Add tag freq for FinetuneDataset

library/train_util.py

@@ -44,7 +44,8 @@ DEFAULT_LAST_OUTPUT_NAME = "last"
 
 # region dataset
 
-IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".PNG", ".JPG", ".JPEG", ".WEBP", ".BMP"]
+IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp", ".bmp"]
+# , ".PNG", ".JPG", ".JPEG", ".WEBP", ".BMP"]         # Linux?
 
 
 class ImageInfo():
@@ -116,6 +117,16 @@ class BaseDataset(torch.utils.data.Dataset):
 
     self.replacements = {}
 
+  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
+    for caption in captions:
+      for tag in caption.split(","):
+        if tag and not tag.isspace():
+          tag = tag.lower()
+          frequency = frequency_for_dir.get(tag, 0)
+          frequency_for_dir[tag] = frequency + 1
+
   def disable_token_padding(self):
     self.token_padding_disabled = True
 
@@ -545,14 +556,7 @@ class DreamBoothDataset(BaseDataset):
         cap_for_img = read_caption(img_path)
         captions.append(caption_by_folder if cap_for_img is None else cap_for_img)
 
-      frequency_for_dir = self.tag_frequency.get(os.path.basename(dir), {})
+      self.set_tag_frequency(os.path.basename(dir), captions)         # タグ頻度を記録
-      self.tag_frequency[os.path.basename(dir)] = frequency_for_dir
-      for caption in captions:
-        for tag in caption.split(","):
-          if tag and not tag.isspace():
-            tag = tag.lower()
-            frequency = frequency_for_dir.get(tag, 0)
-            frequency_for_dir[tag] = frequency + 1
 
       return n_repeats, img_paths, captions
 
@@ -562,10 +566,13 @@ class DreamBoothDataset(BaseDataset):
     for dir in train_dirs:
       n_repeats, img_paths, captions = load_dreambooth_dir(os.path.join(train_data_dir, dir))
       num_train_images += n_repeats * len(img_paths)
+
       for img_path, caption in zip(img_paths, captions):
         info = ImageInfo(img_path, n_repeats, caption, False, img_path)
         self.register_image(info)
+
       self.dataset_dirs_info[os.path.basename(dir)] = {"n_repeats": n_repeats, "img_count": len(img_paths)}
+
     print(f"{num_train_images} train images with repeating.")
     self.num_train_images = num_train_images
 
@@ -579,9 +586,11 @@ class DreamBoothDataset(BaseDataset):
       for dir in reg_dirs:
         n_repeats, img_paths, captions = load_dreambooth_dir(os.path.join(reg_data_dir, dir))
         num_reg_images += n_repeats * len(img_paths)
+
         for img_path, caption in zip(img_paths, captions):
           info = ImageInfo(img_path, n_repeats, caption, True, img_path)
           reg_infos.append(info)
+
         self.reg_dataset_dirs_info[os.path.basename(dir)] = {"n_repeats": n_repeats, "img_count": len(img_paths)}
 
       print(f"{num_reg_images} reg images.")
@@ -626,6 +635,7 @@ class FineTuningDataset(BaseDataset):
     self.train_data_dir = train_data_dir
     self.batch_size = batch_size
 
+    tags_list = []
     for image_key, img_md in metadata.items():
       # path情報を作る
       if os.path.exists(image_key):
@@ -642,6 +652,7 @@ class FineTuningDataset(BaseDataset):
         caption = tags
       elif tags is not None and len(tags) > 0:
         caption = caption + ', ' + tags
+        tags_list.append(tags)
       assert caption is not None and len(caption) > 0, f"caption or tag is required / キャプションまたはタグは必須です:{abs_path}"
 
       image_info = ImageInfo(image_key, dataset_repeats, caption, False, abs_path)
@@ -655,7 +666,8 @@ class FineTuningDataset(BaseDataset):
     self.num_train_images = len(metadata) * dataset_repeats
     self.num_reg_images = 0
 
-    self.dataset_dirs_info[os.path.basename(self.train_data_dir)] = {"n_repeats": dataset_repeats, "img_count": len(metadata)}
+    self.set_tag_frequency(os.path.basename(json_file_name), tags_list)
+    self.dataset_dirs_info[os.path.basename(json_file_name)] = {"n_repeats": dataset_repeats, "img_count": len(metadata)}
 
     # check existence of all npz files
     if not self.color_aug:
@@ -676,6 +688,8 @@ class FineTuningDataset(BaseDataset):
         print(f"npz file does not exist. make latents with VAE / npzファイルが見つからないためVAEを使ってlatentsを取得します")
       elif not npz_all:
         print(f"some of npz file does not exist. ignore npz files / いくつかのnpzファイルが見つからないためnpzファイルを無視します")
+        if self.flip_aug:
+          print("maybe no flipped files / 反転されたnpzファイルがないのかもしれません")
         for image_info in self.image_data.values():
           image_info.latents_npz = image_info.latents_npz_flipped = None
 
@@ -772,8 +786,8 @@ def glob_images(directory, 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 = list(set(img_paths))                    # 重複を排除
-  img_paths.sort()
+  # img_paths.sort()
   return img_paths
 
 
@@ -785,8 +799,8 @@ def glob_images_pathlib(dir_path, recursive):
   else:
     for ext in IMAGE_EXTENSIONS:
       image_paths += list(dir_path.glob('*' + ext))
-  image_paths = list(set(image_paths))        # 重複を排除
+  # image_paths = list(set(image_paths))        # 重複を排除
-  image_paths.sort()
+  # image_paths.sort()
   return image_paths
 
 # endregion