diff --git a/python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/cosmos3.py b/python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/cosmos3.py
index 2082eaf96..54f1c03f1 100644
--- a/python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/cosmos3.py
+++ b/python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/cosmos3.py
@@ -822,11 +822,6 @@ class Cosmos3DecodingStage(PipelineStage):
         self._latents_mean = None
         self._latents_std = None
         self._guardrails = guardrails
-        # Use VideoProcessor for postprocessing (same as other video pipelines)
-        from diffusers.video_processor import VideoProcessor
-
-        vae_scale_factor = getattr(vae.config, "scale_factor_spatial", 16)
-        self.video_processor = VideoProcessor(vae_scale_factor=vae_scale_factor)
         if guardrails:
             from sglang.multimodal_gen.runtime.pipelines_core.stages.model_specific_stages.cosmos3_guardrails import (
                 _init_guardrails,
@@ -876,6 +871,16 @@ class Cosmos3DecodingStage(PipelineStage):
 
         return video
 
+    @staticmethod
+    def _postprocess_tensor(decoded: torch.Tensor) -> torch.Tensor:
+        return (decoded * 0.5 + 0.5).clamp(0, 1).float()
+
+    @staticmethod
+    def _postprocess_video_np(video: torch.Tensor, is_image_gen: bool) -> np.ndarray:
+        if is_image_gen:
+            return video.squeeze(2).permute(0, 2, 3, 1).cpu().numpy()
+        return video.permute(0, 2, 3, 4, 1).cpu().numpy()
+
     def forward(self, batch: Req, server_args: ServerArgs):
         """Decode latents to video, or to a single image for T2I."""
         from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import (
@@ -900,25 +905,21 @@ class Cosmos3DecodingStage(PipelineStage):
             self.vae.to("cpu", non_blocking=True)
 
         self.log_info(f"Decoded tensor shape: {decoded.shape}")
-
-        if is_image_gen:
-            output = self.video_processor.postprocess(
-                decoded.squeeze(2), output_type="np"
-            )
-        else:
-            output = self.video_processor.postprocess_video(decoded, output_type="np")
-            self.log_info(f"Postprocessed video shape: {output.shape}")
+        output = self._postprocess_tensor(decoded)
 
         if self._guardrails and batch.use_guardrails is not False:
             from sglang.multimodal_gen.runtime.pipelines_core.stages.model_specific_stages.cosmos3_guardrails import (
                 check_video_safety,
             )
 
+            output = self._postprocess_video_np(output, is_image_gen)
             if is_image_gen:
                 # check_video_safety expects [B, T, H, W, C]; wrap then unwrap.
                 output = check_video_safety(output[:, np.newaxis, ...])[:, 0, ...]
             else:
                 output = check_video_safety(output)
+        elif not is_image_gen:
+            self.log_info(f"Postprocessed video tensor shape: {output.shape}")
 
         return OutputBatch(
             output=output,