Step‑by‑Step Guide: Deploy the Roop AI Face‑Swap Project with Tongyi Lingma

Overview

Roop is an open‑source AI face‑swap project (≈25 000 ★ on GitHub) that swaps a target face onto a source video using deep‑learning models.

Prerequisites

FFmpeg installed and its bin directory added to the system PATH.

CUDA‑compatible GPU (optional, but required for accelerated inference).

Installation Steps

Clone the repository: git clone https://github.com/s0md3v/roop.git Create and activate a dedicated Conda environment:

conda create --name roop python=3.10
conda activate roop

Install the CUDA‑enabled PyTorch build (adjust the CUDA version if needed):

pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 \
    --index-url https://download.pytorch.org/whl/cu118

Install the remaining Python dependencies listed in the project: pip install -r requirements.txt Install the GPU version of ONNX Runtime for faster model inference:

pip install onnxruntime-gpu

Running the Application

Start the main script from the repository root: python run.py The web UI will appear; select a source video and a target face image, then click Start to begin processing.

Parameter Tuning

Roop provides several command‑line options to control resource usage and output quality. Common examples:

Limit maximum memory usage (in GB): python run.py --max-memory 8 Preserve the original frame rate: python run.py --keep-fps Adjust output frame rate, quality, or other model‑specific flags as documented in the project's README.

These options can be combined as needed, e.g., python run.py --max-memory 8 --keep-fps.