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---
title: Real-Time Latent Consistency Model Image-to-Image ControlNet
emoji: 🖼️🖼️
colorFrom: gray
colorTo: indigo
sdk: docker
pinned: false
suggested_hardware: a10g-small
---
# Real-Time Latent Consistency Model
This demo showcases [Latent Consistency Model (LCM)](https://latent-consistency-models.github.io/) using [Diffusers](https://huggingface.co/docs/diffusers/using-diffusers/lcm) with a MJPEG stream server. You can read more about LCM + LoRAs with diffusers [here](https://huggingface.co/blog/lcm_lora).
You need a webcam to run this demo. 🤗
See a collecting with live demos [here](https://huggingface.co/collections/latent-consistency/latent-consistency-model-demos-654e90c52adb0688a0acbe6f)
## Running Locally
You need CUDA and Python 3.10, Node > 19, Mac with an M1/M2/M3 chip or Intel Arc GPU
## Install
```bash
python -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt
cd frontend && npm install && npm run build && cd ..
# fastest pipeline
python run.py --reload --pipeline img2imgSD21Turbo
```
# Pipelines
You can build your own pipeline following examples here [here](pipelines),
don't forget to fuild the frontend first
```bash
cd frontend && npm install && npm run build && cd ..
```
# LCM
### Image to Image
```bash
python run.py --reload --pipeline img2img
```
# LCM
### Text to Image
```bash
python run.py --reload --pipeline txt2img
```
### Image to Image ControlNet Canny
```bash
python run.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](https://huggingface.co/blog/lcm_lora) or [technical report](https://huggingface.co/papers/2311.05556)
### Image to Image ControlNet Canny LoRa
```bash
python run.py --reload --pipeline controlnetLoraSD15
```
or SDXL, note that SDXL is slower than SD15 since the inference runs on 1024x1024 images
```bash
python run.py --reload --pipeline controlnetLoraSDXL
```
### Text to Image
```bash
python run.py --reload --pipeline txt2imgLora
```
or
```bash
python run.py --reload --pipeline txt2imgLoraSDXL
```
### Setting environment variables
`TIMEOUT`: limit user session timeout
`SAFETY_CHECKER`: disabled if you want NSFW filter off
`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
`USE_TAESD`: enable if you want to use Autoencoder Tiny
If you run using `bash build-run.sh` you can set `PIPELINE` variables to choose the pipeline you want to run
```bash
PIPELINE=txt2imgLoraSDXL bash build-run.sh
```
and setting environment variables
```bash
TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 python run.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](https://github.com/radames/Real-Time-Latent-Consistency-Model/issues/17#issuecomment-1811957196)
```bash
openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
python run.py --reload --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
```
## Docker
You need NVIDIA Container Toolkit for Docker, defaults to `controlnet``
```bash
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
```bash
docker run -ti -p 7860:7860 -e HF_HOME=/data -v ~/.cache/huggingface:/data --gpus all lcm-live
```
or with environment variables
```bash
docker run -ti -e PIPELINE=txt2imgLoraSDXL -p 7860:7860 --gpus all lcm-live
```
# Development Mode
```bash
python run.py --reload
```
# Demo on Hugging Face
https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model
https://github.com/radames/Real-Time-Latent-Consistency-Model/assets/102277/c4003ac5-e7ff-44c0-97d3-464bb659de70