README
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README.md
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title: Real
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emoji:
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sdk: docker
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pinned: false
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suggested_hardware: a10g-small
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disable_embedding: true
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---
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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).
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You need a webcam to run this demo. 🤗
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See a collecting with live demos [here](https://huggingface.co/collections/latent-consistency/latent-consistency-model-demos-654e90c52adb0688a0acbe6f)
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## Running Locally
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You need CUDA and Python 3.10, Node > 19, Mac with an M1/M2/M3 chip or Intel Arc GPU
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## Install
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```bash
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python -m venv venv
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source venv/bin/activate
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pip3 install -r server/requirements.txt
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cd frontend && npm install && npm run build && cd ..
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python server/main.py --reload --pipeline img2imgSDTurbo
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```
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Don't forget to fuild the frontend!!!
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```bash
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cd frontend && npm install && npm run build && cd ..
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```
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# Pipelines
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You can build your own pipeline following examples here [here](pipelines),
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# LCM
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### Image to Image
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```bash
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python server/main.py --reload --pipeline img2img
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```
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# LCM
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### Text to Image
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```bash
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python server/main.py --reload --pipeline txt2img
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```
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### Image to Image ControlNet Canny
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```bash
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python server/main.py --reload --pipeline controlnet
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```
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# LCM + LoRa
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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)
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### Image to Image ControlNet Canny LoRa
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```bash
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python server/main.py --reload --pipeline controlnetLoraSD15
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```
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or SDXL, note that SDXL is slower than SD15 since the inference runs on 1024x1024 images
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```bash
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python server/main.py --reload --pipeline controlnetLoraSDXL
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```
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### Text to Image
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```bash
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python server/main.py --reload --pipeline txt2imgLora
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```
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```bash
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python server/main.py --reload --pipeline txt2imgLoraSDXL
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```
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# Available Pipelines
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#### [LCM](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
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`img2img`
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`txt2img`
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`controlnet`
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`txt2imgLora`
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`controlnetLoraSD15`
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#### [SD15](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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`controlnetLoraSDXL`
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`txt2imgLoraSDXL`
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#### [SDXL Turbo](https://huggingface.co/stabilityai/sd-xl-turbo)
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`img2imgSDXLTurbo`
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`controlnetSDXLTurbo`
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#### [SDTurbo](https://huggingface.co/stabilityai/sd-turbo)
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`img2imgSDTurbo`
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`controlnetSDTurbo`
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#### [Segmind-Vega](https://huggingface.co/segmind/Segmind-Vega)
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`controlnetSegmindVegaRT`
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`img2imgSegmindVegaRT`
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### Setting environment variables
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* `--host`: Host address (default: 0.0.0.0)
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* `--port`: Port number (default: 7860)
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* `--reload`: Reload code on change
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* `--max-queue-size`: Maximum queue size (optional)
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* `--timeout`: Timeout period (optional)
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* `--safety-checker`: Enable Safety Checker (optional)
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* `--torch-compile`: Use Torch Compile
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* `--use-taesd` / `--no-taesd`: Use Tiny Autoencoder
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* `--pipeline`: Pipeline to use (default: "txt2img")
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* `--ssl-certfile`: SSL Certificate File (optional)
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* `--ssl-keyfile`: SSL Key File (optional)
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* `--debug`: Print Inference time
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* `--compel`: Compel option
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* `--sfast`: Enable Stable Fast
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* `--onediff`: Enable OneDiff
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If you run using `bash build-run.sh` you can set `PIPELINE` variables to choose the pipeline you want to run
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```bash
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PIPELINE=txt2imgLoraSDXL bash build-run.sh
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```
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and setting environment variables
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```bash
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TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 python server/main.py --reload --pipeline txt2imgLoraSDXL
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```
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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)
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```bash
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openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem
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python server/main.py --reload --ssl-certfile=certificate.pem --ssl-keyfile=key.pem
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```
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## Docker
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You need NVIDIA Container Toolkit for Docker, defaults to `controlnet``
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```bash
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docker build -t lcm-live .
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docker run -ti -p 7860:7860 --gpus all lcm-live
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```
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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
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```bash
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docker run -ti -p 7860:7860 -e HF_HOME=/data -v ~/.cache/huggingface:/data --gpus all lcm-live
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```
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or with environment variables
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```bash
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docker run -ti -e PIPELINE=txt2imgLoraSDXL -p 7860:7860 --gpus all lcm-live
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```
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# Demo on Hugging Face
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* [radames/Real-Time-Latent-Consistency-Model](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model)
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* [radames/Real-Time-SD-Turbo](https://huggingface.co/spaces/radames/Real-Time-SD-Turbo)
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* [latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5](https://huggingface.co/spaces/latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5)
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* [latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5](https://huggingface.co/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5)
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* [radames/Real-Time-Latent-Consistency-Model-Text-To-Image](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image)
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https://github.com/radames/Real-Time-Latent-Consistency-Model/assets/102277/c4003ac5-e7ff-44c0-97d3-464bb659de70
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---
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title: Real Time Img2img Turbo
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emoji: 🔥🖼️🖼️
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colorFrom: blue
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colorTo: blue
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sdk: docker
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pinned: false
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suggested_hardware: a10g-small
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disable_embedding: true
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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