multi embed training

train_textual_inversion.py

@@ -7,10 +7,13 @@ import toml
 
 from tqdm import tqdm
 import torch
+
 try:
     import intel_extension_for_pytorch as ipex
+
     if torch.xpu.is_available():
         from library.ipex import ipex_init
+
         ipex_init()
 except Exception:
     pass
@@ -167,6 +170,13 @@ class TextualInversionTrainer:
             args.output_name = args.token_string
         use_template = args.use_object_template or args.use_style_template
 
+        assert (
+            args.token_string is not None or args.token_strings is not None
+        ), "token_string or token_strings must be specified / token_stringまたはtoken_stringsを指定してください"
+        assert (
+            not use_template or args.token_strings is None
+        ), "token_strings cannot be used with template / token_stringsはテンプレートと一緒に使えません"
+
         train_util.verify_training_args(args)
         train_util.prepare_dataset_args(args, True)
 
@@ -215,9 +225,17 @@ class TextualInversionTrainer:
         # add new word to tokenizer, count is num_vectors_per_token
         # if token_string is hoge, "hoge", "hoge1", "hoge2", ... are added
 
-        self.assert_token_string(args.token_string, tokenizers)
+        if args.token_strings is not None:
+            token_strings = args.token_strings
+            assert (
+                len(token_strings) == args.num_vectors_per_token
+            ), f"num_vectors_per_token is mismatch for token_strings / token_stringsの数がnum_vectors_per_tokenと合いません: {len(token_strings)}"
+            for token_string in token_strings:
+                self.assert_token_string(token_string, tokenizers)
+        else:
+            self.assert_token_string(args.token_string, tokenizers)
+            token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
 
-        token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
         token_ids_list = []
         token_embeds_list = []
         for i, (tokenizer, text_encoder, init_token_ids) in enumerate(zip(tokenizers, text_encoders, init_token_ids_list)):
@@ -332,7 +350,7 @@ class TextualInversionTrainer:
                 prompt_replacement = None
         else:
             # サンプル生成用
-            if args.num_vectors_per_token > 1:
+            if args.num_vectors_per_token > 1 and args.token_strings is None:
                 replace_to = " ".join(token_strings)
                 train_dataset_group.add_replacement(args.token_string, replace_to)
                 prompt_replacement = (args.token_string, replace_to)
@@ -752,6 +770,13 @@ def setup_parser() -> argparse.ArgumentParser:
         default=None,
         help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
     )
+    parser.add_argument(
+        "--token_strings",
+        type=str,
+        default=None,
+        nargs="*",
+        help="token strings used in training for multiple embedding / 複数のembeddingsの個別学習時に使用されるトークン文字列",
+    )
     parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
     parser.add_argument(
         "--use_object_template",