diff --git a/ldm/modules/callbacks/captions.py b/ldm/modules/callbacks/captions.py
deleted file mode 100644
index fd32bb68..00000000
--- a/ldm/modules/callbacks/captions.py
+++ /dev/null
@@ -1,16 +0,0 @@
-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 44697bbd..6b3f088b 100644
--- a/ldm/pruner.py
+++ b/ldm/pruner.py
@@ -1,6 +1,7 @@
 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 abe8af68..0af91e17 100644
--- a/main.py
+++ b/main.py
@@ -771,9 +771,6 @@ 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
deleted file mode 100755
index ef7471b0..00000000
--- a/scripts/show_captions.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/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])
-