@@ -1,5 +1,5 @@
 from comfy import sd1_clip
-from transformers import LlamaTokenizerFast
+from .llama_tokenizer import LLAMATokenizer
 import comfy.t5
 import os
 
@@ -10,8 +10,8 @@ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
 
 class PT5XlTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LlamaTokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+        tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LLAMATokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
 
 class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None):
 
@@ -0,0 +1,22 @@
+import os
+
+class LLAMATokenizer:
+    @staticmethod
+    def from_pretrained(path):
+        return LLAMATokenizer(path)
+
+    def __init__(self, tokenizer_path):
+        import sentencepiece
+        self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path)
+        self.end = self.tokenizer.eos_id()
+
+    def get_vocab(self):
+        out = {}
+        for i in range(self.tokenizer.get_piece_size()):
+            out[self.tokenizer.id_to_piece(i)] = i
+        return out
+
+    def __call__(self, string):
+        out = self.tokenizer.encode(string)
+        out += [self.end]
+        return {"input_ids": out}