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