Add --keep_tags to keep first N tags fixed on shuffle

data/every_dream.py

@@ -41,6 +41,7 @@ class EveryDreamBatch(Dataset):
                  seed=555,
                  tokenizer=None,
                  shuffle_tags=False,
+                 keep_tags=0,
                  rated_dataset=False,
                  rated_dataset_dropout_target=0.5,
                  name='train'
@@ -54,6 +55,7 @@ class EveryDreamBatch(Dataset):
         self.tokenizer = tokenizer
         self.max_token_length = self.tokenizer.model_max_length
         self.shuffle_tags = shuffle_tags
+        self.keep_tags = keep_tags
         self.seed = seed
         self.rated_dataset = rated_dataset
         self.rated_dataset_dropout_target = rated_dataset_dropout_target
@@ -94,7 +96,7 @@ class EveryDreamBatch(Dataset):
         )
 
         if self.shuffle_tags or train_item["shuffle_tags"]:
-            example["caption"] = train_item["caption"].get_shuffled_caption(self.seed)
+            example["caption"] = train_item["caption"].get_shuffled_caption(self.seed, keep_tags=self.keep_tags)
         else:
             example["caption"] = train_item["caption"].get_caption()
 

data/image_train_item.py

@@ -56,7 +56,7 @@ class ImageCaption:
     def rating(self) -> float:
         return self.__rating
 
-    def get_shuffled_caption(self, seed: int) -> str:
+    def get_shuffled_caption(self, seed: int, keep_tags: int) -> str:
         """
         returns the caption a string with a random selection of the tags in random order
         :param seed used to initialize the randomizer
@@ -74,7 +74,7 @@ class ImageCaption:
             if self.__use_weights:
                 tags_caption = self.__get_weighted_shuffled_tags(seed, self.__tags, self.__tag_weights, max_target_tag_length)
             else:
-                tags_caption = self.__get_shuffled_tags(seed, self.__tags)
+                tags_caption = self.__get_shuffled_tags(seed, self.__tags, keep_tags)
 
             return self.__main_prompt + ", " + tags_caption
         return self.__main_prompt
@@ -111,8 +111,15 @@ class ImageCaption:
         return caption
 
     @staticmethod
-    def __get_shuffled_tags(seed: int, tags: list[str]) -> str:
+    def __get_shuffled_tags(seed: int, tags: list[str], keep_tags: int) -> str:
-        random.Random(seed).shuffle(tags)
+        tags = tags.copy()
+
+        if len(tags) > keep_tags:
+            fixed_tags = tags[:keep_tags]
+            rest = tags[keep_tags:]
+            random.Random(seed).shuffle(rest)
+            tags = fixed_tags + rest
+
         return ", ".join(tags)
 
 class ImageTrainItem:

train.py

@@ -350,6 +350,9 @@ def setup_args(args):
     if not args.shuffle_tags:
         args.shuffle_tags = False
 
+    if not args.keep_tags:
+        args.keep_tags = 0
+
     args.clip_skip = max(min(4, args.clip_skip), 0)
 
     if args.useadam8bit:
@@ -779,6 +782,7 @@ def main(args):
         tokenizer=tokenizer,
         seed = seed,
         shuffle_tags=args.shuffle_tags,
+        keep_tags=args.keep_tags,
         rated_dataset=args.rated_dataset,
         rated_dataset_dropout_target=(1.0 - (args.rated_dataset_target_dropout_percent / 100.0))
     )
@@ -1326,6 +1330,7 @@ if __name__ == "__main__":
     argparser.add_argument("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
     argparser.add_argument("--seed", type=int, default=555, help="seed used for samples and shuffling, use -1 for random")
     argparser.add_argument("--shuffle_tags", action="store_true", default=False, help="randomly shuffles CSV tags in captions, for booru datasets")
+    argparser.add_argument("--keep_tags", type=int, default=0, help="Number of tags to keep when shuffle, def: 0 (shuffle all)")
     argparser.add_argument("--useadam8bit", action="store_true", default=False, help="deprecated, use --optimizer_config and optimizer.json instead")
     argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
     argparser.add_argument("--validation_config", default=None, help="Path to a JSON configuration file for the validator.  Default is no validation.")