diff --git a/finetrainers/conditioning/conditioned_pipeline.py b/finetrainers/conditioning/conditioned_pipeline.py
index 2067b51..0b41be7 100644
--- a/finetrainers/conditioning/conditioned_pipeline.py
+++ b/finetrainers/conditioning/conditioned_pipeline.py
@@ -78,7 +78,7 @@ class LTXConditionedPipeline(LTXPipeline):
         frame_rate: int = 25,
         num_inference_steps: int = 50,
         timesteps: List[int] = None,
-        guidance_scale: float = 3,
+        guidance_scale: float = 0,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -302,15 +302,16 @@ class LTXConditionedPipeline(LTXPipeline):
                     return_dict=False,
                     residual_x=noisy_residual_tokens,
                 )[0]
-                
+                # patchified .... 
                 noise_pred = noise_pred.float()
-
+                # noisy latents isn't patchified ... fix that ...
                 if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
-                noisy_latents = self.scheduler.step(noise_pred, t, noisy_latents, return_dict=False)[0]
+                # Noisy latents needs to be
+                noisy_residual_tokens = self.scheduler.step(noise_pred, t, noisy_residual_tokens, return_dict=False)[0]
 
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
@@ -318,7 +319,7 @@ class LTXConditionedPipeline(LTXPipeline):
                         callback_kwargs[k] = locals()[k]
                     callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
 
-                    noisy_latents = callback_outputs.pop("latents", noisy_latents)
+                    noisy_residual_tokens = callback_outputs.pop("latents", noisy_residual_tokens)
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
 
                 # call the callback, if provided
@@ -331,18 +332,18 @@ class LTXConditionedPipeline(LTXPipeline):
         if output_type == "latent":
             video = latents
         else:
-            noisy_latents = self._unpack_latents(
-                noisy_latents,
+            noisy_residual_tokens = self._unpack_latents(
+                noisy_residual_tokens,
                 latent_num_frames,
                 latent_height,
                 latent_width,
                 self.transformer_spatial_patch_size,
                 self.transformer_temporal_patch_size,
             )
-            noisy_latents = self._denormalize_latents(
-                noisy_latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            noisy_residual_tokens = self._denormalize_latents(
+                noisy_residual_tokens, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
             )
-            noisy_latents = noisy_latents.to(prompt_embeds.dtype)
+            noisy_residual_tokens = noisy_residual_tokens.to(prompt_embeds.dtype)
 
             if not self.vae.config.timestep_conditioning:
                 timestep = None