diff --git a/marigold/marigold_pipeline.py b/marigold/marigold_pipeline.py
index 15b8283..9410b32 100644
--- a/marigold/marigold_pipeline.py
+++ b/marigold/marigold_pipeline.py
@@ -222,14 +222,15 @@ def __call__(
             input_image = input_image.convert("RGB")
             # convert to torch tensor [H, W, rgb] -> [rgb, H, W]
             rgb = pil_to_tensor(input_image)
+            rgb = rgb.unsqueeze(0)  # [1, rgb, H, W]
         elif isinstance(input_image, torch.Tensor):
-            rgb = input_image.squeeze()
+            rgb = input_image
         else:
             raise TypeError(f"Unknown input type: {type(input_image) = }")
         input_size = rgb.shape
         assert (
-            3 == rgb.dim() and 3 == input_size[0]
-        ), f"Wrong input shape {input_size}, expected [rgb, H, W]"
+            4 == rgb.dim() and 3 == input_size[-3]
+        ), f"Wrong input shape {input_size}, expected [1, rgb, H, W]"
 
         # Resize image
         if processing_res > 0:
@@ -246,7 +247,7 @@ def __call__(
 
         # ----------------- Predicting depth -----------------
         # Batch repeated input image
-        duplicated_rgb = torch.stack([rgb_norm] * ensemble_size)
+        duplicated_rgb = rgb_norm.expand(ensemble_size, -1, -1, -1)
         single_rgb_dataset = TensorDataset(duplicated_rgb)
         if batch_size > 0:
             _bs = batch_size
@@ -287,6 +288,7 @@ def __call__(
                 depth_preds,
                 scale_invariant=self.scale_invariant,
                 shift_invariant=self.shift_invariant,
+                max_res=50,
                 **(ensemble_kwargs or {}),
             )
         else:
@@ -297,7 +299,7 @@ def __call__(
         if match_input_res:
             depth_pred = resize(
                 depth_pred,
-                input_size[1:],
+                input_size[-2:],
                 interpolation=resample_method,
                 antialias=True,
             )
diff --git a/marigold/util/image_util.py b/marigold/util/image_util.py
index 9924bab..82078fe 100644
--- a/marigold/util/image_util.py
+++ b/marigold/util/image_util.py
@@ -86,7 +86,7 @@ def resize_max_res(
 
     Args:
         img (`torch.Tensor`):
-            Image tensor to be resized.
+            Image tensor to be resized. Expected shape: [B, C, H, W]
         max_edge_resolution (`int`):
             Maximum edge length (pixel).
         resample_method (`PIL.Image.Resampling`):
@@ -95,8 +95,9 @@ def resize_max_res(
     Returns:
         `torch.Tensor`: Resized image.
     """
-    assert 3 == img.dim()
-    _, original_height, original_width = img.shape
+    assert 4 == img.dim(), f"Invalid input shape {img.shape}"
+
+    original_height, original_width = img.shape[-2:]
     downscale_factor = min(
         max_edge_resolution / original_width, max_edge_resolution / original_height
     )