diff --git a/vllm_omni/diffusion/models/bagel/bagel_transformer.py b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
index e715bdaec8..09144e05a9 100644
--- a/vllm_omni/diffusion/models/bagel/bagel_transformer.py
+++ b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
@@ -1526,6 +1526,11 @@ def _generate_image_parallel(
                 f"Use cfg_parallel_size=3 to enable image CFG in parallel mode."
             )
 
+        # Ensure all ranks start with the same x_t (initial noise may differ
+        # across ranks when no per-request seed is set).
+        x_t = x_t.contiguous()
+        cfg_group.broadcast(x_t, src=0)
+
         # Select this rank's branch inputs
         if cfg_rank == 0:
             # Gen branch: use main inputs directly
@@ -1553,17 +1558,10 @@ def _generate_image_parallel(
 
         for i, t in enumerate(timesteps):
             timestep = torch.tensor([t] * x_t.shape[0], device=x_t.device)
-            if t > cfg_interval[0] and t <= cfg_interval[1]:
-                cfg_text_scale_ = cfg_text_scale
-                cfg_img_scale_ = cfg_img_scale
-            else:
-                cfg_text_scale_ = 1.0
-                cfg_img_scale_ = 1.0
-
-            use_cfg_this_step = cfg_text_scale_ > 1.0
+            use_cfg_this_step = t > cfg_interval[0] and t <= cfg_interval[1] and cfg_text_scale > 1.0
 
             if use_cfg_this_step:
-                # Each rank computes its branch's velocity
+                # CFG interval: each rank computes its own branch
                 local_v_t = self._forward_flow_single_branch(
                     x_t=x_t,
                     timestep=timestep,
@@ -1579,46 +1577,34 @@ def _generate_image_parallel(
                     packed_key_value_indexes=branch_key_value_indexes,
                 )
 
-                # All-gather velocities from all CFG ranks
                 gathered = cfg_group.all_gather(local_v_t, separate_tensors=True)
-
-                # Rank 0 combines with CFG formula
-                if cfg_rank == 0:
-                    v_t = gathered[0]  # gen branch
-                    cfg_text_v_t = gathered[1]  # text_cfg branch
-                    cfg_img_v_t = gathered[2] if (use_cfg_img and len(gathered) > 2) else None
-                    v_t = self._combine_cfg(
-                        v_t,
-                        cfg_text_v_t,
-                        cfg_img_v_t,
-                        cfg_text_scale_,
-                        cfg_img_scale_,
-                        cfg_renorm_type,
-                        cfg_renorm_min,
-                    )
-                    x_t = x_t - v_t.to(x_t.device) * dts[i]
+                v_t = self._combine_cfg(
+                    gathered[0],
+                    gathered[1],
+                    gathered[2] if (use_cfg_img and len(gathered) > 2) else None,
+                    cfg_text_scale,
+                    cfg_img_scale,
+                    cfg_renorm_type,
+                    cfg_renorm_min,
+                )
             else:
-                # Outside cfg_interval: only rank 0 computes (no CFG needed)
-                if cfg_rank == 0:
-                    v_t = self._forward_flow_single_branch(
-                        x_t=x_t,
-                        timestep=timestep,
-                        packed_vae_token_indexes=packed_vae_token_indexes,
-                        packed_vae_position_ids=packed_vae_position_ids,
-                        packed_text_ids=packed_text_ids,
-                        packed_text_indexes=packed_text_indexes,
-                        packed_indexes=packed_indexes,
-                        packed_position_ids=packed_position_ids,
-                        packed_seqlens=packed_seqlens,
-                        key_values_lens=key_values_lens,
-                        past_key_values=past_key_values,
-                        packed_key_value_indexes=packed_key_value_indexes,
-                    )
-                    x_t = x_t - v_t.to(x_t.device) * dts[i]
-
-            # Broadcast updated x_t from rank 0 to all ranks
-            x_t = x_t.contiguous()
-            cfg_group.broadcast(x_t, src=0)
+                # Outside CFG interval: all ranks compute with gen inputs, no comm
+                v_t = self._forward_flow_single_branch(
+                    x_t=x_t,
+                    timestep=timestep,
+                    packed_vae_token_indexes=packed_vae_token_indexes,
+                    packed_vae_position_ids=packed_vae_position_ids,
+                    packed_text_ids=packed_text_ids,
+                    packed_text_indexes=packed_text_indexes,
+                    packed_indexes=packed_indexes,
+                    packed_position_ids=packed_position_ids,
+                    packed_seqlens=packed_seqlens,
+                    key_values_lens=key_values_lens,
+                    past_key_values=past_key_values,
+                    packed_key_value_indexes=packed_key_value_indexes,
+                )
+
+            x_t = x_t - v_t.to(x_t.device) * dts[i]
 
         unpacked_latent = x_t.split((packed_seqlens - 2).tolist())
         return unpacked_latent