@@ -312,12 +312,17 @@ def numpy_to_depth(self, images):
         """
         if images.ndim == 3:
             images = images[None, ...]
-        images = (images * 255).round().astype("uint8")
-        if images.shape[-1] == 1:
-            # special case for grayscale (single channel) images
-            raise Exception("Not supported")
+        images_depth = images[:, :, :, 3:]
+        if images.shape[-1] == 6:
+            images_depth = (images_depth * 255).round().astype("uint8")
+            pil_images = [
+                Image.fromarray(self.rgblike_to_depthmap(image_depth), mode="I;16") for image_depth in images_depth
+            ]
+        elif images.shape[-1] == 4:
+            images_depth = (images_depth * 65535.0).astype(np.uint16)
+            pil_images = [Image.fromarray(image_depth, mode="I;16") for image_depth in images_depth]
         else:
-            pil_images = [Image.fromarray(self.rgblike_to_depthmap(image[:, :, 3:]), mode="I;16") for image in images]
+            raise Exception("Not supported")
 
         return pil_images
 
@@ -349,7 +354,11 @@ def postprocess(
         image = self.pt_to_numpy(image)
 
         if output_type == "np":
-            return image[:, :, :, :3], np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)
+            if image.shape[-1] == 6:
+                image_depth = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)
+            else:
+                image_depth = image[:, :, :, 3:]
+            return image[:, :, :, :3], image_depth
 
         if output_type == "pil":
             return self.numpy_to_pil(image), self.numpy_to_depth(image)
 
@@ -130,9 +130,9 @@ def test_stable_diffusion_ddim(self):
         assert depth.shape == (1, 64, 64)
 
         expected_slice_rgb = np.array(
-            [0.37301102, 0.7023895, 0.7418312, 0.5163375, 0.5825485, 0.60929704, 0.4188174, 0.48407027, 0.46555096]
+            [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]
         )
-        expected_slice_depth = np.array([103.4673, 85.81202, 87.84926])
+        expected_slice_depth = np.array([103.46727, 85.812004, 87.849236])
 
         assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
         assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2
@@ -280,10 +280,30 @@ def test_ldm3d(self):
         output = ldm3d_pipe(**inputs)
         rgb, depth = output.rgb, output.depth
 
-        expected_rgb_mean = 0.54461557
-        expected_rgb_std = 0.2806707
-        expected_depth_mean = 143.64595
-        expected_depth_std = 83.491776
+        expected_rgb_mean = 0.495586
+        expected_rgb_std = 0.33795515
+        expected_depth_mean = 112.48518
+        expected_depth_std = 98.489746
+        assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
+        assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
+        assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
+        assert np.abs(expected_depth_std - depth.std()) < 1e-3
+
+    def test_ldm3d_v2(self):
+        ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c").to(torch_device)
+        ldm3d_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_inputs(torch_device)
+        output = ldm3d_pipe(**inputs)
+        rgb, depth = output.rgb, output.depth
+
+        expected_rgb_mean = 0.4194127
+        expected_rgb_std = 0.35375586
+        expected_depth_mean = 0.5638502
+        expected_depth_std = 0.34686103
+
+        assert rgb.shape == (1, 512, 512, 3)
+        assert depth.shape == (1, 512, 512, 1)
         assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
         assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
         assert np.abs(expected_depth_mean - depth.mean()) < 1e-3