう
-    self.epoch_current: int = int(0)                      
+    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
 
@@ -252,11 +251,14 @@ class BaseDataset(torch.utils.data.Dataset):
 
     self.replacements = {}
 
-  def set_caption_dropout(self, dropout_rate, dropout_every_n_epochs):
-    # 将来的にタグのドロップアウトも対応したいのでメソッドを生やしておく
+  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, {})
@@ -275,27 +277,47 @@ class BaseDataset(torch.utils.data.Dataset):
     self.replacements[str_from] = str_to
 
   def process_caption(self, caption):
-    if self.shuffle_caption:
-      tokens = [t.strip() for t in 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)
+    # 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
 
@@ -609,18 +631,7 @@ class BaseDataset(torch.utils.data.Dataset):
       images.append(image)
       latents_list.append(latents)
 
-      # dropoutの決定
-      is_drop_out = False
-      if self.dropout_rate > 0 and random.random() < self.dropout_rate:
-        is_drop_out = True
-      if self.dropout_every_n_epochs and self.epoch_current % self.dropout_every_n_epochs == 0:
-        is_drop_out = True
-
-      if is_drop_out:
-        caption = ""
-        print(f"Drop caption out: {self.process_caption(image_info.caption)}")
-      else:
-        caption = self.process_caption(image_info.caption)
+      caption = self.process_caption(image_info.caption)
       captions.append(caption)
       if not self.token_padding_disabled:                     # this option might be omitted in future
         input_ids_list.append(self.get_input_ids(caption))
@@ -929,6 +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:
@@ -1437,6 +1450,8 @@ def add_dataset_arguments(parser: argparse.ArgumentParser, support_dreambooth: b
                         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

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
 .

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()

train_db.py

@@ -43,7 +43,7 @@ def train(args):
     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)
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
 
   train_dataset.make_buckets()
 
@@ -208,8 +208,7 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
-
-    train_dataset.epoch_current = epoch + 1
+    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()

train_network.py

@@ -134,7 +134,7 @@ def train(args):
                                       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)
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
 
   train_dataset.make_buckets()
 
@@ -380,8 +380,7 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
-
-    train_dataset.epoch_current = epoch + 1
+    train_dataset.set_current_epoch(epoch + 1)
 
     metadata["ss_epoch"] = str(epoch+1)
 

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