diff --git a/scripts/convert_ltx2_to_diffusers.py b/scripts/convert_ltx2_to_diffusers.py
index 5367113365..2794feffed 100644
--- a/scripts/convert_ltx2_to_diffusers.py
+++ b/scripts/convert_ltx2_to_diffusers.py
@@ -63,6 +63,8 @@ LTX_2_0_VIDEO_VAE_RENAME_DICT = {
     "up_blocks.4": "up_blocks.1",
     "up_blocks.5": "up_blocks.2.upsamplers.0",
     "up_blocks.6": "up_blocks.2",
+    "last_time_embedder": "time_embedder",
+    "last_scale_shift_table": "scale_shift_table",
     # Common
     # For all 3D ResNets
     "res_blocks": "resnets",
@@ -372,7 +374,7 @@ def convert_ltx2_connectors(original_state_dict: Dict[str, Any], version: str) -
     return connectors
 
 
-def get_ltx2_video_vae_config(version: str) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
+def get_ltx2_video_vae_config(version: str, timestep_conditioning: bool = False) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
     if version == "test":
         config = {
             "model_id": "diffusers-internal-dev/dummy-ltx2",
@@ -396,7 +398,7 @@ def get_ltx2_video_vae_config(version: str) -> Tuple[Dict[str, Any], Dict[str, A
                 "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
                 "upsample_residual": (True, True, True),
                 "upsample_factor": (2, 2, 2),
-                "timestep_conditioning": False,
+                "timestep_conditioning": timestep_conditioning,
                 "patch_size": 4,
                 "patch_size_t": 1,
                 "resnet_norm_eps": 1e-6,
@@ -433,7 +435,7 @@ def get_ltx2_video_vae_config(version: str) -> Tuple[Dict[str, Any], Dict[str, A
                 "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
                 "upsample_residual": (True, True, True),
                 "upsample_factor": (2, 2, 2),
-                "timestep_conditioning": False,
+                "timestep_conditioning": timestep_conditioning,
                 "patch_size": 4,
                 "patch_size_t": 1,
                 "resnet_norm_eps": 1e-6,
@@ -450,8 +452,8 @@ def get_ltx2_video_vae_config(version: str) -> Tuple[Dict[str, Any], Dict[str, A
     return config, rename_dict, special_keys_remap
 
 
-def convert_ltx2_video_vae(original_state_dict: Dict[str, Any], version: str) -> Dict[str, Any]:
-    config, rename_dict, special_keys_remap = get_ltx2_video_vae_config(version)
+def convert_ltx2_video_vae(original_state_dict: Dict[str, Any], version: str, timestep_conditioning: bool) -> Dict[str, Any]:
+    config, rename_dict, special_keys_remap = get_ltx2_video_vae_config(version, timestep_conditioning)
     diffusers_config = config["diffusers_config"]
 
     with init_empty_weights():
@@ -717,6 +719,7 @@ def get_args():
         help="Latent upsampler filename",
     )
 
+    parser.add_argument("--timestep_conditioning", action="store_true", help="Whether to add timestep condition to the video VAE model")
     parser.add_argument("--vae", action="store_true", help="Whether to convert the video VAE model")
     parser.add_argument("--audio_vae", action="store_true", help="Whether to convert the audio VAE model")
     parser.add_argument("--dit", action="store_true", help="Whether to convert the DiT model")
@@ -786,7 +789,7 @@ def main(args):
             original_vae_ckpt = load_hub_or_local_checkpoint(filename=args.vae_filename)
         elif combined_ckpt is not None:
             original_vae_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.vae_prefix)
-        vae = convert_ltx2_video_vae(original_vae_ckpt, version=args.version)
+        vae = convert_ltx2_video_vae(original_vae_ckpt, version=args.version, timestep_conditioning=args.timestep_conditioning)
         if not args.full_pipeline and not args.upsample_pipeline:
             vae.to(vae_dtype).save_pretrained(os.path.join(args.output_path, "vae"))
 
diff --git a/src/diffusers/pipelines/ltx2/pipeline_ltx2.py b/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
index 7d1cd3ce65..c662d9c167 100644
--- a/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
+++ b/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
@@ -653,6 +653,11 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMix
         latents: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if latents is not None:
+            if latents.ndim == 5:
+                # latents are of shape [B, C, F, H, W], need to be packed
+                latents = self._pack_latents(
+                    latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
+                )
             return latents.to(device=device, dtype=dtype)
 
         height = height // self.vae_spatial_compression_ratio
@@ -694,6 +699,9 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMix
         latent_length = round(duration_s * latents_per_second)
 
         if latents is not None:
+            if latents.ndim == 4:
+                # latents are of shape [B, C, L, M], need to be packed
+                latents = self._pack_audio_latents(latents)
             return latents.to(device=device, dtype=dtype), latent_length
 
         # TODO: confirm whether this logic is correct
@@ -1097,6 +1105,8 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMix
             self.transformer_spatial_patch_size,
             self.transformer_temporal_patch_size,
         )
+        prenorm_latents = latents
+        prenorm_audio_latents = audio_latents
         latents = self._denormalize_latents(
             latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
         )