Fix duplicated words and a misspelling in messages and docstrings

Remove accidental repeated words in eight "must have have batch size"
error messages raised by the modular pipelines, and in two docstrings
("Dimension to to encode", "image batch to be be inpainted"). Also fix
"arbitary" to "arbitrary" in the LTX2 pipeline docstring.

Text only, no behavior change.

Author: Sreekant13

src/diffusers/models/unets/unet_spatio_temporal_condition.py

@@ -50,7 +50,7 @@ class UNetSpatioTemporalConditionModel(ModelMixin, AttentionMixin, ConfigMixin,
         block_out_channels (`tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
             The tuple of output channels for each block.
         addition_time_embed_dim: (`int`, defaults to 256):
-            Dimension to to encode the additional time ids.
+            Dimension to encode the additional time ids.
         projection_class_embeddings_input_dim (`int`, defaults to 768):
             The dimension of the projection of encoded `added_time_ids`.
         layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.

src/diffusers/modular_pipelines/anima/before_denoise.py

@@ -109,7 +109,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_images_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_images_per_prompt

src/diffusers/modular_pipelines/flux/before_denoise.py

@@ -470,7 +470,7 @@ def intermediate_outputs(self) -> list[OutputParam]:
     def check_inputs(image_latents, latents):
         if image_latents.shape[0] != latents.shape[0]:
             raise ValueError(
-                f"`image_latents` must have have same batch size as `latents`, but got {image_latents.shape[0]} and {latents.shape[0]}"
+                f"`image_latents` must have same batch size as `latents`, but got {image_latents.shape[0]} and {latents.shape[0]}"
             )
 
         if image_latents.ndim != 3:

src/diffusers/modular_pipelines/helios/before_denoise.py

@@ -168,7 +168,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_videos_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_videos_per_prompt

src/diffusers/modular_pipelines/qwenimage/before_denoise.py

@@ -426,7 +426,7 @@ def intermediate_outputs(self) -> list[OutputParam]:
     def check_inputs(image_latents, latents):
         if image_latents.shape[0] != latents.shape[0]:
             raise ValueError(
-                f"`image_latents` must have have same batch size as `latents`, but got {image_latents.shape[0]} and {latents.shape[0]}"
+                f"`image_latents` must have same batch size as `latents`, but got {image_latents.shape[0]} and {latents.shape[0]}"
             )
 
         if image_latents.ndim != 3:

src/diffusers/modular_pipelines/qwenimage/inputs.py

@@ -68,7 +68,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_images_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_images_per_prompt

src/diffusers/modular_pipelines/stable_diffusion_3/inputs.py

@@ -71,7 +71,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_images_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_images_per_prompt

src/diffusers/modular_pipelines/wan/before_denoise.py

@@ -81,7 +81,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_videos_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_videos_per_prompt

src/diffusers/modular_pipelines/z_image/before_denoise.py

@@ -81,7 +81,7 @@ def repeat_tensor_to_batch_size(
         repeat_by = num_images_per_prompt
     else:
         raise ValueError(
-            f"`{input_name}` must have have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
+            f"`{input_name}` must have batch size 1 or {batch_size}, but got {input_tensor.shape[0]}"
         )
 
     # expand the tensor to match the batch_size * num_images_per_prompt

src/diffusers/pipelines/flux/pipeline_flux_kontext_inpaint.py

@@ -923,7 +923,7 @@ def __call__(
 
         Args:
             image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `list[torch.Tensor]`, `list[PIL.Image.Image]`, or `list[np.ndarray]`):
-                `Image`, numpy array or tensor representing an image batch to be be inpainted (which parts of the image
+                `Image`, numpy array or tensor representing an image batch to be inpainted (which parts of the image
                 to be masked out with `mask_image` and repainted according to `prompt` and `image_reference`). For both
                 numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
                 or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a