diff --git a/ddpo_pytorch/aesthetic_scorer.py b/ddpo_pytorch/aesthetic_scorer.py
index bb9af25..461fc2c 100644
--- a/ddpo_pytorch/aesthetic_scorer.py
+++ b/ddpo_pytorch/aesthetic_scorer.py
@@ -5,6 +5,7 @@ import torch
 import torch.nn as nn
 import numpy as np
 from transformers import CLIPModel, CLIPProcessor
+from PIL import Image
 
 ASSETS_PATH = resources.files("ddpo_pytorch.assets")
 
@@ -40,6 +41,8 @@ class AestheticScorer(torch.nn.Module):
 
     @torch.no_grad()
     def __call__(self, images):
+        assert isinstance(images, list)
+        assert isinstance(images[0], Image.Image)
         inputs = self.processor(images=images, return_tensors="pt")
         inputs = {k: v.cuda() for k, v in inputs.items()}
         embed = self.clip.get_image_features(**inputs)
diff --git a/ddpo_pytorch/rewards.py b/ddpo_pytorch/rewards.py
index 1328ac6..0c58533 100644
--- a/ddpo_pytorch/rewards.py
+++ b/ddpo_pytorch/rewards.py
@@ -35,6 +35,10 @@ def aesthetic_score():
     scorer = AestheticScorer().cuda()
 
     def _fn(images, prompts, metadata):
+        if isinstance(images, torch.Tensor):
+            images = (images * 255).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
+            images = images.transpose(0, 2, 3, 1)  # NCHW -> NHWC
+        images = [Image.fromarray(image) for image in images]
         scores = scorer(images)
         return scores, {}