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How to install hacksider/Deep-Live-Cam. Official installation instructions. Project installation guide. Framework or library installation. Build installation instructions. How to setup and install from GitHub. Installation steps and setup instructions. Official docs and installation instructions GitHub.

hacksider/Deep-Live-Cam Installation Guide

How to install hacksider/Deep-Live-Cam. Official project installation instructions and setup guide.

⚡

Automated Install (Recommended)

Quick installation instructions for hacksider/Deep-Live-Cam. This is the fastest way to complete project installation and setup.

Install via curl

curl -fsSL https://hexmos.com/ipm-install | bash && 
ipm i hacksider/Deep-Live-Cam
or

Install via npx

npx @hexmos/ipm i hacksider/Deep-Live-Cam

Prerequisites

Python

language

Version: >=3.6.0

pip

package_manager

Package installer for Python.

Git

system_tool

Version: 2.30.1

Docker

system_tool

Containerization platform for development and deployment.

Manual Installation Methods

Manual installation instructions. How to install from GitHub source.

Exclusive v2.7 beta Quick Start - Pre-built (Windows/Mac Silicon/CPU)

Download the pre-built from https://deeplivecam.net/index.php/quickstart

Clone Repository and Install using Python (Manual - Recommended for technical users)

git clone https://github.com/hacksider/Deep-Live-Cam.git

cd Deep-Live-Cam

wget -O GFPGANv1.4.onnx https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx

wget -O inswapper_128_fp16.onnx https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx

mkdir models

mv *.onnx models/

python -m venv venv

source venv/bin/activate

pip install -r requirements.txt

pip install git+https://github.com/xinntao/BasicSR.git@master

pip uninstall gfpgan -y

pip install git+https://github.com/TencentARC/GFPGAN.git@master

GPU Acceleration - CUDA Execution Provider (Nvidia)

Download and install CUDA Toolkit 12.8.0 from https://developer.nvidia.com/cuda-12-8-0-download-archive

Download and install cuDNN v8.9.7 for CUDA 12.x from https://developer.nvidia.com/rdp/cudnn-archive

Add cuDNN bin directory to your system PATH

pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

pip uninstall onnxruntime onnxruntime-gpu -y

pip install onnxruntime-gpu==1.21.0

python run.py --execution-provider cuda

GPU Acceleration - CoreML Execution Provider (Apple Silicon)

brew install python@3.11

brew install python-tk@3.10

python3.11 -m venv venv

source venv/bin/activate

pip install -r requirements.txt

pip uninstall onnxruntime onnxruntime-silicon -y

pip install onnxruntime-silicon==1.13.1

python3.10 run.py --execution-provider coreml

GPU Acceleration - CoreML Execution Provider (Apple Legacy)

pip uninstall onnxruntime onnxruntime-coreml -y

pip install onnxruntime-coreml==1.21.0

python run.py --execution-provider coreml

GPU Acceleration - DirectML Execution Provider (Windows)

pip uninstall onnxruntime onnxruntime-directml -y

pip install onnxruntime-directml==1.21.0

python run.py --execution-provider directml

GPU Acceleration - OpenVINO Execution Provider (Intel)

pip uninstall onnxruntime onnxruntime-openvino -y

pip install onnxruntime-openvino==1.21.0

python run.py --execution-provider openvino

Post Installation Steps

  • Start the server using `npm start`.
  • Run database migrations with `npx sequelize db:migrate`.
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