diff --git a/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_lora.py b/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_lora.py
index 2cc6623ec7..885daca572 100644
--- a/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_lora.py
+++ b/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_lora.py
@@ -800,7 +800,7 @@ def diagonal_gaussian_distribution_sample(self, latent_dist: ms.Tensor) -> ms.Te
 
         sample = ops.randn_like(mean, dtype=mean.dtype)
         if self.enable_sequence_parallelism:
-            sample = self.broadcast(sample)
+            sample = self.broadcast((sample,))[0]
         x = mean + std * sample
 
         return x
@@ -824,14 +824,14 @@ def construct(self, videos, text_input_ids_or_prompt_embeds, image_rotary_emb=No
         # Sample noise that will be added to the latents
         noise = ops.randn_like(model_input, dtype=model_input.dtype)
         if self.enable_sequence_parallelism:
-            noise = self.broadcast(noise)
+            noise = self.broadcast((noise,))[0]
         batch_size, num_frames, num_channels, height, width = model_input.shape
 
         # Sample a random timestep for each image
         timesteps = ops.randint(0, self.scheduler_num_train_timesteps, (batch_size,), dtype=ms.int64)
 
         if self.enable_sequence_parallelism:
-            timesteps = self.broadcast(timesteps)
+            timesteps = self.broadcast((timesteps,))[0]
 
         # Rotary embeds is Prepared in dataset.
         if self.use_rotary_positional_embeddings:
diff --git a/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_sft.py b/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_sft.py
index 2bfb8936ab..5a0a7d493a 100644
--- a/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_sft.py
+++ b/examples/diffusers/cogvideox_factory/training/cogvideox_text_to_video_sft.py
@@ -824,7 +824,7 @@ def diagonal_gaussian_distribution_sample(self, latent_dist: ms.Tensor) -> ms.Te
 
         sample = ops.randn_like(mean, dtype=mean.dtype)
         if self.enable_sequence_parallelism:
-            sample = self.broadcast(sample)
+            sample = self.broadcast((sample,))[0]
         x = mean + std * sample
 
         return x
@@ -848,13 +848,13 @@ def construct(self, videos, text_input_ids_or_prompt_embeds, image_rotary_emb=No
         # Sample noise that will be added to the latents
         noise = ops.randn_like(model_input, dtype=model_input.dtype)
         if self.enable_sequence_parallelism:
-            noise = self.broadcast(noise)
+            noise = self.broadcast((noise,))[0]
         batch_size, num_frames, num_channels, height, width = model_input.shape
 
         # Sample a random timestep for each image
         timesteps = ops.randint(0, self.scheduler_num_train_timesteps, (batch_size,), dtype=ms.int64)
         if self.enable_sequence_parallelism:
-            timesteps = self.broadcast(timesteps)
+            timesteps = self.broadcast((timesteps,))[0]
 
         # Rotary embeds is Prepared in dataset.
         if self.use_rotary_positional_embeddings: