title	emoji	colorFrom	colorTo	sdk	pinned	suggested_hardware	disable_embedding
Real-Time Latent Consistency Model Image-to-Image ControlNet	🖼️🖼️	gray	indigo	docker	false	a10g-small	true

Real-Time Latent Consistency Model

This demo showcases Latent Consistency Model (LCM) using Diffusers with a MJPEG stream server. You can read more about LCM + LoRAs with diffusers here.

You need a webcam to run this demo. 🤗

See a collecting with live demos here

Running Locally

You need CUDA and Python 3.10, Node > 19, Mac with an M1/M2/M3 chip or Intel Arc GPU

Install

python -m venv venv
source venv/bin/activate
pip3 install -r server/requirements.txt
cd frontend && npm install && npm run build && cd ..
python server/main.py --reload --pipeline img2imgSDTurbo 

Don't forget to fuild the frontend!!!

cd frontend && npm install && npm run build && cd ..

Pipelines

You can build your own pipeline following examples here here,

LCM

Image to Image

python server/main.py --reload --pipeline img2img 

LCM

Text to Image

python server/main.py --reload --pipeline txt2img 

Image to Image ControlNet Canny

python server/main.py --reload --pipeline controlnet 

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

python server/main.py --reload --pipeline controlnetLoraSD15

or SDXL, note that SDXL is slower than SD15 since the inference runs on 1024x1024 images

python server/main.py --reload --pipeline controlnetLoraSDXL

Text to Image

python server/main.py --reload --pipeline txt2imgLora

python server/main.py --reload --pipeline txt2imgLoraSDXL

Available Pipelines

LCM

img2img
txt2img
controlnet
txt2imgLora
controlnetLoraSD15

SD15

controlnetLoraSDXL
txt2imgLoraSDXL

SDXL Turbo

img2imgSDXLTurbo
controlnetSDXLTurbo

SDTurbo

img2imgSDTurbo
controlnetSDTurbo

Segmind-Vega

controlnetSegmindVegaRT
img2imgSegmindVegaRT

Setting environment variables

• --host: Host address (default: 0.0.0.0)
• --port: Port number (default: 7860)
• --reload: Reload code on change
• --max-queue-size: Maximum queue size (optional)
• --timeout: Timeout period (optional)
• --safety-checker: Enable Safety Checker (optional)
• --torch-compile: Use Torch Compile
• --use-taesd / --no-taesd: Use Tiny Autoencoder
• --pipeline: Pipeline to use (default: "txt2img")
• --ssl-certfile: SSL Certificate File (optional)
• --ssl-keyfile: SSL Key File (optional)
• --debug: Print Inference time
• --compel: Compel option
• --sfast: Enable Stable Fast
• --onediff: Enable OneDiff

If you run using bash build-run.sh you can set PIPELINE variables to choose the pipeline you want to run

PIPELINE=txt2imgLoraSDXL bash build-run.sh

and setting environment variables

TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 python server/main.py --reload --pipeline txt2imgLoraSDXL

If you're running locally and want to test it on Mobile Safari, the webserver needs to be served over HTTPS, or follow this instruction on my comment

openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
python server/main.py --reload --ssl-certfile=certificate.pem --ssl-keyfile=key.pem

Docker

You need NVIDIA Container Toolkit for Docker, defaults to `controlnet``

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

reuse models data from host to avoid downloading them again, you can change ~/.cache/huggingface to any other directory, but if you use hugingface-cli locally, you can share the same cache

docker run -ti -p 7860:7860 -e HF_HOME=/data -v ~/.cache/huggingface:/data  --gpus all lcm-live

or with environment variables

docker run -ti -e PIPELINE=txt2imgLoraSDXL -p 7860:7860 --gpus all lcm-live

Demo on Hugging Face

• radames/Real-Time-Latent-Consistency-Model
• radames/Real-Time-SD-Turbo
• latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5
• latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5
• radames/Real-Time-Latent-Consistency-Model-Text-To-Image

lcm-real.mp4