| --- |
| 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), |
| ) |
| |
| |
| ``` |
|
|
