facefusion

Industry leading face manipulation platform

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An arbitrary face-swapping framework on images and videos with one single trained model!

Quick Overview

FaceFusion is an open-source face swapping and face editing tool. It allows users to replace faces in images and videos with high-quality results. The project aims to provide a user-friendly interface for face manipulation tasks while maintaining ethical standards.

Pros

High-quality face swapping results
User-friendly command-line interface
Supports both image and video processing
Actively maintained and regularly updated

Cons

Requires significant computational resources for optimal performance
Limited documentation for advanced features
Potential for misuse in creating deepfakes
May struggle with complex lighting conditions or extreme facial angles

Code Examples

# Basic face swap on an image
from facefusion import FaceFusion

ff = FaceFusion()
ff.swap_face("source_image.jpg", "target_image.jpg", "output_image.jpg")

# Face swap on a video with custom settings
from facefusion import FaceFusion

ff = FaceFusion(model='high_quality', device='cuda')
ff.swap_face("source_image.jpg", "target_video.mp4", "output_video.mp4", 
             frame_processor='face_swapper', 
             face_recognizer='arcface_insightface')

# Batch processing multiple images
from facefusion import FaceFusion

ff = FaceFusion()
source_images = ["source1.jpg", "source2.jpg", "source3.jpg"]
target_images = ["target1.jpg", "target2.jpg", "target3.jpg"]
ff.batch_process(source_images, target_images, "output_folder")

Getting Started

To get started with FaceFusion, follow these steps:

Install the required dependencies:
```
pip install facefusion
```

Import the FaceFusion class and create an instance:

from facefusion import FaceFusion
ff = FaceFusion()

Perform a basic face swap:

ff.swap_face("source_image.jpg", "target_image.jpg", "output_image.jpg")

For more advanced usage and options, refer to the project's documentation on GitHub.

Competitor Comparisons

faceswap

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Deepfakes Software For All

Pros of Faceswap

More extensive documentation and user guides
Larger community and longer development history
Supports multiple face swapping methods (Original, DFAKER, DFL-H128)

Cons of Faceswap

Steeper learning curve for beginners
Requires more computational resources
Less focus on real-time applications

Code Comparison

Facefusion (simplified usage):

from facefusion import FaceFusion

ff = FaceFusion()
ff.run(source_path='source.jpg', target_path='target.jpg', output_path='output.jpg')

Faceswap (simplified usage):

from lib.cli import FullHelpArgumentParser
from scripts.train import Train
from scripts.convert import Convert

parser = FullHelpArgumentParser()
Train(parser).process()
Convert(parser).process()

Facefusion offers a more straightforward API for quick face swapping tasks, while Faceswap provides a more comprehensive command-line interface with separate training and conversion steps. Facefusion is designed for ease of use and real-time applications, whereas Faceswap offers more advanced features and customization options at the cost of increased complexity.

DeepFaceLab

18,058

DeepFaceLab is the leading software for creating deepfakes.

Pros of DeepFaceLab

More comprehensive and feature-rich, offering a wider range of face swapping and manipulation options
Supports both video and image processing, allowing for more versatile applications
Has a larger community and more extensive documentation, making it easier for users to find support and resources

Cons of DeepFaceLab

Steeper learning curve due to its complexity and numerous features
Requires more computational resources and time for processing, especially for high-quality results
Less user-friendly interface compared to FaceFusion's streamlined approach

Code Comparison

DeepFaceLab:

from core.leras import nn
from facelib import FaceType
from models import ModelBase
from samplelib import *

class Model(ModelBase):
    # Complex model implementation

FaceFusion:

import cv2
import insightface
from core.config import get_config

def get_face_analyser():
    # Simpler face analysis implementation

DeepFaceLab offers a more complex and customizable codebase, while FaceFusion provides a more straightforward implementation focused on ease of use and quick results. DeepFaceLab's code allows for greater flexibility in model architecture and training, whereas FaceFusion emphasizes simplicity and efficiency in face swapping tasks.

first-order-model

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This repository contains the source code for the paper First Order Motion Model for Image Animation

Pros of first-order-model

More versatile, capable of animating a wider range of objects beyond just faces
Provides a more comprehensive framework for motion transfer and image animation
Offers better performance for full-body motion transfer

Cons of first-order-model

Generally slower processing speed compared to facefusion
May require more computational resources for complex animations
Less specialized for face-specific tasks, potentially resulting in lower quality for facial animations

Code Comparison

first-order-model:

source_image = imageio.imread(opt.source_image)
driving_video = imageio.mimread(opt.driving_video)
source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)

facefusion:

source_face = get_face_single(source_path)
target_face = get_face_single(target_path)
result = swap_face(source_face, target_face)
cv2.imwrite(output_path, result)

The code snippets show that first-order-model uses a more general approach for image and video processing, while facefusion is more focused on face-specific operations. first-order-model handles both source images and driving videos, whereas facefusion deals primarily with face swapping between two images.

SimSwap

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An arbitrary face-swapping framework on images and videos with one single trained model!

Pros of SimSwap

Offers more advanced face swapping techniques, including attention mechanisms
Provides pre-trained models for immediate use
Supports both image and video face swapping

Cons of SimSwap

Less actively maintained, with fewer recent updates
More complex setup process and dependencies
Limited documentation compared to FaceFusion

Code Comparison

SimSwap:

from models.models import create_model
from options.test_options import TestOptions
from insightface_func.face_detect_crop_single import Face_detect_crop

model = create_model(opt)
model.eval()

FaceFusion:

from facefusion import core
from facefusion.content_analyser import analyse_content
from facefusion.face_analyser import get_face_analyser

core.initialise()
face_analyser = get_face_analyser()

SimSwap focuses on creating and loading a specific face swapping model, while FaceFusion provides a more modular approach with separate analyzers for content and faces. FaceFusion's code structure appears more organized and easier to integrate into larger projects.

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README

FaceFusion

Industry leading face manipulation platform.

Preview

Installation

Be aware, the installation needs technical skills and is not recommended for beginners. In case you are not comfortable using a terminal, our Windows Installer and macOS Installer get you started.

Usage

Run the command:

python facefusion.py [commands] [options]

options:
  -h, --help                                      show this help message and exit
  -v, --version                                   show program's version number and exit

commands:
    run                                           run the program
    headless-run                                  run the program in headless mode
    batch-run                                     run the program in batch mode
    force-download                                force automate downloads and exit
    job-list                                      list jobs by status
    job-create                                    create a drafted job
    job-submit                                    submit a drafted job to become a queued job
    job-submit-all                                submit all drafted jobs to become a queued jobs
    job-delete                                    delete a drafted, queued, failed or completed job
    job-delete-all                                delete all drafted, queued, failed and completed jobs
    job-add-step                                  add a step to a drafted job
    job-remix-step                                remix a previous step from a drafted job
    job-insert-step                               insert a step to a drafted job
    job-remove-step                               remove a step from a drafted job
    job-run                                       run a queued job
    job-run-all                                   run all queued jobs
    job-retry                                     retry a failed job
    job-retry-all                                 retry all failed jobs

Documentation

Read the documentation for a deep dive.

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