diff --git a/finetrainers/conditioning/LTXVideoConditionedTransformer3DModel.py b/finetrainers/conditioning/LTXVideoConditionedTransformer3DModel.py
index cd17de6..98ca484 100644
--- a/finetrainers/conditioning/LTXVideoConditionedTransformer3DModel.py
+++ b/finetrainers/conditioning/LTXVideoConditionedTransformer3DModel.py
@@ -106,7 +106,7 @@ class LTXVideoConditionedTransformer3DModel(LTXVideoTransformer3DModel):
             # inject the condition and the residual then project it into the pretrained proj_in
             hidden_states = self.adapter(residual_x=residual_x,
                                         conditioned_x=hidden_states)
-
+            # whats this value ? 
             hidden_states = self.proj_in(hidden_states)
 
             temb, embedded_timestep = self.time_embed(
@@ -134,7 +134,7 @@ class LTXVideoConditionedTransformer3DModel(LTXVideoTransformer3DModel):
                         return custom_forward
 
                     ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                    hidden_states = torch.utils.checkpoint.checkpoint(
+                    hidden_states = torch.utils.checkpoint.checkpoint( # crashes here
                         create_custom_forward(block),
                         hidden_states,
                         encoder_hidden_states,
diff --git a/finetrainers/conditioning/conditioned_pipeline.py b/finetrainers/conditioning/conditioned_pipeline.py
index 4355359..b52e845 100644
--- a/finetrainers/conditioning/conditioned_pipeline.py
+++ b/finetrainers/conditioning/conditioned_pipeline.py
@@ -180,7 +180,7 @@ class LTXConditionedPipeline(LTXPipeline):
             height,
             width,
             num_frames,
-            torch.float32,
+            torch.bfloat16,
             device,
             generator,
         )
@@ -192,7 +192,7 @@ class LTXConditionedPipeline(LTXPipeline):
             patch_size=self.transformer.config.patch_size,
             patch_size_t=self.transformer.config.patch_size_t,
             device=device,
-            dtype=torch.float32,
+            dtype=torch.bfloat16,
             generator=generator,
         )["latents"]
 
@@ -202,7 +202,7 @@ class LTXConditionedPipeline(LTXPipeline):
             patch_size=self.transformer.config.patch_size,
             patch_size_t=self.transformer.config.patch_size_t,
             device=device,
-            dtype=torch.float32,
+            dtype=torch.bfloat16,
             generator=generator,
         )["latents"]
 
@@ -280,6 +280,8 @@ class LTXConditionedPipeline(LTXPipeline):
                 # change this 
                 # latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 # latent_model_input = latent_model_input.to(prompt_embeds.dtype)
+                
+                # might be  the bug ..
                 latent_model_input = noisy_latent_tokens
                 latent_model_input = latent_model_input.to(prompt_embeds.dtype)
 
diff --git a/finetrainers/trainer.py b/finetrainers/trainer.py
index 35015a1..c3f3c57 100644
--- a/finetrainers/trainer.py
+++ b/finetrainers/trainer.py
@@ -813,7 +813,7 @@ class Trainer:
                             noisy_latents = noisy_latents.to(target_video_latents.dtype)
 
                             # add pose information to both channels
-                            pose_noisy_latents = noisy_latents + pose_video_latents["latents"]
+                            pose_noisy_latents = noisy_latents # + pose_video_latents["latents"]
                             pose_img_ref_latents = img_refs_latents["latents"] + pose_video_latents["latents"]
                             
                             # expand channel information # B x 2C  latent will be projected to adapter to scale it back to 128d using adapter.
@@ -821,14 +821,14 @@ class Trainer:
                             # project this turn into patches
 
                             # concat tensor
-                            pose_img_ref_latents = post_conditioned_latent_patchify(latents=pose_img_ref_latents,
+                            pose_img_ref_tokens = post_conditioned_latent_patchify(latents=pose_img_ref_latents,
                                                                             num_frames=latent_conditions["num_frames"],
                                                                             height=latent_conditions["height"],
                                                                             width=latent_conditions["width"],
                                                                             patch_size = 1,
                                                                             patch_size_t = 1)
                             # target video as a input residual
-                            noisy_latents_residual = post_conditioned_latent_patchify(latents=noisy_latents,
+                            noisy_residual_tokens = post_conditioned_latent_patchify(latents=noisy_latents,
                                                                             num_frames=latent_conditions["num_frames"],
                                                                             height=latent_conditions["height"],
                                                                             width=latent_conditions["width"],
@@ -836,9 +836,9 @@ class Trainer:
                                                                             patch_size_t = 1)
                             # Target Latent Patchified concat tensor
                             # pose template noisey input [cat] img_ref + pose video 
-                            latent_conditions.update({"noisy_latents": pose_img_ref_latents["latents"]})
+                            latent_conditions.update({"noisy_latents": pose_img_ref_tokens["latents"]})
                             # input video noise at level residual information to adapter
-                            latent_conditions.update({"noisy_latents_residual":noisy_latents_residual["latents"]})
+                            latent_conditions.update({"noisy_latents_residual":noisy_residual_tokens["latents"]})
                         else:
                             # Default to flow-matching noise addition
                             noisy_latents = (1.0 - sigmas) * latent_conditions["latents"] + sigmas * noise
@@ -1029,7 +1029,7 @@ class Trainer:
                 image = load_image(image)
             if video is not None:
                 video = load_video(video)
-
+            # loading videos for inference ..
             if self.pose_condition:
                 if pose_video is not None:
                     pose_video = self.preprocess_condition_video(pose_video,max_num_frames=self.dataset.max_num_frames)