diff --git a/scripts/t5_precompute.py b/scripts/t5_precompute.py
new file mode 100644
index 00000000..11b1f8ec
--- /dev/null
+++ b/scripts/t5_precompute.py
@@ -0,0 +1,112 @@
+# Copyright 2022 MosaicML Diffusion authors
+# SPDX-License-Identifier: Apache-2.0
+
+"""Script to stream text from a dataset, compute CLIP and T5 latents, and write the latents to streaming dataset."""
+
+import json
+import os
+from argparse import ArgumentParser
+
+import torch
+from streaming import MDSWriter, StreamingDataset
+from tqdm import trange
+from transformers import AutoModel, AutoTokenizer, CLIPTextModel
+
+# TODO: Implement batching? 10% faster (when using t5-only), but a lot more complicated code
+
+arg_parser = ArgumentParser()
+arg_parser.add_argument('--remote_src_base',
+                        type=str,
+                        required=True,
+                        help='Remote base to download MDS-formatted shards.')
+arg_parser.add_argument('--remote_dst_base', type=str, required=True, help='Remote base to write MDS-formatted shards.')
+arg_parser.add_argument('--subdir_paths',
+                        nargs='+',
+                        type=str,
+                        required=True,
+                        help='Path to the subdirectory to process.')
+args = arg_parser.parse_args()
+
+cache_dir = '/tmp/hf_files'
+
+# Instantiate tokenizers
+print('Building tokenizers')
+t5_tokenizer = AutoTokenizer.from_pretrained('google/t5-v1_1-xxl', cache_dir=cache_dir, local_files_only=True)
+clip_tokenizer = AutoTokenizer.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',
+                                               subfolder='tokenizer',
+                                               cache_dir=cache_dir,
+                                               local_files_only=True)
+
+print('Building models')
+t5_model = AutoModel.from_pretrained('google/t5-v1_1-xxl',
+                                     torch_dtype=torch.float16,
+                                     cache_dir=cache_dir,
+                                     local_files_only=True).encoder.cuda().eval()
+clip_model = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',
+                                           subfolder='text_encoder',
+                                           torch_dtype=torch.float16,
+                                           cache_dir=cache_dir,
+                                           local_files_only=True).cuda().eval()
+
+columns = None
+for subdir_path in args.subdir_paths:
+    remote_src = os.path.join(args.remote_src_base, subdir_path)
+    remote_dst = os.path.join(args.remote_dst_base, subdir_path)
+    # Dataset
+    print('Building dataset')
+    dataset = StreamingDataset(remote=remote_src,
+                               local=os.path.join('/tmp', subdir_path),
+                               download_timeout=300,
+                               shuffle=False)
+
+    # Get columns
+    if columns is None:
+        with open(os.path.join('/tmp/', subdir_path, 'index.json')) as f:
+            index_json = json.load(f)
+        columns = dict(zip(index_json['shards'][0]['column_names'], index_json['shards'][0]['column_encodings']))
+        columns['T5_ATTENTION_MASK'] = 'bytes'
+        columns['T5_LATENTS'] = 'bytes'
+        columns['CLIP_ATTENTION_MASK'] = 'bytes'
+        columns['CLIP_LATENTS'] = 'bytes'
+        columns['CLIP_POOLED_TEXT'] = 'bytes'
+        print(columns)
+
+    # Make writer
+    writer = MDSWriter(out=remote_dst, columns=columns, compression='zstd', hashes=[], size_limit='1GB')
+
+    print('Loading batch')
+    with torch.no_grad():
+        for i in trange(len(dataset)):
+            sample = dataset[i]
+            captions = sample['DESCRIPTION']
+
+            # Pre-compute T5
+            t5_tokenizer_out = t5_tokenizer(captions,
+                                            padding='max_length',
+                                            max_length=t5_tokenizer.model_max_length,
+                                            truncation=True,
+                                            return_tensors='pt')
+            tokenized_captions = t5_tokenizer_out['input_ids'].cuda()
+            attention_masks = t5_tokenizer_out['attention_mask'].to(torch.bool).cuda()
+            sample['T5_ATTENTION_MASK'] = t5_tokenizer_out['attention_mask'].squeeze(0).to(torch.bool).numpy().tobytes()
+            t5_out = t5_model(input_ids=tokenized_captions, attention_mask=attention_masks)
+            sample['T5_LATENTS'] = t5_out[0].squeeze(0).cpu().numpy().tobytes()
+
+            # Pre-compute CLIP
+            clip_tokenizer_out = clip_tokenizer(captions,
+                                                padding='max_length',
+                                                max_length=clip_tokenizer.model_max_length,
+                                                truncation=True,
+                                                return_tensors='pt')
+            tokenized_captions = clip_tokenizer_out['input_ids'].cuda()
+            attention_masks = clip_tokenizer_out['attention_mask'].cuda()
+            sample['CLIP_ATTENTION_MASK'] = clip_tokenizer_out['attention_mask'].squeeze(0).to(
+                torch.bool).numpy().tobytes()
+            clip_out = clip_model(input_ids=tokenized_captions,
+                                  attention_mask=attention_masks,
+                                  output_hidden_states=True)
+            sample['CLIP_LATENTS'] = clip_out.hidden_states[-2].squeeze(0).cpu().numpy().tobytes()
+            sample['CLIP_POOLED_TEXT'] = clip_out[1].squeeze(0).cpu().numpy().tobytes()
+
+            writer.write(sample)
+    writer.finish()