Create Stunning DeepFake in Just 10 Minutes!

Create Stunning DeepFake in Just 10 Minutes!

Table of Contents

1. Introduction
2. What is Deepfake?
3. Creating Deepfake with Google Colab
   • 3.1. Using the First Order Model
4. Cloning the Repository
5. Mounting the Google Drive
6. Creating the First Order Motion Model Directory
7. Importing and Resizing the Source Image and Driving Video
8. Displaying the Source Image and Driving Video
9. Loading the Weights to the Model
10. Making the Animation
11. Saving and Displaying the Generated Deepfake
12. Conclusion

Introduction

In the world of technology, deepfake has become a buzzword. This advanced AI technology allows for the manipulation and synthesis of media content, particularly videos and images, to make them appear real but completely fabricated. Creating deepfake used to be a task reserved for those with powerful GPUs, but thanks to Google Colab, You can now easily Create deepfake content without needing high-end hardware. In this article, we'll explore the process of creating deepfake using Google Colab's First Order Model. So, let's get started!

What is Deepfake?

Before diving into the process, let's briefly understand what deepfake is. Deepfake refers to AI-generated media, such as videos or images, that depict people saying or doing things they Never actually did. By using machine learning algorithms, deepfake technology can manipulate and superimpose faces onto existing images or videos, making it seem like someone else is doing or saying something. Deepfakes have gained Attention for their potential to spread misinformation or create realistic but entirely false content.

Creating Deepfake with Google Colab

To create deepfake content using Google Colab, we will be using the First Order Model. This powerful machine learning model, developed and trained by Alexander Ciarohin, allows us to animate and manipulate various types of animations, videos, and photos. However, in this article, we will focus solely on image animation.

3.1 Using the First Order Model

To utilize the First Order Model in Google Colab, follow these steps:

Cloning the Repository

Firstly, we need to clone the repository that contains the weights and architecture of the model, as well as the samples we will use.

Mounting the Google Drive

Next, we need to mount our Google Drive to access and use the cloned model and data files in Google Colab.

Creating the First Order Motion Model Directory

Within our Google Drive, we must create a directory dedicated to the First Order Motion Model. We will copy the necessary zipped files (weights and architecture) into this directory.

Importing and Resizing the Source Image and Driving Video

In this step, the source image (the image we want to animate) and the driving video (used to manipulate the face of the source image) will be imported and resized to match the input matrix size (256x256) required by the neural network.

Displaying the Source Image and Driving Video

Using the matplotlib library, we can display the source image and driving video to Visualize what we will be working with.

Loading the Weights to the Model

Now, we load the weights of the First Order Motion Model into the model from our Google Drive. This step ensures that we can manipulate the data effectively.

Making the Animation

Provide the source image and driving video as inputs to the make animation function, which is imported from the demo. This function generates the animated deepfake.

Saving and Displaying the Generated Deepfake

Once the animation is complete, we save it to our directory and display the source image, driving video, and the generated animation using the matplotlib library.

Conclusion

Creating deepfake content has become much more accessible with the help of Google Colab and the First Order Model. By following the steps outlined in this article, you can easily experiment and generate your own deepfake content without the need for a powerful GPU. However, it is important to acknowledge the ethical implications and potential misuse of deepfake technology. As deepfake continues to evolve, society must remain cautious and vigilant in differentiating between real and manipulated media.