replicate/latent-consistency-model

replicate/latent-consistency-model

Description: Run Latent Consistency Models on your Mac

Language: Python

License: MIT

Stars: 196

Forks: 13

Open issues: 9

Created: 2023-10-24T10:45:42Z

Pushed: 2023-11-10T22:28:49Z

Default branch: main

Fork: no

Archived: no

README:

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. |