diff --git a/scripts/Clip_IO.py b/scripts/Clip_IO.py
index 77ae2b6..a447aac 100644
--- a/scripts/Clip_IO.py
+++ b/scripts/Clip_IO.py
@@ -3,7 +3,7 @@ import os, csv, warnings, datetime
 import gradio
 import torch
 import lark
-from tkinter import filedialog
+import open_clip
 
 from modules import scripts, script_callbacks, shared, devices, processing, prompt_parser 
 from modules.shared import opts
@@ -445,7 +445,17 @@ class Clip_IO(scripts.Script):
         clip.hijack.fixes = [fixes]
         input_ids_Tensor = torch.asarray([input_ids]).to(devices.device)
 
-        tokens = [clip.wrapped.tokenizer.decoder.get(input_id) for input_id in input_ids]
+        decode: callable[any, str]
+        if hasattr(clip.wrapped, "tokenizer"):  # Ver.1.x
+            decode = clip.wrapped.tokenizer.decoder.get
+            is_open_clip = False
+            pass
+        else:   # Ver.2.x
+            decode = lambda t: open_clip.tokenizer._tokenizer.decoder.get(t)
+            is_open_clip = True
+            pass
+        tokens = [decode(input_id) for input_id in input_ids]
+
         for fix in fixes:
             tokens[fix.offset + 1] = fix.embedding.name
             for i in range(1, fix.embedding.vec.shape[0]):
@@ -453,7 +463,7 @@ class Clip_IO(scripts.Script):
                 pass
             pass
 
-        return clip.wrapped.transformer.text_model.embeddings.token_embedding(input_ids_Tensor), tokens
+        return clip.wrapped.model.token_embedding(input_ids_Tensor) if is_open_clip else clip.wrapped.transformer.text_model.embeddings.token_embedding(input_ids_Tensor), tokens
         pass
 
     def on_save_embeddings_as_pt(prompt: str, filename: str, overwrite: bool):
@@ -546,7 +556,7 @@ class Clip_IO(scripts.Script):
                         for batch_pos in range(len(remade_batch_tokens)):
                             index = remade_batch_tokens[batch_pos].index(clip.id_end)
                             tokens[batch_pos, index+1:tokens.shape[1]] = clip.id_pad
-                    
+
                     z = clip.encode_with_transformers(tokens)
                     if not no_emphasis:
                         batch_multipliers = torch.asarray([x.multipliers for x in batch_chunk]).to(devices.device)