--- language: - en - de - es - fr - ja - ko - zh - it - pt library_name: diffusers license: other license_name: ltx-2-community-license-agreement license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE pipeline_tag: image-to-video arxiv: 2601.03233 tags: - image-to-video - text-to-video - video-to-video - image-text-to-video - audio-to-video - text-to-audio - video-to-audio - audio-to-audio - text-to-audio-video - image-to-audio-video - image-text-to-audio-video - ltx-2 - ltx-video - ltxv - lightricks pinned: true base_model: - Lightricks/LTX-2 --- # LTX-2-19B-Distilled **Distilled LTX-2 text-to-video & image-to-video generation model (Diffusers)** This repository hosts the **distilled checkpoint** of the LTX-2 19B model optimized for **fast video generation** with fewer diffusion steps. It supports both **text-to-video** and **image-to-video** generation using Hugging Face’s 🤗 Diffusers pipelines. | Pipeline | Output | |:-------:|:--------:| | Text-to-Video |  | | Image-to-Video |  | ## Text-to-Video Usage ```py import torch from diffusers.pipelines.ltx2 import LTX2Pipeline, LTX2LatentUpsamplePipeline from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES from diffusers.pipelines.ltx2.export_utils import encode_video device = "cuda" width = 768 height = 512 random_seed = 42 generator = torch.Generator(device).manual_seed(random_seed) model_path = "rootonchair/LTX-2-19b-distilled" pipe = LTX2Pipeline.from_pretrained( model_path, torch_dtype=torch.bfloat16 ) pipe.enable_sequential_cpu_offload(device=device) prompt = "A beautiful sunset over the ocean" negative_prompt = "shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static." frame_rate = 24.0 video_latent, audio_latent = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_frames=121, frame_rate=frame_rate, num_inference_steps=8, sigmas=DISTILLED_SIGMA_VALUES, guidance_scale=1.0, generator=generator, output_type="latent", return_dict=False, ) latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained( model_path, subfolder="latent_upsampler", torch_dtype=torch.bfloat16, ) upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler) upsample_pipe.enable_model_cpu_offload(device=device) upscaled_video_latent = upsample_pipe( latents=video_latent, output_type="latent", return_dict=False, )[0] video, audio = pipe( latents=upscaled_video_latent, audio_latents=audio_latent, prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=3, noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[0], # renoise with first sigma value https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/distilled.py#L178 sigmas=STAGE_2_DISTILLED_SIGMA_VALUES, generator=generator, guidance_scale=1.0, 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, output_path="ltx2_distilled_sample.mp4", ) ``` ## Image-to-Video Usage ```py import torch from diffusers.pipelines.ltx2 import LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES from diffusers.pipelines.ltx2.export_utils import encode_video from diffusers.utils import load_image device = "cuda" width = 768 height = 512 random_seed = 45 generator = torch.Generator(device).manual_seed(random_seed) model_path = "rootonchair/LTX-2-19b-distilled" pipe = LTX2ImageToVideoPipeline.from_pretrained( model_path, torch_dtype=torch.bfloat16 ) pipe.enable_sequential_cpu_offload(device=device) image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" ) prompt = "An astronaut hatches from a fragile egg on the surface of the Moon, the shell cracking and peeling apart in gentle low-gravity motion. Fine lunar dust lifts and drifts outward with each movement, floating in slow arcs before settling back onto the ground. The astronaut pushes free in a deliberate, weightless motion, small fragments of the egg tumbling and spinning through the air. In the background, the deep darkness of space subtly shifts as stars glide with the camera's movement, emphasizing vast depth and scale. The camera performs a smooth, cinematic slow push-in, with natural parallax between the foreground dust, the astronaut, and the distant starfield. Ultra-realistic detail, physically accurate low-gravity motion, cinematic lighting, and a breath-taking, movie-like shot." negative_prompt = "shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static." frame_rate = 24.0 video_latent, audio_latent = pipe( image=image, prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_frames=121, frame_rate=frame_rate, num_inference_steps=8, sigmas=DISTILLED_SIGMA_VALUES, guidance_scale=1.0, generator=generator, output_type="latent", return_dict=False, ) latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained( model_path, subfolder="latent_upsampler", torch_dtype=torch.bfloat16, ) upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler) upsample_pipe.enable_model_cpu_offload(device=device) upscaled_video_latent = upsample_pipe( latents=video_latent, output_type="latent", return_dict=False, )[0] video, audio = pipe( image=image, latents=upscaled_video_latent, audio_latents=audio_latent, prompt=prompt, negative_prompt=negative_prompt, width=width * 2, height=height * 2, num_inference_steps=3, noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[0], sigmas=STAGE_2_DISTILLED_SIGMA_VALUES, generator=generator, guidance_scale=1.0, 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, output_path="image_ltx2_distilled_sample.mp4", ) ```
