diff --git a/lerobot/common/datasets/transforms.py b/lerobot/common/datasets/transforms.py
index 25c68227..eeab633d 100644
--- a/lerobot/common/datasets/transforms.py
+++ b/lerobot/common/datasets/transforms.py
@@ -98,7 +98,7 @@ class RangeRandomSharpness(Transform):
         return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
 
 
-def make_transforms(cfg):
+def make_transforms(cfg, to_dtype: torch.dtype = torch.float32):
     transforms_list = [
         v2.ColorJitter(brightness=(cfg.brightness.min, cfg.brightness.max)),
         v2.ColorJitter(contrast=(cfg.contrast.min, cfg.contrast.max)),
@@ -118,4 +118,6 @@ def make_transforms(cfg):
         transforms_list, p=transforms_weights, n_subset=cfg.max_num_transforms, random_order=cfg.random_order
     )
 
-    return v2.Compose([transforms, v2.ToDtype(torch.float32, scale=True)])
+    # return transforms
+    # return v2.Compose([transforms, v2.ToDtype(to_dtype, scale=True)])
+    return v2.Compose([transforms, v2.ToDtype(to_dtype, scale=False)])
diff --git a/tests/scripts/save_image_transforms.py b/tests/scripts/save_image_transforms.py
index 8cc7d7ba..777dcd96 100644
--- a/tests/scripts/save_image_transforms.py
+++ b/tests/scripts/save_image_transforms.py
@@ -1,6 +1,8 @@
 from pathlib import Path
 
-from torchvision.transforms import ToPILImage, v2
+import torch
+from torchvision.transforms import v2
+from safetensors.torch import save_file
 
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.transforms import RangeRandomSharpness
@@ -9,7 +11,7 @@ from lerobot.common.utils.utils import seeded_context
 DEFAULT_CONFIG_PATH = "lerobot/configs/default.yaml"
 ARTIFACT_DIR = "tests/data/save_image_transforms"
 SEED = 1336
-to_pil = ToPILImage()
+to_pil = v2.ToPILImage()
 
 
 def main(repo_id):
@@ -30,12 +32,15 @@ def main(repo_id):
         "sharpness": RangeRandomSharpness(0.0, 2.0),
     }
 
-    # Apply each single transformation
+    # frames = {"original_frame": original_frame}
     for name, transform in transforms.items():
         with seeded_context(SEED):
+            # transform = v2.Compose([transform, v2.ToDtype(torch.float32, scale=True)])
             transformed_frame = transform(original_frame)
+            # frames[name] = transform(original_frame)
             to_pil(transformed_frame).save(output_dir / f"{SEED}_{name}.png", quality=100)
 
+    # save_file(frames, output_dir / f"transformed_frames_{SEED}.safetensors")
 
 if __name__ == "__main__":
     repo_id = "lerobot/aloha_mobile_shrimp"
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 6dc91f91..8429997a 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -1,9 +1,17 @@
+from pathlib import Path
+import numpy as np
+from omegaconf import OmegaConf
 import pytest
 import torch
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import functional as F  # noqa: N812
+from PIL import Image
+from safetensors.torch import load_file
 
-from lerobot.common.datasets.transforms import RandomSubsetApply, RangeRandomSharpness
+from lerobot.common.datasets.transforms import RandomSubsetApply, RangeRandomSharpness, make_transforms
+from lerobot.common.datasets.utils import flatten_dict
+from lerobot.common.utils.utils import init_hydra_config, seeded_context
+from tests.utils import DEFAULT_CONFIG_PATH
 
 
 class TestRandomSubsetApply:
@@ -76,5 +84,84 @@ class TestRangeRandomSharpness:
 
 
 class TestMakeTransforms:
-    ...
-    # TODO
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Seed should be the same as the one that was used to generate artifacts"""
+        self.config = {
+            "enable": True,
+            "max_num_transforms": 1,
+            "random_order": False,
+            "brightness": {
+                "weight": 0,
+                "min": 0.0,
+                "max": 2.0
+            },
+            "contrast": {
+                "weight": 0,
+                "min": 0.0,
+                "max": 2.0,
+            },
+            "saturation": {
+                "weight": 0,
+                "min": 0.0,
+                "max": 2.0,
+            },
+            "hue": {
+                "weight": 0,
+                "min": -0.5,
+                "max": 0.5,
+            },
+            "sharpness": {
+                "weight": 0,
+                "min": 0.0,
+                "max": 2.0,
+            },
+        }
+        self.path = Path("tests/data/save_image_transforms")
+        # self.expected_frames = load_file(self.path / f"transformed_frames_1336.safetensors")
+        self.original_frame = self.load_png_to_tensor(self.path / "original_frame.png")
+        # self.original_frame = self.expected_frames["original_frame"]
+        self.transforms = {
+            "brightness": v2.ColorJitter(brightness=(0.0, 2.0)),
+            "contrast": v2.ColorJitter(contrast=(0.0, 2.0)),
+            "saturation": v2.ColorJitter(saturation=(0.0, 2.0)),
+            "hue": v2.ColorJitter(hue=(-0.5, 0.5)),
+            "sharpness": RangeRandomSharpness(0.0, 2.0),
+        }
+
+    @staticmethod
+    def load_png_to_tensor(path: Path):
+        return torch.from_numpy(np.array(Image.open(path).convert('RGB'))).permute(2, 0, 1)
+
+    @pytest.mark.parametrize(
+        "transform_key, seed",
+        [
+            ("brightness", 1336),
+            ("contrast", 1336),
+            ("saturation", 1336),
+            ("hue", 1336),
+            ("sharpness", 1336),
+        ]
+    )
+    def test_single_transform(self, transform_key, seed):
+        config = self.config
+        config[transform_key]["weight"] = 1
+        cfg = OmegaConf.create(config)
+        transform = make_transforms(cfg, to_dtype=torch.uint8)
+
+        # expected_t = self.transforms[transform_key]
+        with seeded_context(seed):
+            actual = transform(self.original_frame)
+        # torch.manual_seed(42)
+        # actual = actual_t(self.original_frame)
+        # torch.manual_seed(42)
+        # expected = expected_t(self.original_frame)
+
+        # with seeded_context(1336):
+        #     expected = expected_t(self.original_frame)
+
+        expected = self.load_png_to_tensor(self.path / f"{seed}_{transform_key}.png")
+        # # expected = self.expected_frames[transform_key]
+        to_pil = v2.ToPILImage()
+        to_pil(actual).save(self.path / f"{seed}_{transform_key}_test.png", quality=100)
+        torch.testing.assert_close(actual, expected)