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):

pip install git+https://github.com/Blaizzy/mlx-video.git

Option 2: Install with uv (ultra-fast package manager, optional):

uv pip install git+https://github.com/Blaizzy/mlx-video.git

Supported Models

LTX-2

LTX-2 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

# 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

Converting weights:

Pre-converted weights are available on HuggingFace (LTX-2-distilled, LTX-2-dev, LTX-2.3-distilled, LTX-2.3-dev), or convert from the original Lightricks checkpoint:

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