Create README.md

README.md

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+---
+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
+
+```