LTX-2.3-FP8

This is an FP8 / INT8 quantized version of LTX-2.3. <br>

• Optimized for efficient inference with reduced memory footprint. Same-seed first-frame LPIPS vs the bf16 model (lower is better): 0.42 INT8, 0.50 FP8. The 22B distilled DiT is more quant-sensitive than base LTX-2 (0.24 / 0.26): frames stay sharp and well-formed but composition drifts more (see samples). Each checkpoint is verified bit-identical to quantizing the dense weights on the fly (4186 tensors compared, zero mismatches), so the hosted files add no error over runtime int8/fp8 quantization.

<div> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> </div>

---

Samples

Prompt: "cute sloth typing on a computer" (first frames)

<table> <tr> <td align="center"><b>INT8</b></td> <td align="center"><b>INT8</b></td> </tr> <tr> <td><img src="samples/int8_1.png" width="300"></td> <td><img src="samples/int8_2.png" width="300"></td> </tr> <tr> <td align="center"><b>FP8</b></td> <td align="center"><b>FP8</b></td> </tr> <tr> <td><img src="samples/fp8_1.png" width="300"></td> <td><img src="samples/fp8_2.png" width="300"></td> </tr> </table>

---

LTX-2.3 Model Card

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.

Model Checkpoints

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}
}

---

Quantized transformer checkpoints (this repo)

This repo adds pre-quantized diffusion transformer checkpoints for Lightricks/LTX-2.3, built with torchao dynamic activation quantization from the dense bf16 transformer of the diffusers-format release (Lightricks/LTX-2.3). The official model card above is unchanged from the source repo.

Files:

• LTX-2.3-INT8.pt (19.3 GB)
• LTX-2.3-FP8.pt (19.1 GB)

Details:

• int8: Int8DynamicActivationInt8WeightConfig (per-token activation, per-channel weight, torch._int_mm).
• fp8: Float8DynamicActivationFloat8WeightConfig with PerRow granularity (e4m3, torch._scaled_mm). The loader must floor the dynamic activation scale (activation_value_lb=1e-12 on torchao 0.13+) so all-zero activation token rows cannot produce a zero scale.
• Loading a checkpoint is bit-identical to quantizing the dense bf16 transformer on the fly; the checkpoint skips the dense load and quantize step.
• Dual-expert pipelines (Wan2.2 A14B) carry one file per expert and scheme; the -2 suffix is the transformer_2 expert. A partial pair never loads (all-or-none).
• Built from the official single-file ltx-2.3-22b-distilled.safetensors via the exact runtime assembly path (load_ltx23_transformer key renames + config overrides), so offline quantization matches what Studio produces at load time.
• Bit-identity proof: every tensor of each checkpoint equals a fresh on-the-fly quantize_ of the official weights (int8: 4186 tensors, 1350 quantized, 0 mismatches; fp8: 4186 tensors, 1360 quantized, 0 mismatches). Generation is deterministic for a loaded pipeline (same seed twice: max frame diff 0.0 across 17 frames).
• Validated against same-seed dense bf16 clips (LPIPS-vgg, PSNR, non-finite and black-frame checks) on torch 2.10 and torchao 0.17.

Samples

Prompt: "cute sloth typing on a computer" (first frames, family default settings, seeds 0-2).

int8

<img src="samples/int8_1.png" width="260"> <img src="samples/int8_2.png" width="260"> <img src="samples/int8_3.png" width="260">

fp8

<img src="samples/fp8_1.png" width="260"> <img src="samples/fp8_2.png" width="260"> <img src="samples/fp8_3.png" width="260">