deep-live-cam
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name: deep-live-cam
description: >-
Real-time face swap and video deepfake using a single source image. Use when: building
face-swap applications, real-time video effects, virtual try-on features, AI video
effects pipelines.
license: AGPL-3.0
compatibility: "Python 3.10+, CUDA GPU recommended"
metadata:
author: terminal-skills
version: "1.0.0"
category: data-ai
tags:
- deepfake
- face-swap
- real-time
- video
- computer-vision
Deep-Live-Cam — Real-Time Face Swap
Overview
Real-time face swap and video deepfake using a single source image. Supports webcam, video files, and streaming with GPU acceleration. The pipeline detects faces, extracts embeddings, swaps faces using the inswapper model, and post-processes with GFPGAN/CodeFormer for quality.
Source: hacksider/Deep-Live-Cam
Instructions
1. Install and configure
git clone https://github.com/hacksider/Deep-Live-Cam.git
cd Deep-Live-Cam
pip install -r requirements.txt
Download models into the models/ directory:
mkdir -p models
wget -O models/inswapper_128_fp16.onnx "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx"
For GPU acceleration:
pip install onnxruntime-gpu # NVIDIA CUDA
pip install onnxruntime-rocm # AMD ROCm
pip install onnxruntime-coreml # Apple Silicon
2. Run face swap
GUI mode (webcam, real-time):
python run.py
CLI mode — process a video file:
python run.py \
--source path/to/source_face.jpg \
--target path/to/target_video.mp4 \
--output path/to/output.mp4 \
--execution-provider cuda
CLI mode — process a single image:
python run.py \
--source path/to/source_face.jpg \
--target path/to/target_image.jpg \
--output path/to/output.jpg
3. Key features
- Mouth Mask — Retains original mouth for accurate lip movement:
--mouth-mask - Face Mapping — Different source faces on multiple people:
--face-mapping - Quality Enhancement — GFPGAN or CodeFormer:
--enhancer gfpgan
Examples
Example 1: Swap a face in a conference recording
python run.py \
--source speaker_headshot.jpg \
--target conference_talk.mp4 \
--output anonymized_talk.mp4 \
--execution-provider cuda \
--enhancer gfpgan
This replaces the speaker's face in a 45-minute conference recording with the source face, using GPU acceleration and GFPGAN enhancement for broadcast-quality output.
Example 2: Programmatic face swap with Python
import cv2
import insightface
from insightface.app import FaceAnalysis
app = FaceAnalysis(name="buffalo_l", providers=["CUDAExecutionProvider"])
app.prepare(ctx_id=0, det_size=(640, 640))
swapper = insightface.model_zoo.get_model(
"models/inswapper_128_fp16.onnx",
providers=["CUDAExecutionProvider"]
)
source_img = cv2.imread("actor_headshot.jpg")
target_img = cv2.imread("movie_scene_frame.jpg")
source_faces = app.get(source_img)
target_faces = app.get(target_img)
if source_faces and target_faces:
result = swapper.get(target_img, target_faces[0], source_faces[0], paste_back=True)
cv2.imwrite("swapped_scene.jpg", result)
Example 3: Real-time webcam with mouth mask
python run.py --mouth-mask --execution-provider cuda
Launches the GUI with webcam input. Select a source face image, enable mouth mask for natural lip sync, and start the live face swap at 25-30 FPS on an RTX 3060.
Guidelines
- Always obtain consent from the person whose face you're using
- Label all outputs as AI-generated/deepfake when sharing publicly
- Legal compliance — Many jurisdictions have laws against non-consensual deepfakes
- Lighting matters — Works best with even, front-facing lighting; degrades at extreme head rotations (>60°)
- GPU recommended — CPU mode works but is very slow; NVIDIA RTX 3060+ with 6GB+ VRAM recommended
- Verify results — Heavy occlusion (masks, large sunglasses) can cause artifacts
- Use batch mode for quality — Real-time trades resolution for speed; use offline processing for high-res output