---
license: mit
---
use normal pipeline to run it
example:
```

from diffusers import LTX2Pipeline
from diffusers.pipelines.ltx2.export_utils import encode_video
repo= 'smthem/ltx-2-19b-dev-diffusers-4bit'


### text_encoder
from transformers import Gemma3ForConditionalGeneration
text_encoder = Gemma3ForConditionalGeneration.from_pretrained(
                  repo,
                  subfolder="text_encoder",
                )

### transformer
transformer_4bit = AutoModel.from_pretrained(
                    repo,
                    subfolder="transformer",
                )
pipeline = LTX2Pipeline.from_pretrained("smthem/ltx-2-19b-dev-diffusers-test",transformer=transformer_4bit,text_encoder=text_encoder,torch_dtype=torch.float16,)
pipeline.enable_model_cpu_offload()

prompt='A video of a dog dancing to energetic electronic dance music'
negative_prompt="blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
            "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
            "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
            "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
            "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
            "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
            "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
            "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
            "off-sync audio,incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
            "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
            "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."


video, audio = pipeline(
      prompt=prompt,
      negative_prompt=negative_prompt,
      height=512,
      width=768,
      num_frames=121,
      frame_rate=25,
      num_inference_steps=20,
      guidance_scale=guidance_scale,
      generator=torch.Generator(device="cuda").manual_seed(42),
      output_type="np",
      return_dict=False,
  )

# Convert video to uint8 (but keep as NumPy array)
video = (video * 255).round().astype("uint8")
video = torch.from_numpy(video)

encode_video(
      video[0],
      fps=args.frame_rate,
      audio=audio[0].float().cpu(),
      audio_sample_rate=pipeline.vocoder.config.output_sampling_rate,  # should be 24000
      output_path=os.path.join(args.output_dir, args.output_filename),
  )


```