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 3327c2a18..5989b21e5 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
@@ -808,36 +808,6 @@ class Cosmos3LatentPreparationStage(PipelineStage):
 
         self.log_info(f"Prepared latents with shape {shape}")
 
-        # Transfer (control-video) conditioning: VAE-encode each control clip
-        # into clean latents the transformer prepends to the GEN sequence. Stored
-        # as a list (one block per hint) so multi-hint transfer (edge + depth …)
-        # threads through the denoiser uniformly with the single-hint case.
-        preprocessed_control = batch.extra.get("preprocessed_control")
-        if preprocessed_control is not None:
-            control_blocks = (
-                preprocessed_control
-                if isinstance(preprocessed_control, list)
-                else [preprocessed_control]
-            )
-            vae_dtype = next(self.vae.parameters()).dtype
-            control_latents_list: list[torch.Tensor] = []
-            for control_pixels_t in control_blocks:
-                control_pixels = control_pixels_t.to(device=device, dtype=vae_dtype)
-                with torch.no_grad():
-                    control_latent = self._vae_encode(control_pixels).to(dtype)
-                if control_latent.shape[-2:] != latents.shape[-2:]:
-                    raise ValueError(
-                        "control latent spatial dims "
-                        f"{tuple(control_latent.shape[-2:])} must match the target "
-                        f"latents {tuple(latents.shape[-2:])}"
-                    )
-                control_latents_list.append(control_latent)
-            batch.extra["control_latents"] = control_latents_list
-            self.log_info(
-                f"Prepared {len(control_latents_list)} control latent block(s) "
-                f"with shape {tuple(control_latents_list[0].shape)}"
-            )
-
         sound_duration = float(getattr(batch, "sound_duration", 0.0) or 0.0)
         if sound_duration > 0.0:
             if not getattr(self.transformer, "sound_gen", False):