diff --git a/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py b/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
index 47098ff8a1..a3cc6cbbb6 100644
--- a/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
+++ b/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
@@ -89,6 +89,66 @@ def forward(
         return hidden_states, vid_freqs, txt_freqs
 
 
+class ModulateIndexPrepare(nn.Module):
+    """Prepares modulate_index for sequence parallel when zero_cond_t is enabled.
+
+    This module encapsulates the creation of modulate_index tensor, which is used
+    to select different conditioning parameters (shift/scale/gate) for different
+    token positions in image editing tasks.
+
+    Similar to Z-Image's UnifiedPrepare and ImageRopePrepare, this creates a module
+    boundary where _sp_plan can shard the output via split_output=True.
+
+    The modulate_index must be sharded along the sequence dimension to match the
+    sharded hidden_states in SP mode.
+
+    Note: Our _sp_plan corresponds to diffusers' _cp_plan (Context Parallelism).
+    """
+
+    def __init__(self, zero_cond_t: bool = False):
+        super().__init__()
+        self.zero_cond_t = zero_cond_t
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        img_shapes: list[list[tuple[int, int, int]]],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Prepare timestep and modulate_index for SP.
+
+        Args:
+            timestep: Timestep tensor [batch]
+            img_shapes: List of image shape tuples per batch item.
+                Each item is a list of (frame, height, width) tuples.
+                For edit models: [[source_shape], [target_shape1, target_shape2, ...]]
+
+        Returns:
+            timestep: Doubled timestep if zero_cond_t, else original [batch] or [2*batch]
+            modulate_index: Token condition index [batch, seq_len] if zero_cond_t, else None
+                - index=0: source image tokens (use normal timestep conditioning)
+                - index=1: target image tokens (use zero timestep conditioning)
+
+        Note: _sp_plan will shard modulate_index via split_output=True when SP is enabled.
+              The modulate_index sequence dimension must match hidden_states after sharding.
+        """
+        if self.zero_cond_t:
+            # Double the timestep: [timestep, timestep * 0]
+            # This creates two sets of conditioning parameters in AdaLayerNorm
+            timestep = torch.cat([timestep, timestep * 0], dim=0)
+
+            # Create modulate_index to select conditioning per token position
+            # - First image (sample[0]): source image, use index=0 (normal timestep)
+            # - Remaining images (sample[1:]): target images, use index=1 (zero timestep)
+            modulate_index = torch.tensor(
+                [[0] * prod(sample[0]) + [1] * sum([prod(s) for s in sample[1:]]) for sample in img_shapes],
+                device=timestep.device,
+                dtype=torch.int,
+            )
+            return timestep, modulate_index
+
+        return timestep, None
+
+
 class QwenTimestepProjEmbeddings(nn.Module):
     def __init__(self, embedding_dim, use_additional_t_cond=False):
         super().__init__()
@@ -785,6 +845,12 @@ class QwenImageTransformer2DModel(CachedTransformer):
             1: SequenceParallelInput(split_dim=0, expected_dims=2, split_output=True, auto_pad=True),
             # txt_freqs (index 2) is NOT sharded - kept replicated for dual-stream attention
         },
+        # Shard ModulateIndexPrepare output (modulate_index must be sharded to match hidden_states)
+        # This is only active when zero_cond_t=True (image editing models)
+        # Output index 1 is modulate_index [batch, seq_len], needs sharding along dim=1
+        "modulate_index_prepare": {
+            1: SequenceParallelInput(split_dim=1, expected_dims=2, split_output=True, auto_pad=True),
+        },
         # Gather output at proj_out
         "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
     }
@@ -848,6 +914,11 @@ def __init__(
         # This ensures RoPE dimensions align with hidden_states after sharding
         self.image_rope_prepare = ImageRopePrepare(self.img_in, self.pos_embed)
 
+        # ModulateIndexPrepare module for _sp_plan to shard modulate_index
+        # This ensures modulate_index dimensions align with hidden_states after sharding
+        # Only active when zero_cond_t=True (image editing models)
+        self.modulate_index_prepare = ModulateIndexPrepare(zero_cond_t=zero_cond_t)
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -906,15 +977,10 @@ def forward(
         # Ensure timestep tensor is on the same device and dtype as hidden_states
         timestep = timestep.to(device=hidden_states.device, dtype=hidden_states.dtype)
 
-        if self.zero_cond_t:
-            timestep = torch.cat([timestep, timestep * 0], dim=0)
-            modulate_index = torch.tensor(
-                [[0] * prod(sample[0]) + [1] * sum([prod(s) for s in sample[1:]]) for sample in img_shapes],
-                device=timestep.device,
-                dtype=torch.int,
-            )
-        else:
-            modulate_index = None
+        # Prepare timestep and modulate_index via ModulateIndexPrepare module
+        # _sp_plan will shard modulate_index via split_output=True (when zero_cond_t=True)
+        # This ensures modulate_index sequence dimension matches sharded hidden_states
+        timestep, modulate_index = self.modulate_index_prepare(timestep, img_shapes)
 
         encoder_hidden_states = self.txt_norm(encoder_hidden_states)
         encoder_hidden_states = self.txt_in(encoder_hidden_states)