v21.3.9

.gitignore

@@ -8,4 +8,5 @@ wd14_tagger_model
 .DS_Store
 locon
 gui-user.bat
-gui-user.ps1
+gui-user.ps1
+library/__init__.py

README.md

@@ -192,6 +192,8 @@ This will store your a backup file with your current locally installed pip packa
 
 ## Change History
 
+* 2023/04/01 (v21.3.9)
+    - Update how setup is done on Windows by introducing a setup.bat script. This will make it easier to install/re-install on Windows if needed. Many thanks to @missionfloyd for his PR: https://github.com/bmaltais/kohya_ss/pull/496
 * 2023/03/30 (v21.3.8)
     - Fix issue with LyCORIS version not being found: https://github.com/bmaltais/kohya_ss/issues/481
 * 2023/03/29 (v21.3.7)

finetune/tag_images_by_wd14_tagger_bmaltais.py

@@ -0,0 +1,217 @@
+import argparse
+import csv
+import glob
+import os
+
+from PIL import Image
+import cv2
+from tqdm import tqdm
+import numpy as np
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+import torch
+
+# import library.train_util as train_util
+
+# from wd14 tagger
+IMAGE_SIZE = 448
+IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".PNG", ".JPG", ".JPEG", ".WEBP", ".BMP"]
+
+# wd-v1-4-swinv2-tagger-v2 / wd-v1-4-vit-tagger / wd-v1-4-vit-tagger-v2/ wd-v1-4-convnext-tagger / wd-v1-4-convnext-tagger-v2
+DEFAULT_WD14_TAGGER_REPO = 'SmilingWolf/wd-v1-4-convnext-tagger-v2'
+FILES = ["keras_metadata.pb", "saved_model.pb", "selected_tags.csv"]
+SUB_DIR = "variables"
+SUB_DIR_FILES = ["variables.data-00000-of-00001", "variables.index"]
+CSV_FILE = FILES[-1]
+
+def glob_images(directory, base="*"):
+    img_paths = []
+    for ext in IMAGE_EXTENSIONS:
+        if base == "*":
+            img_paths.extend(glob.glob(os.path.join(glob.escape(directory), base + ext)))
+        else:
+            img_paths.extend(glob.glob(glob.escape(os.path.join(directory, base + ext))))
+    img_paths = list(set(img_paths))  # 重複を排除
+    img_paths.sort()
+    return img_paths
+
+def preprocess_image(image):
+  image = np.array(image)
+  image = image[:, :, ::-1]                         # RGB->BGR
+
+  # pad to square
+  size = max(image.shape[0:2])
+  pad_x = size - image.shape[1]
+  pad_y = size - image.shape[0]
+  pad_l = pad_x // 2
+  pad_t = pad_y // 2
+  image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode='constant', constant_values=255)
+
+  interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
+  image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
+
+  image = image.astype(np.float32)
+  return image
+
+
+class ImageLoadingPrepDataset(torch.utils.data.Dataset):
+  def __init__(self, image_paths):
+    self.images = image_paths
+
+  def __len__(self):
+    return len(self.images)
+
+  def __getitem__(self, idx):
+    img_path = self.images[idx]
+
+    try:
+      image = Image.open(img_path).convert("RGB")
+      image = preprocess_image(image)
+      tensor = torch.tensor(image)
+    except Exception as e:
+      print(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}")
+      return None
+
+    return (tensor, img_path)
+
+
+def collate_fn_remove_corrupted(batch):
+  """Collate function that allows to remove corrupted examples in the
+  dataloader. It expects that the dataloader returns 'None' when that occurs.
+  The 'None's in the batch are removed.
+  """
+  # Filter out all the Nones (corrupted examples)
+  batch = list(filter(lambda x: x is not None, batch))
+  return batch
+
+
+def main(args):
+  # hf_hub_downloadをそのまま使うとsymlink関係で問題があるらしいので、キャッシュディレクトリとforce_filenameを指定してなんとかする
+  # depreacatedの警告が出るけどなくなったらその時
+  # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/22
+  if not os.path.exists(args.model_dir) or args.force_download:
+    print(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}")
+    for file in FILES:
+      hf_hub_download(args.repo_id, file, cache_dir=args.model_dir, force_download=True, force_filename=file)
+    for file in SUB_DIR_FILES:
+      hf_hub_download(args.repo_id, file, subfolder=SUB_DIR, cache_dir=os.path.join(
+          args.model_dir, SUB_DIR), force_download=True, force_filename=file)
+  else:
+    print("using existing wd14 tagger model")
+
+  # 画像を読み込む
+  image_paths = glob_images(args.train_data_dir)
+  print(f"found {len(image_paths)} images.")
+
+  print("loading model and labels")
+  model = load_model(args.model_dir)
+
+  # label_names = pd.read_csv("2022_0000_0899_6549/selected_tags.csv")
+  # 依存ライブラリを増やしたくないので自力で読むよ
+  with open(os.path.join(args.model_dir, CSV_FILE), "r", encoding="utf-8") as f:
+    reader = csv.reader(f)
+    l = [row for row in reader]
+    header = l[0]             # tag_id,name,category,count
+    rows = l[1:]
+  assert header[0] == 'tag_id' and header[1] == 'name' and header[2] == 'category', f"unexpected csv format: {header}"
+
+  tags = [row[1] for row in rows[1:] if row[2] == '0']      # categoryが0、つまり通常のタグのみ
+
+  # 推論する
+  def run_batch(path_imgs):
+    imgs = np.array([im for _, im in path_imgs])
+
+    probs = model(imgs, training=False)
+    probs = probs.numpy()
+
+    for (image_path, _), prob in zip(path_imgs, probs):
+      # 最初の4つはratingなので無視する
+      # # First 4 labels are actually ratings: pick one with argmax
+      # ratings_names = label_names[:4]
+      # rating_index = ratings_names["probs"].argmax()
+      # found_rating = ratings_names[rating_index: rating_index + 1][["name", "probs"]]
+
+      # それ以降はタグなのでconfidenceがthresholdより高いものを追加する
+      # Everything else is tags: pick any where prediction confidence > threshold
+      tag_text = ""
+      for i, p in enumerate(prob[4:]):                # numpyとか使うのが良いけど、まあそれほど数も多くないのでループで
+        if p >= args.thresh and i < len(tags):
+          tag_text += ", " + tags[i]
+
+      if len(tag_text) > 0:
+        tag_text = tag_text[2:]                   # 最初の ", " を消す
+
+      with open(os.path.splitext(image_path)[0] + args.caption_extension, "wt", encoding='utf-8') as f:
+        f.write(tag_text + '\n')
+        if args.debug:
+          print(image_path, tag_text)
+
+  # 読み込みの高速化のためにDataLoaderを使うオプション
+  if args.max_data_loader_n_workers is not None:
+    dataset = ImageLoadingPrepDataset(image_paths)
+    data = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
+                                       num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+  else:
+    data = [[(None, ip)] for ip in image_paths]
+
+  b_imgs = []
+  for data_entry in tqdm(data, smoothing=0.0):
+    for data in data_entry:
+      if data is None:
+        continue
+
+      image, image_path = data
+      if image is not None:
+        image = image.detach().numpy()
+      else:
+        try:
+          image = Image.open(image_path)
+          if image.mode != 'RGB':
+            image = image.convert("RGB")
+          image = preprocess_image(image)
+        except Exception as e:
+          print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
+          continue
+      b_imgs.append((image_path, image))
+
+      if len(b_imgs) >= args.batch_size:
+        run_batch(b_imgs)
+        b_imgs.clear()
+
+  if len(b_imgs) > 0:
+    run_batch(b_imgs)
+
+  print("done!")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
+  parser.add_argument("--repo_id", type=str, default=DEFAULT_WD14_TAGGER_REPO,
+                      help="repo id for wd14 tagger on Hugging Face / Hugging Faceのwd14 taggerのリポジトリID")
+  parser.add_argument("--model_dir", type=str, default="wd14_tagger_model",
+                      help="directory to store wd14 tagger model / wd14 taggerのモデルを格納するディレクトリ")
+  parser.add_argument("--force_download", action='store_true',
+                      help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします")
+  parser.add_argument("--thresh", type=float, default=0.35, help="threshold of confidence to add a tag / タグを追加するか判定する閾値")
+  parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
+                      help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）")
+  parser.add_argument("--caption_extention", type=str, default=None,
+                      help="extension of caption file (for backward compatibility) / 出力されるキャプションファイルの拡張子（スペルミスしていたのを残してあります）")
+  parser.add_argument("--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子")
+  parser.add_argument("--debug", action="store_true", help="debug mode")
+
+  return parser
+
+
+if __name__ == '__main__':
+  parser = setup_parser()
+
+  args = parser.parse_args()
+
+  # スペルミスしていたオプションを復元する
+  if args.caption_extention is not None:
+    args.caption_extension = args.caption_extention
+
+  main(args)

library/wd14_caption_gui.py

@@ -33,7 +33,7 @@ def caption_images(
         return
 
     print(f'Captioning files in {train_data_dir}...')
-    run_cmd = f'accelerate launch "./finetune/tag_images_by_wd14_tagger.py"'
+    run_cmd = f'accelerate launch "./finetune/tag_images_by_wd14_tagger_bmaltais.py"'
     run_cmd += f' --batch_size="{int(batch_size)}"'
     run_cmd += f' --thresh="{thresh}"'
     run_cmd += f' --caption_extension="{caption_extension}"'

setup.bat

@@ -1,5 +1,9 @@
 @echo off
-python -m venv venv
+IF NOT EXIST venv (
+    python -m venv venv
+) ELSE (
+    echo venv folder already exists, skipping creation...
+)
 call .\venv\Scripts\activate.bat
 
 pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
@@ -10,4 +14,4 @@ copy /y .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
 copy /y .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
 copy /y .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
 
-accelerate config
+accelerate config