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deep-live-cam

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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.

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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

References