/Real-Time-Latent-Consistency-Model

Demo showcasing ~real-time Latent Consistency Model pipeline with Diffusers and a MJPEG stream server

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Real-Time Latent Consistency Model Image-to-Image ControlNet
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Real-Time Latent Consistency Model

I've made several changes to the main branch for this Repo.

  1. I've added a Desktop Capture feature for Img2Img ControlNet/Canny.
  2. I've added several Windows Bat scripts to get you started more easily.

This demo showcases Latent Consistency Model (LCM) using Diffusers with a MJPEG stream server.

Running Locally

You need CUDA and Python 3.10, Mac with an M1/M2/M3 chip or Intel Arc GPU

TIMEOUT: limit user session timeout
SAFETY_CHECKER: disabled if you want NSFW filter off (Currently Disabled for Img2Img ControlNet/Canny). MAX_QUEUE_SIZE: limit number of users on current app instance
TORCH_COMPILE: enable if you want to use torch compile for faster inference works well on A100 GPUs

Install

python -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

OR

Run included "_Step_1_Install.bat"

SSL (Optional)

If you're running locally and want to test it on Mobile Safari, the webserver needs to be served over HTTPS. This requires OpenSSL to be installed on your system. It is only needed if you want to access the RT-LCM web UI remotely.

openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload --log-level info --ssl-certfile=certificate.pem --ssl-keyfile=key.pem

OR

Run included "_Step_2_Optional_Create_SSL_Needed_for_Remote_access.bat"

LCM

Image to Image ControlNet Canny

Based pipeline from taabata

uvicorn "app-controlnet:app" --host 0.0.0.0 --port 7860 --reload

OR Run "_Step_3a_Start_RTLCM-With_SSL_ControlNet.bat" if you are using an SSL Cert.

Run "_Step_3b_Start_RTLCM-Without_SSL_ControlNet.bat" if you are not using an SSL Cert.

Img2Img w/ ControlNet is the only mode I've updated with "Capture Desktop" option at this point.

Image to Image

uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload

Text to Image

uvicorn "app-txt2img:app" --host 0.0.0.0 --port 7860 --reload

LCM + LoRa

Using LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. Learn more here or technical report

Image to Image ControlNet Canny LoRa

uvicorn "app-controlnetlora:app" --host 0.0.0.0 --port 7860 --reload

Text to Image

uvicorn "app-txt2imglora:app" --host 0.0.0.0 --port 7860 --reload

Setting environment variables

TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload

Docker

You need NVIDIA Container Toolkit for Docker

docker build -t lcm-live .
docker run -ti -p 7860:7860 --gpus all lcm-live

or with environment variables

docker run -ti -e TIMEOUT=0 -e SAFETY_CHECKER=False -p 7860:7860 --gpus all lcm-live

Demo on Hugging Face

https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model

lcm-real.mp4