Model reference · open weights

LTX-2.3

Available as managed deployment Licence fee Video zachyuan · community Image→video 1 variants 703 dl/mo

LTX-2.3 is an open-weight video model from zachyuan. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byzachyuan
TypeVideo models
TaskImage→video
Runs withdiffusers
Released2026-04-01
Popularity703 downloads / month
LicenceCommercial licence needed

About

What LTX-2.3 is

This model card focuses on the LTX-2.3 model, which is a significant update to the LTX-2 model with improved audio and visual quality as well as enhanced prompt adherence. LTX-2 was presented in the paper LTX-2: Efficient Joint Audio-Visual Foundation Model.

💻💻 If you want to dive in right to the code - it is available here. 💾💾

LTX-2.3 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution.

Read the full model card

Model Checkpoints

NameNotes
ltx-2.3-22b-devThe full model, flexible and trainable in bf16
ltx-2.3-22b-distilledThe distilled version of the full model, 8 steps, CFG=1
ltx-2.3-22b-distilled-lora-384A LoRA version of the distilled model applicable to the full model
ltx-2.3-spatial-upscaler-x2-1.1An x2 spatial upscaler for the ltx-2.3 latents, used in multi stage (multiscale) pipelines for higher resolution
ltx-2.3-spatial-upscaler-x1.5-1.0An x1.5 spatial upscaler for the ltx-2.3 latents, used in multi stage (multiscale) pipelines for higher resolution
ltx-2.3-temporal-upscaler-x2-1.0An x2 temporal upscaler for the ltx-2.3 latents, used in multi stage (multiscale) pipelines for higher FPS

Model Details

  • Developed by: Lightricks
  • Model type: Diffusion-based audio-video foundation model
  • Language(s): English

Online demo

LTX-2.3 is accessible right away via the API Playground.

Run locally

Direct use license

You can use the models - full, distilled, upscalers and any derivatives of the models - for purposes under the license.

ComfyUI

We recommend you use the built-in LTXVideo nodes that can be found in the ComfyUI Manager. For manual installation information, please refer to our documentation site.

PyTorch codebase

The LTX-2 codebase is a monorepo with several packages. From model definition in 'ltx-core' to pipelines in 'ltx-pipelines' and training capabilities in 'ltx-trainer'. The codebase was tested with Python >=3.12, CUDA version >12.7, and supports PyTorch ~= 2.7.

Installation

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

# From the repository root
uv sync
source .venv/bin/activate

Inference

To use our model, please follow the instructions in our ltx-pipelines package.

Diffusers 🧨

LTX-2.3 support in the Diffusers Python library is coming soon!

General tips:

  • Width & height settings must be divisible by 32. Frame count must be divisible by 8 + 1.
  • In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input should be padded with -1 and then cropped to the desired resolution and number of frames.
  • For tips on writing effective prompts, please visit our Prompting guide

Limitations

  • This model is not intended or able to provide factual information.
  • As a statistical model this checkpoint might amplify existing societal biases.
  • The model may fail to generate videos that matches the prompts perfectly.
  • Prompt following is heavily influenced by the prompting-style.
  • The model may generate content that is inappropriate or offensive.
  • When generating audio without speech, the audio may be of lower quality.

Train the model

The base (dev) model is fully trainable.

It's extremely easy to reproduce the LoRAs and IC-LoRAs we publish with the model by following the instructions on the LTX-2 Trainer Readme.

Training for motion, style or likeness (sound+appearance) can take less than an hour in many settings.

Citation

@article{hacohen2025ltx2,
  title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
  author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and Bitterman, Yaki and Kvochko, Andrew and Berkowitz, Avishai and Shalem, Daniel and Lifschitz, Daphna and Moshe, Dudu and Porat, Eitan and Richardson, Eitan and Guy Shiran and Itay Chachy and Jonathan Chetboun and Michael Finkelson and Michael Kupchick and Nir Zabari and Nitzan Guetta and Noa Kotler and Ofir Bibi and Ori Gordon and Poriya Panet and Roi Benita and Shahar Armon and Victor Kulikov and Yaron Inger and Yonatan Shiftan and Zeev Melumian and Zeev Farbman},
  journal={arXiv preprint arXiv:2601.03233},
  year={2025}
}

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How video models work

Prompt / imagestart pointTemporal diffusionframes over timeVideoMP4 clipA video model generates a sequence of coherent frames from your prompt or a starting image.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys zachyuan-ltx-2-3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (zachyuan-ltx-2-3 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"zachyuan-ltx-2-3","prompt":"a drone shot over a forest"}'

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