diff --git a/vllm_omni/diffusion/models/bagel/bagel_transformer.py b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
index a14e875c06c..d32a6d8acac 100644
--- a/vllm_omni/diffusion/models/bagel/bagel_transformer.py
+++ b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
@@ -25,6 +25,7 @@
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
+    MergedColumnParallelLinear,
     QKVParallelLinear,
     RowParallelLinear,
 )
@@ -157,21 +158,12 @@ def __init__(
         prefix: str = "",
     ) -> None:
         super().__init__()
-        self.gate_proj = ColumnParallelLinear(
+        self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size,
-            intermediate_size,
-            bias=False,
-            gather_output=False,
-            quant_config=quant_config,
-            prefix=f"{prefix}.gate_proj",
-        )
-        self.up_proj = ColumnParallelLinear(
-            hidden_size,
-            intermediate_size,
+            [intermediate_size, intermediate_size],
             bias=False,
-            gather_output=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.up_proj",
+            prefix=f"{prefix}.gate_up_proj",
         )
         self.down_proj = RowParallelLinear(
             intermediate_size,
@@ -186,8 +178,8 @@ def __init__(
         self.act_fn = nn.SiLU()
 
     def forward(self, x):
-        gate, _ = self.gate_proj(x)
-        up, _ = self.up_proj(x)
+        gate_up, _ = self.gate_up_proj(x)
+        gate, up = gate_up.chunk(2, dim=-1)
         x = self.act_fn(gate) * up
         x, _ = self.down_proj(x)
         return x
@@ -929,13 +921,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             (".qkv_proj", ".q_proj", "q"),
             (".qkv_proj", ".k_proj", "k"),
             (".qkv_proj", ".v_proj", "v"),
-            # MLP gate/up projections — the DiT uses separate
-            # ColumnParallelLinear layers (no fused gate_up_proj), but
-            # these entries are needed so that DiffusionLoRAManager can
-            # derive the packed→sublayer mapping for LoRA checkpoints
-            # that store weights under fused gate_up_proj keys.
-            # The weight loader gracefully falls through to the
-            # non-stacked path when the fused parameter doesn't exist.
+            # MLP gate/up projections — fused into MergedColumnParallelLinear.
+            # HF checkpoints store separate gate_proj / up_proj weights;
+            # these entries remap them to the fused gate_up_proj parameter.
             (".gate_up_proj", ".gate_proj", 0),
             (".gate_up_proj", ".up_proj", 1),
         ]
diff --git a/vllm_omni/diffusion/models/bagel/pipeline_bagel.py b/vllm_omni/diffusion/models/bagel/pipeline_bagel.py
index 3e053cbda50..84f177e01a9 100644
--- a/vllm_omni/diffusion/models/bagel/pipeline_bagel.py
+++ b/vllm_omni/diffusion/models/bagel/pipeline_bagel.py
@@ -675,6 +675,8 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             (".qkv_proj_moe_gen", ".q_proj_moe_gen"),
             (".qkv_proj_moe_gen", ".k_proj_moe_gen"),
             (".qkv_proj_moe_gen", ".v_proj_moe_gen"),
+            (".gate_up_proj", ".gate_proj"),
+            (".gate_up_proj", ".up_proj"),
         ]
         stacked_source_names: set[str] = set()
         for name in list(allowed):