Fix duplicated words in runtime error messages

src/diffusers/modular_pipelines/anima/before_denoise.py

@@ -108,9 +108,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)

src/diffusers/modular_pipelines/components_manager.py

@@ -172,7 +172,7 @@ def __call__(self, hooks, model_id, model, execution_device):
         try:
             mem_on_device = device_module.mem_get_info(execution_device.index)[0]
         except AttributeError:
-            raise AttributeError(f"Do not know how to obtain obtain memory info for {str(device_module)}.")
+            raise AttributeError(f"Do not know how to obtain memory info for {str(device_module)}.")
 
         mem_on_device = mem_on_device - self.memory_reserve_margin
         if current_module_size < mem_on_device:

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

@@ -167,9 +167,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)

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

@@ -67,9 +67,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)

src/diffusers/modular_pipelines/stable_diffusion_3/inputs.py

@@ -70,9 +70,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)

src/diffusers/modular_pipelines/wan/before_denoise.py

@@ -80,9 +80,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)

src/diffusers/modular_pipelines/z_image/before_denoise.py

@@ -80,9 +80,7 @@ def repeat_tensor_to_batch_size(
     elif input_tensor.shape[0] == 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]}"
-        )
+        raise ValueError(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
     input_tensor = input_tensor.repeat_interleave(repeat_by, dim=0)