fix: bring positional args back, add recursive to blip etc

finetune/make_captions.py

@@ -4,6 +4,7 @@
 import json
 import random
 
+from pathlib import Path
 from PIL import Image
 from tqdm import tqdm
 import numpy as np
@@ -72,7 +73,8 @@ def main(args):
     os.chdir('finetune')
 
   print(f"load images from {args.train_data_dir}")
-  image_paths = train_util.glob_images(args.train_data_dir)
+  train_data_dir_path = Path(args.train_data_dir)
+  image_paths = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
   print(f"found {len(image_paths)} images.")
 
   print(f"loading BLIP caption: {args.caption_weights}")
@@ -152,7 +154,8 @@ def setup_parser() -> argparse.ArgumentParser:
   parser.add_argument("--min_length", type=int, default=5, help="min length of caption / captionの最小長")
   parser.add_argument('--seed', default=42, type=int, help='seed for reproducibility / 再現性を確保するための乱数seed')
   parser.add_argument("--debug", action="store_true", help="debug mode")
-
+  parser.add_argument("--recursive", action="store_true", help="search for images in subfolders recursively")  
+
   return parser
 
 

finetune/make_captions_by_git.py

@@ -2,6 +2,7 @@
 import os
 import re
 
+from pathlib import Path
 from PIL import Image
 from tqdm import tqdm
 import torch
@@ -65,7 +66,8 @@ def _prepare_input_ids_for_generation_patch(self, bos_token_id, encoder_outputs)
   GenerationMixin._prepare_input_ids_for_generation = _prepare_input_ids_for_generation_patch
 
   print(f"load images from {args.train_data_dir}")
-  image_paths = train_util.glob_images(args.train_data_dir)
+  train_data_dir_path = Path(args.train_data_dir)
+  image_paths = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
   print(f"found {len(image_paths)} images.")
 
   # できればcacheに依存せず明示的にダウンロードしたい
@@ -140,7 +142,8 @@ def setup_parser() -> argparse.ArgumentParser:
   parser.add_argument("--remove_words", action="store_true",
                       help="remove like `with the words xxx` from caption / `with the words xxx`のような部分をキャプションから削除する")
   parser.add_argument("--debug", action="store_true", help="debug mode")
-
+  parser.add_argument("--recursive", action="store_true", help="search for images in subfolders recursively")  
+
   return parser
 
 

finetune/prepare_buckets_latents.py

@@ -2,6 +2,8 @@
 import os
 import json
 
+from pathlib import Path
+from typing import List
 from tqdm import tqdm
 import numpy as np
 from PIL import Image
@@ -41,22 +43,31 @@ def get_latents(vae, images, weight_dtype):
   return latents
 
 
-def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip):
+def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip, recursive):
   if is_full_path:
     base_name = os.path.splitext(os.path.basename(image_key))[0]
+    relative_path = os.path.relpath(os.path.dirname(image_key), data_dir)
   else:
     base_name = image_key
+    relative_path = ""
+
   if flip:
     base_name += '_flip'
-  return os.path.join(data_dir, base_name)
+
+  if recursive and relative_path:
+    return os.path.join(data_dir, relative_path, base_name)
+  else:
+    return os.path.join(data_dir, base_name)
+
 
 
 def main(args):
   # assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります"
   if args.bucket_reso_steps % 8 > 0:
     print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります")
 
-  image_paths = train_util.glob_images(args.train_data_dir)
+  train_data_dir_path = Path(args.train_data_dir)
+  image_paths: List[str] = [str(p) for p in train_util.glob_images_pathlib(train_data_dir_path, args.recursive)]
   print(f"found {len(image_paths)} images.")
 
   if os.path.exists(args.in_json):
@@ -99,20 +110,20 @@ def process_batch(is_last):
             f"latent shape {latents.shape}, {bucket[0][1].shape}"
 
         for (image_key, _), latent in zip(bucket, latents):
-          npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False)
+          npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False, args.recursive)
           np.savez(npz_file_name, latent)
 
         # flip
         if args.flip_aug:
           latents = get_latents(vae, [img[:, ::-1].copy() for _, img in bucket], weight_dtype)   # copyがないとTensor変換できない
 
           for (image_key, _), latent in zip(bucket, latents):
-            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True)
+            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True, args.recursive)
             np.savez(npz_file_name, latent)
         else:
           # remove existing flipped npz
           for image_key, _ in bucket:
-            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz"
+            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True, args.recursive) + ".npz"
             if os.path.isfile(npz_file_name):
               print(f"remove existing flipped npz / 既存のflipされたnpzファイルを削除します: {npz_file_name}")
               os.remove(npz_file_name)
@@ -169,9 +180,9 @@ def process_batch(is_last):
 
     # 既に存在するファイルがあればshapeを確認して同じならskipする
     if args.skip_existing:
-      npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"]
+      npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False, args.recursive) + ".npz"]
       if args.flip_aug:
-        npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz")
+        npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True, args.recursive) + ".npz")
 
       found = True
       for npz_file in npz_files:
@@ -256,6 +267,8 @@ def setup_parser() -> argparse.ArgumentParser:
                       help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する")
   parser.add_argument("--skip_existing", action="store_true",
                       help="skip images if npz already exists (both normal and flipped exists if flip_aug is enabled) / npzが既に存在する画像をスキップする（flip_aug有効時は通常、反転の両方が存在する画像をスキップ）")
+  parser.add_argument("--recursive", action="store_true",
+                      help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す")
 
   return parser
 

finetune/tag_images_by_wd14_tagger.py

@@ -10,7 +10,7 @@
 from tensorflow.keras.models import load_model
 from huggingface_hub import hf_hub_download
 import torch
-import pathlib
+from pathlib import Path
 
 import library.train_util as train_util
 
@@ -103,8 +103,8 @@ def main(args):
 
     # 画像を読み込む
 
-    train_data_dir = pathlib.Path(args.train_data_dir)
-    image_paths = train_util.glob_images_pathlib(train_data_dir, args.recursive)
+    train_data_dir_path = Path(args.train_data_dir)
+    image_paths = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
     print(f"found {len(image_paths)} images.")
 
     tag_freq = {}
@@ -205,7 +205,7 @@ def run_batch(path_imgs):
 
 if __name__ == '__main__':
     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("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",