diff --git a/src/diffusers/pipelines/ltx2/pipeline_ltx2.py b/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
index 7cbcca67d2..103e324e11 100644
--- a/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
+++ b/src/diffusers/pipelines/ltx2/pipeline_ltx2.py
@@ -47,24 +47,36 @@ EXAMPLE_DOC_STRING = """
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import LTXPipeline
-        >>> from diffusers.utils import export_to_video
+        >>> from diffusers import LTX2Pipeline
+        >>> from diffusers.pipelines.ltx2.export_utils import encode_video
 
-        >>> pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
-        >>> pipe.to("cuda")
+        >>> pipe = LTX2Pipeline.from_pretrained("Lightricks/LTX-2", torch_dtype=torch.bfloat16)
+        >>> pipe.enable_model_cpu_offload()
 
         >>> prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
         >>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
 
-        >>> video = pipe(
+        >>> frame_rate = 24.0
+        >>> video, audio = pipe(
         ...     prompt=prompt,
         ...     negative_prompt=negative_prompt,
-        ...     width=704,
-        ...     height=480,
-        ...     num_frames=161,
-        ...     num_inference_steps=50,
-        ... ).frames[0]
-        >>> export_to_video(video, "output.mp4", fps=24)
+        ...     width=768,
+        ...     height=512,
+        ...     frame_rate=frame_rate,
+        ...     num_frames=121,
+        ...     output_type="np",
+        ...     return_dict=False,
+        ... )
+        >>> video = (video * 255).round().astype("uint8")
+        >>> video = torch.from_numpy(video)
+
+        >>> encode_video(
+        ...     video[0],
+        ...     fps=frame_rate,
+        ...     audio=audio[0].float().cpu(),
+        ...     audio_sample_rate=pipe.vocoder.config.output_sampling_rate,  # should be 24000
+        ...     output_path="video.mp4",
+        ... )
         ```
 """
 
diff --git a/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py b/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py
index 0a707806ce..df0faa2b07 100644
--- a/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py
+++ b/src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py
@@ -48,11 +48,12 @@ EXAMPLE_DOC_STRING = """
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import LTX2ImageToVideoPipeline
-        >>> from diffusers.utils import export_to_video, load_image
+        >>> from diffusers import LTX2Pipeline
+        >>> from diffusers.pipelines.ltx2.export_utils import encode_video
+        >>> from diffusers.utils import load_image
 
-        >>> pipe = LTX2ImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video-2", torch_dtype=torch.bfloat16)
-        >>> pipe.to("cuda")
+        >>> pipe = LTX2ImageToVideoPipeline.from_pretrained("Lightricks/LTX-2", torch_dtype=torch.bfloat16)
+        >>> pipe.enable_model_cpu_offload()
 
         >>> image = load_image(
         ...     "https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
@@ -60,16 +61,28 @@ EXAMPLE_DOC_STRING = """
         >>> prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background."
         >>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
 
+        >>> frame_rate = 24.0
         >>> video = pipe(
         ...     image=image,
         ...     prompt=prompt,
         ...     negative_prompt=negative_prompt,
-        ...     width=704,
-        ...     height=480,
+        ...     width=768,
+        ...     height=512,
         ...     num_frames=121,
-        ...     num_inference_steps=40,
-        ... ).frames[0]
-        >>> export_to_video(video, "output.mp4", fps=24)
+        ...     frame_rate=frame_rate,
+        ...     output_type="np",
+        ...     return_dict=False,
+        ... )
+        >>> video = (video * 255).round().astype("uint8")
+        >>> video = torch.from_numpy(video)
+
+        >>> encode_video(
+        ...     video[0],
+        ...     fps=frame_rate,
+        ...     audio=audio[0].float().cpu(),
+        ...     audio_sample_rate=pipe.vocoder.config.output_sampling_rate,  # should be 24000
+        ...     output_path="video.mp4",
+        ... )
         ```
 """