mlx-video

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

Installation

Install from source:

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 a 19B parameter video generation model from Lightricks.

Features

• Text-to-video (T2V) and Image-to-video (I2V) generation
• Three pipeline modes: Distilled, Dev, and Dev Two-Stage
• Synchronized audio-video generation (experimental)
• LoRA support (including HuggingFace repos)
• Prompt enhancement via Gemma
• 2x spatial upscaling for images and videos
• Optimized for Apple Silicon using MLX

Usage

Pipelines

mlx-video supports three pipeline types via the --pipeline flag:

Pipeline	Description	CFG	Stages	Speed
distilled (default)	Fixed sigma schedule, no CFG	No	2 (8+3 steps)	Fastest
dev	Dynamic sigmas, constant CFG	Yes	1 (30 steps)	Medium
dev-two-stage	Dev + LoRA refinement	Yes (stage 1)	2 (30+3 steps)	Slowest, highest quality

Text-to-Video

# Distilled (default) - fast, two-stage
uv run mlx_video.generate --prompt "Two dogs wearing sunglasses, cinematic, sunset" -n 97 --width 768

# Dev - single-stage with CFG
uv run mlx_video.generate --pipeline dev --prompt "A cinematic scene" --cfg-scale 3.0

# Dev two-stage - dev + LoRA refinement (highest quality)
uv run mlx_video.generate --pipeline dev-two-stage \
    --prompt "Two dogs of the poodle breed wearing sunglasses, close up, cinematic, sunset" \
    -n 145 --width 1024 --height 768 \
    --model-repo prince-canuma/LTX-2-dev \
    --cfg-scale 3.0 --lora-strength 0.8 \
    --enhance-prompt

Image-to-Video

# Distilled I2V
uv run mlx_video.generate --prompt "A person dancing" --image photo.jpg

# Dev I2V
uv run mlx_video.generate --pipeline dev --prompt "Waves crashing" --image beach.png --cfg-scale 3.5

Audio-Video (experimental)

uv run mlx_video.generate --prompt "Ocean waves crashing" --audio
uv run mlx_video.generate --pipeline dev --prompt "A jazz band playing" --audio --enhance-prompt

LoRA

LoRA weights can be loaded from a file, directory, or HuggingFace repo:

# From HuggingFace repo
uv run mlx_video.generate --pipeline dev-two-stage \
    --prompt "Camera dolly out of a forest" \
    --lora-path Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out \
    --lora-strength 1.0

# From local file
uv run mlx_video.generate --pipeline dev-two-stage \
    --prompt "A scene" \
    --lora-path ./my-lora/weights.safetensors

# From local directory (auto-detects .safetensors file)
uv run mlx_video.generate --pipeline dev-two-stage \
    --prompt "A scene" \
    --lora-path ./LTX-2-distilled/lora

Upscaling

# Upscale an image 2x
uv run mlx_video.upscale --input photo.png --output upscaled.png

# Upscale a video 2x
uv run mlx_video.upscale --input video.mp4 --output upscaled.mp4

# Upscale with refinement (higher quality, requires text prompt)
uv run mlx_video.upscale --input video.mp4 --output upscaled.mp4 --refine --prompt "A cinematic scene"

CLI Options

Option	Default	Description
--prompt, -p	(required)	Text description of the video
--pipeline	distilled	Pipeline type: distilled, dev, or dev-two-stage
--height, -H	512	Output height (divisible by 64 for two-stage, 32 for dev)
--width, -W	512	Output width (divisible by 64 for two-stage, 32 for dev)
--num-frames, -n	33	Number of frames (must be 1 + 8*k)
--seed, -s	42	Random seed for reproducibility
--fps	24	Frames per second
--output-path, -o	output.mp4	Output video path
--model-repo	Lightricks/LTX-2	HuggingFace model repository
--text-encoder-repo	None	Separate text encoder repo (if not in model repo)
--save-frames	false	Save individual frames as images
--enhance-prompt	false	Enhance prompt using Gemma
--image, -i	None	Conditioning image for I2V
--image-strength	1.0	Conditioning strength for I2V
--audio, -a	false	Enable synchronized audio generation
--tiling	auto	VAE tiling mode: auto, none, aggressive, conservative
--stream	false	Stream frames as they decode

Dev/Dev-Two-Stage options:

Option	Default	Description
--steps	30	Number of denoising steps
--cfg-scale	3.0	CFG guidance scale
--cfg-rescale	0.7	CFG rescale factor (reduces over-saturation)
--negative-prompt	(default)	Negative prompt for CFG
--apg	false	Use Adaptive Projected Guidance (more stable for I2V)

Dev-Two-Stage LoRA options:

Option	Default	Description
--lora-path	auto-detect	Path to LoRA file, directory, or HuggingFace repo
--lora-strength	1.0	LoRA merge strength

How It Works

Distilled Pipeline (default)

1. Stage 1: Generate at half resolution with 8 denoising steps (fixed sigmas)
2. Upsample: 2x spatial upsampling via LatentUpsampler
3. Stage 2: Refine at full resolution with 3 denoising steps
4. Decode: VAE decoder converts latents to RGB video

Dev Pipeline

1. Generate: Full resolution with configurable steps and constant CFG
2. Decode: VAE decoder converts latents to RGB video

Dev Two-Stage Pipeline

1. Stage 1: Dev denoising at half resolution with CFG
2. Upsample: 2x spatial upsampling via LatentUpsampler
3. Stage 2: Distilled refinement at full resolution with LoRA weights (3 steps, no CFG)
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 (19B parameters)
• Latent channels: 128
• Text encoder: Gemma 3 with 3840-dim output
• Audio: Synchronized audio-video with separate audio VAE and vocoder

License

MIT