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--- |
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title: Real-Time Latent Consistency Model Image-to-Image ControlNet |
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emoji: 🖼️🖼️ |
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colorFrom: gray |
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colorTo: indigo |
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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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# Real-Time Latent Consistency Model |
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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 requirements.txt |
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cd frontend && npm install && npm run build && cd .. |
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# fastest pipeline |
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python run.py --reload --pipeline img2imgSD21Turbo |
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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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don't forget to fuild the frontend first |
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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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# LCM |
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### Image to Image |
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```bash |
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python run.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 run.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 run.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 run.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 run.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 run.py --reload --pipeline txt2imgLora |
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``` |
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or |
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```bash |
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python run.py --reload --pipeline txt2imgLoraSDXL |
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``` |
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### Setting environment variables |
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`TIMEOUT`: limit user session timeout |
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`SAFETY_CHECKER`: disabled if you want NSFW filter off |
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`MAX_QUEUE_SIZE`: limit number of users on current app instance |
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`TORCH_COMPILE`: enable if you want to use torch compile for faster inference works well on A100 GPUs |
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`USE_TAESD`: enable if you want to use Autoencoder Tiny |
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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 run.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 run.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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# Development Mode |
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```bash |
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python run.py --reload |
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``` |
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# Demo on Hugging Face |
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https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model |
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https://github.com/radames/Real-Time-Latent-Consistency-Model/assets/102277/c4003ac5-e7ff-44c0-97d3-464bb659de70 |
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