diff --git a/src/config.py b/src/config.py
index a844c3c..441635a 100644
--- a/src/config.py
+++ b/src/config.py
@@ -2,7 +2,7 @@ from pathlib import Path
 
 DATA = sorted(Path("/mnt/wsl/PHYSICALDRIVE1/data/unsplash").glob("*.jpg"))
 
-CACHE_PATH = Path("/mnt/wsl/PHYSICALDRIVE1/data/cache2")
+CACHE_PATH = Path("/mnt/wsl/PHYSICALDRIVE1/data/cache")
 CACHE_PATH.mkdir(exist_ok=True, parents=True)
 
 MODELS_PATH = Path("/home/andras/projects/bipolaroid/models")
diff --git a/src/editor/training/histogram_dataset.py b/src/editor/training/histogram_dataset.py
index baf549c..fe39deb 100644
--- a/src/editor/training/histogram_dataset.py
+++ b/src/editor/training/histogram_dataset.py
@@ -4,6 +4,7 @@ from editor.utils import compute_histogram
 from .random_edit import random_edit
 from PIL import Image
 from tqdm import tqdm
+import logging
 import torch
 from pathlib import Path
 
@@ -18,7 +19,7 @@ class HistogramDataset(Dataset):
         paths: List[Path],
         edit_count: int = 5,
         bin_count: int = 32,
-        target_size=(480, 480),
+        target_size=(240, 240),
         delete_corrupt_images: bool = False,
         cache_path: Optional[Path] = None,
     ):
@@ -27,6 +28,11 @@ class HistogramDataset(Dataset):
         self._bin_count = bin_count
         self._target_size = target_size
         self._cache_path = cache_path
+        if self._cache_path:
+            self._cache_path = (
+                self._cache_path
+                / f"{self._bin_count}bins_{self._target_size[0]}x{self._target_size[1]}px"
+            )
 
         if delete_corrupt_images:
             self._delete_corrupt_images()
@@ -37,10 +43,10 @@ class HistogramDataset(Dataset):
             try:
                 Image.open(path)
             except:
-                print(f"Failed to open {path}, deleting...")
+                logging.warning(f"Failed to open {path}, deleting...")
                 deleted_count += 1
                 path.unlink()
-        print(f"Deleted {deleted_count} corrupt images")
+        logging.info(f"Deleted {deleted_count} corrupt images")
 
     def __len__(self):
         return len(self._paths) * self._edit_count
@@ -55,25 +61,29 @@ class HistogramDataset(Dataset):
 
     def get_edited_image(self, original_idx: int, edit_idx: int) -> Image.Image:
         original_image = self.get_original_image(original_idx)
-        return random_edit(original_image, seed=edit_idx)
+        return random_edit(original_image, seed=original_idx * 7919 + edit_idx)
 
     def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
-        if self._cache_path is not None:
-            self._cached_data_path = self._cache_path / f"{idx}.pt"
-            if self._cached_data_path.exists():
-                try:
-                    return torch.load(self._cached_data_path)
-                except:
-                    print(f"Failed to load {self._cached_data_path}, regenerating...")
-
         original_idx = idx // self._edit_count
-        original = self.get_original_image(original_idx)
-        edited = random_edit(original, seed=idx)
+        edit_idx = idx % self._edit_count
 
+        if self._cache_path is not None:
+            _cached_data_path = self._cache_path / str(original_idx) / f"{edit_idx}.pt"
+            _cached_data_path.parent.mkdir(parents=True, exist_ok=True)
+            if _cached_data_path.exists():
+                try:
+                    return torch.load(_cached_data_path)
+                except:
+                    logging.warning(
+                        f"Failed to load {_cached_data_path}, regenerating..."
+                    )
+
+        edited = self.get_edited_image(original_idx, edit_idx)
         edited_histogram = compute_histogram(
             edited, bins=self._bin_count, normalize=True
         )
 
+        original = self.get_original_image(original_idx)
         original_histogram = compute_histogram(
             original, bins=self._bin_count, normalize=True
         )
@@ -84,6 +94,6 @@ class HistogramDataset(Dataset):
         )
 
         if self._cache_path is not None:
-            torch.save(result, self._cached_data_path)
+            torch.save(result, _cached_data_path)
 
         return result