# mlx-video

MLX-Video is the best package for inference and finetuning of Image-Video-Audio generation models on your Mac using MLX.

## Installation

### Option 1: Install with pip (requires git):
```bash
pip install git+https://github.com/Blaizzy/mlx-video.git
```

### Option 2: Install with uv (ultra-fast package manager, optional):
```bash
uv pip install git+https://github.com/Blaizzy/mlx-video.git
```

## Supported Models

### LTX-2
[LTX-2](https://huggingface.co/Lightricks/LTX-Video) is 19B parameter video generation model from Lightricks

## Features

- Text-to-video generation with the LTX-2 19B DiT model
- Two-stage generation pipeline for high-quality output
- 2x spatial upscaling for images and videos
- Optimized for Apple Silicon using MLX


## Usage

> **ℹ️ Info:** Currently, only the distilled variant is supported. Full LTX-2 feature support is coming soon.

### Text-to-Video Generation

```bash
# Text-to-Video (distilled, fastest)
uv run mlx_video.generate --prompt "Two dogs wearing sunglasses, cinematic, sunset" -n 97 --width 768

# Image-to-Video
uv run mlx_video.generate --prompt "A person dancing" --image photo.jpg

# Audio-to-Video
uv run mlx_video.generate --audio-file music.wav --prompt "A band playing music"

# Dev pipeline with CFG (higher quality)
uv run mlx_video.generate --pipeline dev --prompt "A cinematic scene" --cfg-scale 3.0

# Dev two-stage HQ (highest quality)
uv run mlx_video.generate --pipeline dev-two-stage-hq \
    --prompt "A cinematic scene of ocean waves at golden hour" \
    --model-repo prince-canuma/LTX-2-dev
```

<img src="https://github.com/Blaizzy/mlx-video/raw/main/examples/poodles.gif" width="512" alt="Poodles demo">

**Converting weights:**

Pre-converted weights are available on HuggingFace ([LTX-2-distilled](https://huggingface.co/prince-canuma/LTX-2-distilled), [LTX-2-dev](https://huggingface.co/prince-canuma/LTX-2-dev), [LTX-2.3-distilled](https://huggingface.co/prince-canuma/LTX-2.3-distilled), [LTX-2.3-dev](https://huggingface.co/prince-canuma/LTX-2.3-dev)), or convert from the original Lightricks checkpoint:

```bash
python -m mlx_video.generate \
    --prompt "Ocean waves crashing on a beach at sunset" \
    --height 768 \
    --width 768 \
    --num-frames 65 \
    --seed 123 \
    --output my_video.mp4
```

### CLI Options

| Option | Default | Description |
|--------|---------|-------------|
| `--prompt`, `-p` | (required) | Text description of the video |
| `--height`, `-H` | 512 | Output height (must be divisible by 64) |
| `--width`, `-W` | 512 | Output width (must be divisible by 64) |
| `--num-frames`, `-n` | 100 | Number of frames |
| `--seed`, `-s` | 42 | Random seed for reproducibility |
| `--fps` | 24 | Frames per second |
| `--output`, `-o` | output.mp4 | Output video path |
| `--save-frames` | false | Save individual frames as images |
| `--model-repo` | Lightricks/LTX-2 | HuggingFace model repository |

## How It Works

The pipeline uses a two-stage generation process:

1. **Stage 1**: Generate at half resolution (e.g., 384x384) with 8 denoising steps
2. **Upsample**: 2x spatial upsampling via LatentUpsampler
3. **Stage 2**: Refine at full resolution (e.g., 768x768) with 3 denoising steps
4. **Decode**: VAE decoder converts latents to RGB video

## Requirements

- macOS with Apple Silicon
- Python >= 3.11
- MLX >= 0.22.0

## Model Specifications

- **Transformer**: 48 layers, 32 attention heads, 128 dim per head
- **Latent channels**: 128
- **Text encoder**: Gemma 3 with 3840-dim output
- **RoPE**: Split mode with double precision

## Project Structure

```
mlx_video/
├── generate.py             # Video generation pipeline
├── convert.py              # Weight conversion (PyTorch -> MLX)
├── postprocess.py          # Video post-processing utilities
├── utils.py                # Helper functions
└── models/
    └── ltx/
        ├── ltx.py          # Main LTXModel (DiT transformer)
        ├── config.py       # Model configuration
        ├── transformer.py  # Transformer blocks
        ├── attention.py    # Multi-head attention with RoPE
        ├── text_encoder.py # Text encoder
        ├── upsampler.py    # 2x spatial upsampler
        └── video_vae/      # VAE encoder/decoder
```

## License

MIT