diff --git a/python/sglang/multimodal_gen/configs/pipeline_configs/base.py b/python/sglang/multimodal_gen/configs/pipeline_configs/base.py
index 5c0989045..afa353b87 100644
--- a/python/sglang/multimodal_gen/configs/pipeline_configs/base.py
+++ b/python/sglang/multimodal_gen/configs/pipeline_configs/base.py
@@ -343,9 +343,9 @@ class PipelineConfig:
 
     def shard_latents_for_sp(self, batch, latents):
         # general logic for video models
-        if batch.enable_sequence_shard:
-            return latents, False
         sp_world_size, rank_in_sp_group = get_sp_world_size(), get_sp_parallel_rank()
+        if batch.enable_sequence_shard and sp_world_size > 1:
+            return latents, False
         if latents.dim() != 5:
             return latents, False
         time_dim = latents.shape[2]
diff --git a/python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py b/python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py
index ba0074cb5..059d5b582 100644
--- a/python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py
+++ b/python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py
@@ -968,6 +968,28 @@ class WanTransformer3DModel(CachableDiT, OffloadableDiTMixin):
             # batch_size, 6, inner_dim
             timestep_proj = timestep_proj.unflatten(1, (6, -1))
 
+        if sequence_shard_enabled and ts_seq_len is not None:
+            if seq_shard_pad > 0:
+                pad = torch.zeros(
+                    (
+                        batch_size,
+                        seq_shard_pad,
+                        timestep_proj.shape[2],
+                        timestep_proj.shape[3],
+                    ),
+                    dtype=timestep_proj.dtype,
+                    device=timestep_proj.device,
+                )
+                timestep_proj = torch.cat([timestep_proj, pad], dim=1)
+            timestep_proj = timestep_proj.view(
+                batch_size,
+                self.sp_size,
+                local_seq_len,
+                timestep_proj.shape[2],
+                timestep_proj.shape[3],
+            )
+            timestep_proj = timestep_proj[:, sp_rank, :, :, :]
+
         if encoder_hidden_states_image is not None:
             encoder_hidden_states = torch.concat(
                 [encoder_hidden_states_image, encoder_hidden_states], dim=1