diff --git a/library/train_util.py b/library/train_util.py
index d4a0ec45..8c114297 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -5518,7 +5518,7 @@ def sample_images_common(
                 idx = (idx + 1) % len(example_tuple[1])
                 if idx == 0:
                     break
-                prompts[i]["prompt"] = prompt_dict.get("prompt").replace('__caption__', 'example_tuple[1][idx]') 
+                prompts[i]["prompt"] = prompt_dict.get("prompt").replace('__caption__', example_tuple[1][idx]) 
                 prompts[i]["height"] = example_tuple[0].shape[2] * 8
                 prompts[i]["width"] = example_tuple[0].shape[3] * 8
                 prompts[i]["original_lantent"] = example_tuple[0][idx].unsqueeze(0)
@@ -5540,6 +5540,9 @@ def sample_images_common(
         # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts.
         with torch.no_grad():
             for prompt_dict in prompts:
+                if prompt_dict["prompt"] == '__caption__':
+                    logger.info("No training prompts loaded, skipping '__caption__' prompt.")
+                    continue
                 sample_image_inference(
                     accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet
                 )
@@ -5553,6 +5556,9 @@ def sample_images_common(
         with torch.no_grad():
             with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists:
                 for prompt_dict in prompt_dict_lists[0]:
+                    if prompt_dict["prompt"] == '__caption__':
+                    logger.info("No training prompts loaded, skipping '__caption__' prompt.")
+                    continue
                     sample_image_inference(
                         accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet
                     )