SimSwap is an acronym for Simple Swap. It is an Machine Learning framework that aims for generalized and high-fidelity face-swapping.
This tutorial will give an overview of the framework, how it can be applied in various use cases, and implement an example to demonstrate how it can be used.
To follow along with this tutorial, the reader will need to be familiar with:
Table of contents
- High-level overview
- Cloning the GitHub repo
- Installing the required dependencies
- Downloading additional models
- Learning how to face swap images
- Wrapping up
We achieve face swapping using a model called SimSwap. SimSwap uses a GAN architecture. You can think of it as having two competing neural networks; a Generator and a Discriminator. The generator tries to apply a person’s identity to a target image. It does this by extracting the key features in a person’s face, i.e., eyes, nose, mouth, and applying those to the target image. It is attributed to the
ID Injection Module (IIM) which makes it perform better.
The discriminators’ role is that of a critic. It tries to pick the images that look real. It uses a technique known as,
Weak Feature Matching Loss to compare the fake with the real target image. The technique helps us preserve the facial features attributes. By optimizing for that loss, it learns to create ideal face swaps.
In the beginning, the model starts with a source and target image. The source image is the one where we want to extract features from. The source and target images pass through the encoder and decoder architecture of the SimSwap model. The resulting output will be the target image applied with the features from the source image.
To fine-tune the model, they pass in the target and final resulting image into a discriminator. This process is similar to how other GANs work. The discriminator then tells whether these predictions are close to real images.
That’s a high-level overview of how the SimSwap model works. You can read more about the SimSwap model here.
Cloning the GitHub repo
We first need to clone the GitHub repository for the model.
!git clone https://github.com/neuralchen/SimSwap
The code above will clone the repository into our notebook. If you go to the folders tab on your Google Colab, you’ll see all the files present in the GitHub now available in your notebook.
Make sure to add the exclamation mark
gitcommand. Otherwise, you’ll get a syntax error.
The next step involved is to install our dependencies.
Installing the required dependencies
Our main dependency for this build is PyTorch. To get your computer-specific PyTorch installation, head over to PyTorch’s website. Select your PyTorch build, your OS, package, language, and computing platform of choice. Once you do this, a user-specific installation command will be generated. Use the generated command to install PyTorch on your notebook.
The following command is generated for this build:
!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
The next dependency we will install is based on what the SimSwap model documentation recommends here.
!pip install insightface==0.2.1 onnxruntime moviepy
If you have a GPU machine, use the
onnxruntime-gpu command instead. Since we are using a Google Colab notebook with a GPU enabled, use this command:
!pip install insightface==0.2.1 onnxruntime-gpu moviepy
insightface==0.2.1is an open-source 2D & 3D deep face analysis library that implements a rich variety of state-of-the-art algorithms for face recognition, face detection, and face alignment. You can read more about it here.
onnxruntime-gpuis an engine for Open Neural Network Exchange (ONNX). It allows for serialization in deep learning models, improving their performance.
moviepyis a module used in Python programming language for video editing, i.e., cuts, adding titles, concatenation, etc.
Downloading additional models
According to this documentation, we need to download additional models and place them into the correct folder in the repository.
These files include:
- InsightFace to help in image processing. It should be unzipped into the
You can download and upload it manually or use the following code:
!wget --no-check-certificate "https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w" -O antelope.zip !unzip ./antelope.zip -d ./insightface_func/models/antelope
- Face parsing for image post-processing. It should be placed into the
You can download it and upload it manually. You can also use the following code to download it:
!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth
- Two archives on the drive.
arcface_checkpoint.tarto be placed in the
checkpoints.zip. unzipped into the
You can download it and upload it manually. Alternatively, you can use the following code:
!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar !wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip !unzip ./checkpoints.zip -d ./checkpoints
Once you’ve downloaded these files, you’ll need to upload them into the correct folder. If not, you’ll get several errors. It takes some time to set it up, but be patient and ensure that you’ve followed every instruction to the letter.
We can now go ahead and test this model on images.
Learning how to face swap images
To perform face swapping on images, we need to run the following command:
cd SimSwap & python test_one_image.py --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/
Since our cloned SimSwap repository is in another folder, we first need to enter it into the folder using
cd SimSwap. We then perform some Python commands while inside the folder.
test_one-image.pyis a Python file that comes with the repository.
--name peopleallows the SimSwap to use the
--pic_a_pathindicates the path of the image with the target face. The target face image is located inside the
crop_224folder. You can change the target image to a different one inside the folder.
--pic_b_pathindicates the path of the image with the source face to swap. The source face to swap image is located inside the
crop_224folder too. You can change the target image to a different one inside the folder.
--output_pathindicates the path of the directory to store the face-swapping result.
When you swap the target face image,
6.jpg with the source face to swap image,
ds.jpg, we get the following resulting image:
We have successfully swapped facial features from a source image onto a target image. You can find the complete code for this tutorial here.
Make sure to use this model for good purposes. Do not apply this model for illegal and unethical purposes.
SimSwap is a model that performs well in face-swapping tasks. This model can be extended to swap people’s faces in videos. Please refer to their documentation on GitHub to learn more.
It is widely used in the film industry to help generate non-existent twins. It is used to reconstruct an actor’s face model. It also rebuilds a scene’s attributes, i.e., lighting condition.
Peer Review Contributions by: Willies Ogola