Run latent consistency models on your Mac

Latent consistency models (LCMs) are based on Stable Diffusion, but they can generate images much faster, needing only 4 to 8 steps for a good image (compared to 25 to 50 steps). Simian Luo et al released the first Stable Diffusion distilled model. It’s distilled from the Dreamshaper fine-tune by incorporating classifier-free guidance into the model’s input.

You can run Latent Consistency Models in the cloud on Replicate, but it's also possible to run it locally.

Prerequisites

You’ll need:

• a Mac with an M1 or M2 chip
• 16GB RAM or more
• macOS 13.0 or higher
• Python 3.10 or above

Install

Run this to clone the repo:

git clone https://github.com/replicate/latent-consistency-model.git
cd latent-consistency-model

Set up a virtualenv to install the dependencies:

python3 -m pip install virtualenv
python3 -m virtualenv venv

Activate the virtualenv:

source venv/bin/activate

(You'll need to run this command again any time you want to run the script.)

Then, install the dependencies:

pip install -r requirements.txt

Run

The script will automatically download the SimianLuo/LCM_Dreamshaper_v7 (3.44 GB) and safety checker (1.22 GB) models from HuggingFace.

python main.py \
  "a beautiful apple floating in outer space, like a planet" \
  --steps 4 --width 512 --height 512

You’ll see an output like this:

Output image saved to: output/out-20231026-144506.png
Using seed: 48404
100%|███████████████████████████| 4/4 [00:00<00:00,  5.54it/s]

Options

Parameter	Type	Default	Description
prompt	str	N/A	A text string for image generation.
--width	int	512	The width of the generated image.
--height	int	512	The height of the generated image.
--steps	int	8	The number of inference steps.
--seed	int	None	Seed for random number generation.
--continuous	flag	False	Enable continuous generation.