[docs] Fix syntax

docs/source/en/api/pipelines/marigold.md

@@ -75,7 +75,7 @@ The following is a summary of the recommended checkpoints, all of which produce
 | [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1)                   | Depth        | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference.                    |
 | [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1)               | Normals      | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1.                                                        |
 | [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) | Intrinsics   | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity.                                                                      | 
-| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)     | Intrinsics   | HyperSim decomposition of an image &nbsp\\(I\\)&nbsp is comprised of Albedo &nbsp\\(A\\), Diffuse shading &nbsp\\(S\\), and Non-diffuse residual &nbsp\\(R\\): &nbsp\\(I = A*S+R\\). |
+| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)     | Intrinsics   | HyperSim decomposition of an image $I$ is comprised of Albedo $A$, Diffuse shading $S$, and Non-diffuse residual $R$: $I = A*S+R$. |
 
 > [!TIP]
 > Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff 

src/diffusers/pipelines/marigold/pipeline_marigold_depth.py

@@ -86,15 +86,14 @@ class MarigoldDepthOutput(BaseOutput):
 
     Args:
         prediction (`np.ndarray`, `torch.Tensor`):
-            Predicted depth maps with values in the range [0, 1]. The shape is $numimages \times 1 \times height \times
-            width$ for `torch.Tensor` or $numimages \times height \times width \times 1$ for `np.ndarray`.
+            Predicted depth maps with values in the range [0, 1]. The shape is `numimages × 1 × height × width` for
+            `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
         uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
-            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
-            \times 1 \times height \times width$ for `torch.Tensor` or $numimages \times height \times width \times 1$
-            for `np.ndarray`.
+            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `numimages × 1 ×
+            height × width` for `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
         latent (`None`, `torch.Tensor`):
             Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
-            The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
+            The shape is `numimages * numensemble × 4 × latentheight × latentwidth`.
     """
 
     prediction: Union[np.ndarray, torch.Tensor]

src/diffusers/pipelines/marigold/pipeline_marigold_intrinsics.py

@@ -99,17 +99,17 @@ class MarigoldIntrinsicsOutput(BaseOutput):
 
     Args:
         prediction (`np.ndarray`, `torch.Tensor`):
-            Predicted image intrinsics with values in the range [0, 1]. The shape is $(numimages * numtargets) \times 3
-            \times height \times width$ for `torch.Tensor` or $(numimages * numtargets) \times height \times width
-            \times 3$ for `np.ndarray`, where `numtargets` corresponds to the number of predicted target modalities of
-            the intrinsic image decomposition.
+            Predicted image intrinsics with values in the range [0, 1]. The shape is `(numimages * numtargets) × 3 ×
+            height × width` for `torch.Tensor` or `(numimages * numtargets) × height × width × 3` for `np.ndarray`,
+            where `numtargets` corresponds to the number of predicted target modalities of the intrinsic image
+            decomposition.
         uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
-            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $(numimages *
-            numtargets) \times 3 \times height \times width$ for `torch.Tensor` or $(numimages * numtargets) \times
-            height \times width \times 3$ for `np.ndarray`.
+            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `(numimages *
+            numtargets) × 3 × height × width` for `torch.Tensor` or `(numimages * numtargets) × height × width × 3` for
+            `np.ndarray`.
         latent (`None`, `torch.Tensor`):
             Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
-            The shape is $(numimages * numensemble) \times (numtargets * 4) \times latentheight \times latentwidth$.
+            The shape is `(numimages * numensemble) × (numtargets * 4) × latentheight × latentwidth`.
     """
 
     prediction: Union[np.ndarray, torch.Tensor]

src/diffusers/pipelines/marigold/pipeline_marigold_normals.py

@@ -81,15 +81,14 @@ class MarigoldNormalsOutput(BaseOutput):
 
     Args:
         prediction (`np.ndarray`, `torch.Tensor`):
-            Predicted normals with values in the range [-1, 1]. The shape is $numimages \times 3 \times height \times
-            width$ for `torch.Tensor` or $numimages \times height \times width \times 3$ for `np.ndarray`.
+            Predicted normals with values in the range [-1, 1]. The shape is `numimages × 3 × height × width` for
+            `torch.Tensor` or `numimages × height × width × 3` for `np.ndarray`.
         uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
-            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
-            \times 1 \times height \times width$ for `torch.Tensor` or $numimages \times height \times width \times 1$
-            for `np.ndarray`.
+            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `numimages × 1 ×
+            height × width` for `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`.
         latent (`None`, `torch.Tensor`):
             Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
-            The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
+            The shape is `numimages * numensemble × 4 × latentheight × latentwidth`.
     """
 
     prediction: Union[np.ndarray, torch.Tensor]