experiment: use T5Tokenizer instead of T5TokenizerFast

invokeai/backend/model_manager/load/model_loaders/flux.py

@@ -13,7 +13,7 @@ from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
     T5EncoderModel,
-    T5TokenizerFast,
+    T5Tokenizer,
 )
 
 from invokeai.app.services.config.config_default import get_config
@@ -146,7 +146,7 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
             )
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
@@ -190,7 +190,7 @@ class T5EncoderCheckpointModel(ModelLoader):
 
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 return T5EncoderModel.from_pretrained(
                     Path(config.path) / "text_encoder_2", torch_dtype="auto", low_cpu_mem_usage=True