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LTX-2

LTX-2 is the first DiT-based audio-video foundation model that contains all core capabilities of modern video generation in one model: synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.

🚀 Quick Start

Clone the repo

git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2

Download the relevant models or use the Hugging Face CLI

hf auth login
hf download Lightricks/LTX-2.3 \
    ltx-2.3-22b-distilled-1.1.safetensors ltx-2.3-spatial-upscaler-x2-1.1.safetensors --local-dir models/ltx-2.3
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b

If you get a 401/403, accept the model terms on Hugging Face and log in with a Read token (fine-grained tokens need the "read gated repos" scope enabled).

Generate

uv run python -m ltx_pipelines.distilled \
    --distilled-checkpoint-path models/ltx-2.3/ltx-2.3-22b-distilled-1.1.safetensors \
    --spatial-upsampler-path    models/ltx-2.3/ltx-2.3-spatial-upscaler-x2-1.1.safetensors \
    --gemma-root models/gemma-3-12b \
    --seed 42 \
    --output-path output.mp4 \
    --prompt "A medium close-up shot features a Caucasian man with a beard, wearing a green and white baseball cap without any letters on the front, and a light blue shirt over a white t-shirt. He is positioned in the center of the frame, looking intently directly at the camera, his eyes focused on camera. His facial expression is one of deep concentration, with his brow slightly raised. As he looks straight at the camera, a quick sniff sound is heard, and then he speaks with a deep male voice and a satisfied tone, saying, 'I think it's so good.' The camera remains static throughout, maintaining a shallow depth of field, which keeps the man in sharp focus while the background is softly blurred, showing a beige wall behind him. After a brief pause, another short, audible sniff is heard. The man then continues to speak, his voice maintaining the same quality, as he states, 'So good. So good.' He elaborates further, emphasizing his point with a final statement, 'This got to be, it's got to be the best tool I've ever seen.'"

In cases of GPU memory constraints, consider --quantization fp8-cast --offload {cpu, disk}. See additional flags.

This uses the distilled model and pipeline for fast results. For better quality or other capabilities, see Models and Pipelines.

Full Model List

For pipelines beyond the quickstart, download the relevant models from the LTX-2.3 HuggingFace repository:

LTX-2.3 Model Checkpoint (choose and download one of the following)

Spatial Upscaler - Required for current two-stage pipeline implementations in this repository

Temporal Upscaler - Supported by the model and will be required for future pipeline implementations

ltx-2.3-temporal-upscaler-x2-1.0.safetensors - Download

Distilled LoRA - Required for current two-stage pipeline implementations in this repository (except DistilledPipeline, ICLoraPipeline, and LipDubPipeline)

ltx-2.3-22b-distilled-lora-384-1.1.safetensors - Download

Gemma Text Encoder (download all assets from the repository)

Gemma 3

LoRAs

Available Pipelines

TI2VidTwoStagesPipeline - Production-quality text/image-to-video with 2x upsampling (recommended)
TI2VidTwoStagesHQPipeline - Same two-stage flow as above but uses the res_2s second-order sampler (fewer steps, better quality)
TI2VidOneStagePipeline - Single-stage generation for quick prototyping
DistilledPipeline - Fastest inference with 8 predefined sigmas
ICLoraPipeline - Video-to-video and image-to-video transformations (uses distilled model.)
KeyframeInterpolationPipeline - Interpolate between keyframe images
A2VidPipelineTwoStage - Audio-to-video generation conditioned on an input audio file
RetakePipeline - Regenerate a specific time region of an existing video
HDRICLoraPipeline - Video-to-video with HDR output (linear float frames via LogC3 inverse decode, suitable for EXR export and tonemapping)
LipDubPipeline - Lip dubbing, rephrasing, matching speaker identity (distilled model, single IC-LoRA, Two stages).

⚡ Optimization Tips

Use DistilledPipeline - Fastest inference with only 8 predefined sigmas (8 steps stage 1, 4 steps stage 2)
Enable FP8 quantization - Enables lower memory footprint: --quantization fp8-cast (C