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---
pipeline_tag: image-to-video
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
language:
- en
- de
- es
- fr
- ja
- ko
- zh
- it
- pt
license: other
license_name: ltx-2-open-weights-license
license_link: https://static.lightricks.com/legal/ltx-2-open-weights-license-0.X.pdf
library_name: diffusers
demo: https://app.ltx.studio/ltx-2-playground/i2v
---
# LTX-2 Model Card
This model card focuses on the LTX-2 model, codebase available [here](https://github.com/Lightricks/LTX-2).
LTX-2 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.
<img src="./media/trailer.gif" alt="trailer" width="512">
# Model Checkpoints
| Name | Notes |
|--------------------------------|----------------------------------------------------------------------------------------------------------------|
| ltx-2-19b-dev | The full model, flexible and trainable in bf16 |
| ltx-2-19b-dev-fp8 | The full model in fp8 quantization |
| ltx-2-19b-dev-fp4 | The full model in nvfp4 quantization |
| ltx-2-19b-distilled | The distilled version of the full model, 8 steps, CFG=1 |
| ltx-2-19b-distilled-lora-384 | A LoRA version of the distilled model applicable to the full model |
| ltx-2-spatial-upscaler-x2-1.0 | An x2 spatial upscaler for the ltx-2 latents, used in multi stage (multiscale) pipelines for higher resolution |
| ltx-2-temporal-upscaler-x2-1.0 | An x2 temporal upscaler for the ltx-2 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 is accessible right away via the following links:
- [LTX-Studio text-to-video](https://app.ltx.studio/ltx-2-playground/t2v)
- [LTX-Studio image-to-video](https://app.ltx.studio/ltx-2-playground/i2v)
# Run locally
## Direct use license
You can use the models - full, distilled, upscalers and any derivatives of the models - for purposes under the [license](https://static.lightricks.com/legal/ltx-2-open-weights-license-0.X.pdf).
## 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](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui).
## PyTorch codebase
The [LTX-2 codebase](https://github.com/Lightricks/LTX-2) 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
```bash
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](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/README.md) package.
## Diffusers 🧨
LTX-2 is supported in the [Diffusers Python library](https://huggingface.co/docs/diffusers/main/en/index) for image-to-video generation.
## 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](https://ltx.video/blog/how-to-prompt-for-ltx-2)
### 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.
## Image-to-video examples
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