diff --git a/finetrainers/ltx_video/lora.py b/finetrainers/ltx_video/lora.py
index 424dc47..8f33da0 100644
--- a/finetrainers/ltx_video/lora.py
+++ b/finetrainers/ltx_video/lora.py
@@ -220,10 +220,11 @@ def conditioned_forward_pass(
     num_frames: int,
     height: int,
     width: int,
+    noisy_latents_residual:torch.Tensor,
     **kwargs,
 ) -> torch.Tensor:
     rope_interpolation_scale = [1 / 25, 32, 32]
-
+   
     denoised_latents = transformer(
         hidden_states=noisy_latents,
         encoder_hidden_states=prompt_embeds,
@@ -234,6 +235,7 @@ def conditioned_forward_pass(
         width=width,
         rope_interpolation_scale=rope_interpolation_scale,
         return_dict=False,
+        residual_x=noisy_latents_residual,
     )[0]
 
     return {"latents": denoised_latents}
diff --git a/finetrainers/trainer.py b/finetrainers/trainer.py
index 9c17640..0e87537 100644
--- a/finetrainers/trainer.py
+++ b/finetrainers/trainer.py
@@ -827,6 +827,7 @@ class Trainer:
                                                                             patch_size_t = 1)
                             # pose information latent 
                             latent_conditions.update({"noisy_latents": latent_final["latents"]})
+                            latent_conditions.update({"noisy_latents_residual":noisy_latents_residual["latents"]})
                         else:
                             # Default to flow-matching noise addition
                             noisy_latents = (1.0 - sigmas) * latent_conditions["latents"] + sigmas * noise
@@ -848,7 +849,6 @@ class Trainer:
                             transformer=self.transformer,
                             scheduler=self.scheduler,
                             timesteps=timesteps,
-                            residual_x=noisy_latents_residual,
                             **latent_conditions,
                             **text_conditions,
                         )