---
tags:
- ltx-video
- text-to-video
- image-to-video
pinned: true
language:
- en
---

# LTX-Video Model Card
This model card focuses on the model associated with the LTX-Video model, codebase available [here](https://github.com/Lightricks/LTX-Video).


## Model Details
- **Developed by:** Lightricks
- **Model type:** Diffusion-based text-to-video and image-to-video generation model
- **Language(s):** English
- **Model Description:** LTX-Video is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content.


## Usage

### Setup
The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.

#### Installation

```bash
git clone https://github.com/LightricksResearch/LTX-Video.git
cd ltx_video-core

# create env
python -m venv env
source env/bin/activate
python -m pip install -e .\[inference-script\]
```

Then, download the model from [Hugging Face](https://huggingface.co/Lightricks/LTX-Video) 

```python
from huggingface_hub import snapshot_download

model_path = 'PATH'   # The local directory to save downloaded checkpoint
snapshot_download("Lightricks/LTX-Video", local_dir=model_path, local_dir_use_symlinks=False, repo_type='model')
```

### Inference

#### Inference Code

To use our model, please follow the inference code in `inference.py` at [https://github.com/LightricksResearch/LTX-Video/blob/main/inference.py]():

For text-to-video generation:

```bash
python inference.py --ckpt_dir 'PATH' --prompt "PROMPT" --height HEIGHT --width WIDTH
```

For image-to-video generation:

```python
python inference.py --ckpt_dir 'PATH' --prompt "PROMPT" --input_image_path IMAGE_PATH --height HEIGHT --width WIDTH

```