diff --git a/ldm/modules/callbacks/captions.py b/ldm/modules/callbacks/captions.py
new file mode 100644
index 00000000..fd32bb68
--- /dev/null
+++ b/ldm/modules/callbacks/captions.py
@@ -0,0 +1,16 @@
+from captionizer import caption_from_path
+from pytorch_lightning.callbacks import Callback
+
+class CaptionSaverCallback(Callback):
+    def __init__(self):
+        super().__init__()
+
+    def on_save_checkpoint(self, trainer, pl_module, checkpoint):
+        print('Adding training captions to checkpoint [Dataloader]')
+        data = trainer.train_dataloader.loaders.sampler.data_source  # type: ignore
+        prompts = set([
+            caption_from_path(image_path, data.data_root, data.coarse_class_text, data.placeholder_token)
+            for image_path in data.image_paths
+        ])
+        trained_prompts = (list(prompts))
+        checkpoint['trained_captions'] = trained_prompts
diff --git a/ldm/pruner.py b/ldm/pruner.py
index 6b3f088b..44697bbd 100644
--- a/ldm/pruner.py
+++ b/ldm/pruner.py
@@ -1,7 +1,6 @@
 def prune_checkpoint(old_state):
     print(f"Pruning Checkpoint")
     pruned_checkpoint = dict()
-    print(f"Checkpoint Keys: {old_state.keys()}")
     for key in old_state.keys():
         if key != "optimizer_states":
             pruned_checkpoint[key] = old_state[key]
diff --git a/main.py b/main.py
index 0af91e17..abe8af68 100644
--- a/main.py
+++ b/main.py
@@ -771,6 +771,9 @@ def on_train_epoch_start(self, trainer, pl_module):
             "cuda_callback": {
                 "target": "main.CUDACallback"
             },
+            "captions_callback": {
+                "target": "ldm.modules.callbacks.captions.CaptionSaverCallback"
+            }
         }
         if version.parse(pl.__version__) >= version.parse('1.4.0'):
             default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
diff --git a/scripts/show_captions.py b/scripts/show_captions.py
new file mode 100755
index 00000000..ef7471b0
--- /dev/null
+++ b/scripts/show_captions.py
@@ -0,0 +1,23 @@
+#!/bin/env python3
+import torch
+import sys, os
+
+def main(checkpoint_path):
+    try:
+        checkpoint = torch.load(checkpoint_path, map_location="cpu")
+        if 'trained_captions' in checkpoint:
+            captions = checkpoint['trained_captions']
+            global_step = checkpoint['global_step']
+            print(f'Captions in {os.path.basename(checkpoint_path)} [{global_step} Global Steps]:')
+            for caption in captions:
+                print(f'\t"{caption}"')
+        else:
+            print(f'{checkpoint_path} has no captions saved')
+    except:
+        print(f'Failed to extract captions from {checkpoint_path}')
+
+if __name__ == '__main__':
+    if len(sys.argv) == 1:
+        print(f'{sys.argv[0]} <checkpoint>')
+    main(sys.argv[1])
+