diff --git a/muse_maskgit_pytorch/dataset.py b/muse_maskgit_pytorch/dataset.py
index 753b351..431583f 100644
--- a/muse_maskgit_pytorch/dataset.py
+++ b/muse_maskgit_pytorch/dataset.py
@@ -167,16 +167,20 @@ def __getitem__(self, index):
         else:
             text = descriptions
         # max length from the paper
-        encoded = self.tokenizer.batch_encode_plus(
-            [str(text)],
-            return_tensors="pt",
-            padding="max_length",
-            max_length=MAX_LENGTH,
-            truncation=True,
-        )
+        if self.tokenizer is not None:
+            encoded = self.tokenizer.batch_encode_plus(
+                [str(text)],
+                return_tensors="pt",
+                padding="max_length",
+                max_length=MAX_LENGTH,
+                truncation=True,
+            )
 
-        input_ids = encoded.input_ids
-        attn_mask = encoded.attention_mask
+            input_ids = encoded.input_ids
+            attn_mask = encoded.attention_mask
+        else:
+            input_ids = []
+            attn_mask = []
 
         if self.using_taming:
             if self.embeds:
@@ -242,16 +246,20 @@ def __getitem__(self, index):
         else:
             text = descriptions
         # max length from the paper
-        encoded = self.tokenizer.batch_encode_plus(
-            [str(text)],
-            return_tensors="pt",
-            padding="max_length",
-            max_length=MAX_LENGTH,
-            truncation=True,
-        )
+        if self.tokenizer is not None:
+            encoded = self.tokenizer.batch_encode_plus(
+                [str(text)],
+                return_tensors="pt",
+                padding="max_length",
+                max_length=MAX_LENGTH,
+                truncation=True,
+            )
 
-        input_ids = encoded.input_ids
-        attn_mask = encoded.attention_mask
+            input_ids = encoded.input_ids
+            attn_mask = encoded.attention_mask
+        else:
+            input_ids = []
+            attn_mask = []
 
         if self.using_taming:
             if self.embeds:
@@ -338,16 +346,21 @@ def __getitem__(self, index):
             embed = self.embeds[index]
 
         # max length from the paper
-        encoded = self.tokenizer.batch_encode_plus(
-            [str(text)],
-            return_tensors="pt",
-            padding="max_length",
-            max_length=MAX_LENGTH,
-            truncation=True,
-        )
+        if self.tokenizer is not None:
+            encoded = self.tokenizer.batch_encode_plus(
+                [str(text)],
+                return_tensors="pt",
+                padding="max_length",
+                max_length=MAX_LENGTH,
+                truncation=True,
+            )
+
+            input_ids = encoded.input_ids
+            attn_mask = encoded.attention_mask
+        else:
+            input_ids = []
+            attn_mask = []
 
-        input_ids = encoded.input_ids
-        attn_mask = encoded.attention_mask
         if self.using_taming:
             if self.embeds:
                 return self.transform(image) - 0.5, input_ids[0], attn_mask[0], embed, text
diff --git a/muse_maskgit_pytorch/muse_maskgit_pytorch.py b/muse_maskgit_pytorch/muse_maskgit_pytorch.py
index 2b3fd7b..0199b2c 100644
--- a/muse_maskgit_pytorch/muse_maskgit_pytorch.py
+++ b/muse_maskgit_pytorch/muse_maskgit_pytorch.py
@@ -185,6 +185,7 @@ def __init__(
         self.norm = LayerNorm(dim)
 
         self.use_clip = use_clip
+        self.tokenizer = None
 
         self.dim_out = default(dim_out, num_tokens)
         self.to_logits = nn.Linear(dim, self.dim_out, bias=False)
diff --git a/train_muse_maskgit.py b/train_muse_maskgit.py
index b8efe29..c573a9d 100644
--- a/train_muse_maskgit.py
+++ b/train_muse_maskgit.py
@@ -824,6 +824,9 @@ def main():
     else:
         embeds = []
 
+    if args.use_metaclip:
+        transformer.tokenizer = None
+
     # Create the dataset objects
     with accelerator.main_process_first():
         if args.no_cache and args.train_data_dir:
@@ -1003,8 +1006,8 @@ def main():
             clip_precision = args.mixed_precision
 
         clip = open_clip.create_model_and_transforms(
-            "ViT-L-14",
-            pretrained="metaclip/l14_400m.pt",
+            "convnext_base_w",
+            pretrained="laion2b_s13b_b82k_augreg",
             cache_dir=args.cache_path,
             precision=clip_precision,
             device=accelerator.device,