Easy Deepfake Tutorial: DeepFaceLab 2.0 Quick96

Deepfakery
27 Jul 202006:39

TLDRThis tutorial guides viewers through the process of creating deepfake videos using DeepFaceLab 2.0. It requires a Windows PC with an NVIDIA graphics card. The steps include downloading and installing DeepFaceLab, extracting images from videos, processing facesets, training the model with Quick96 preset, and merging the deepfake faces into a final video. The instructor emphasizes the ability to improve results by restarting training and experimenting with settings.

Takeaways

  • 🖥️ This tutorial uses DeepFaceLab 2.0 build 7182020 for creating deepfake videos.
  • 💻 A Windows PC with an NVIDIA graphics card is required for the process.
  • 🔧 The Quick96 preset trainer with default settings is utilized for simplicity.
  • 📥 Users need to download DeepFaceLab from GitHub and extract the files without installation.
  • 📂 The 'workspace' folder contains subfolders for images and trained model files.
  • 📸 Images are extracted from source and destination videos using provided batch files.
  • 🔍 Face extraction is performed on the images to prepare for the deepfake creation.
  • 👀 The facesets can be viewed and unwanted images can be removed if necessary.
  • 🤖 Training of the deepfake model begins with the 'train Quick96' file and default settings.
  • 📊 The training accuracy is monitored through a preview window displaying loss values.
  • 🎞️ The merging process combines the trained model with the video to create the final deepfake.
  • 🔧 Users can adjust erode and blur mask values for better deepfake results.
  • 📹 The final step involves merging the deepfake frames into a video with destination audio.
  • 🚀 The tutorial encourages experimentation with training and merger settings for quality improvement.

Q & A

  • What software is used in the tutorial for creating deepfake videos?

    -The tutorial uses DeepFaceLab 2.0 build 7182020 for creating deepfake videos.

  • What are the system requirements for running DeepFaceLab 2.0?

    -A Windows PC with an NVIDIA graphics card is required to run DeepFaceLab 2.0.

  • How can one obtain DeepFaceLab 2.0 for the tutorial?

    -DeepFaceLab 2.0 can be downloaded from the releases section on github.com/iperov/DeepFaceLab, using either a torrent magnet link or from Mega.nz.

  • What is the purpose of the 'workspace' folder in DeepFaceLab?

    -The 'workspace' folder in DeepFaceLab holds the images and trained model files used in the deepfake process.

  • What does the 'extract images from video' step involve?

    -The 'extract images from video' step involves processing the video file to create a .png file for each frame.

  • How are facesets extracted in the tutorial?

    -Facesets are extracted by processing the images and identifying faces using the 'data src faceset extract' and 'data dst faceset extract' files.

  • What can be done in the 'view aligned results' step?

    -In the 'view aligned results' step, one can view the source and destination facesets and remove unwanted faces from the project.

  • What happens during the training step in the deepfake creation process?

    -During the training step, the software loads all image files and runs the first iteration of training to create the deepfake model.

  • How can one update the preview window during the training?

    -Pressing the 'P' key updates the preview window, showing changes in the graphic images.

  • What is the purpose of the merging step in creating a deepfake video?

    -The merging step combines the trained model with the video frames to create the final deepfake video, adjusting settings like erode mask and blur mask values.

  • How is the final deepfake video created after the merging step?

    -After the merging step, the new deepfake frames are merged into a video file with the destination audio using the 'merge to mp4' file.

Outlines

00:00

🎥 Deepfake Video Creation Tutorial

This paragraph introduces a tutorial on creating deepfake videos using DeepFaceLab 2.0. The process requires a Windows PC with an NVIDIA graphics card. The tutorial involves downloading and installing DeepFaceLab from GitHub, setting up the workspace, and using specific batch files for the deepfake creation. The steps include extracting images from videos, processing these images to extract faces, viewing and selecting faces for the deepfake, training the deepfake model using default settings, and finally merging the trained model to create the final video.

05:03

🔧 Finalizing the Deepfake Video

This paragraph details the final steps in the deepfake video creation process. It covers the merging of trained faces to create the final deepfake video, adjusting settings for optimal results, and processing the remaining frames. The tutorial also explains how to merge the new deepfake frames with the destination audio to create a complete video file. The process concludes with viewing the final deepfake video and offers advice on improving quality by restarting training or experimenting with different settings. Additionally, the instructor encourages using personal videos for deepfake creation by following the same steps.

Mindmap

Keywords

Deepfake

A deepfake refers to a synthetic media in which a person's likeness is superimposed onto someone else's body in a video or image with the help of artificial intelligence and deep learning algorithms. In the context of the video, deepfakes are created by using DeepFaceLab 2.0 software to swap faces in videos, which is a process that involves extracting images, training a model, and merging the results to produce a convincing fake video.

DeepFaceLab

DeepFaceLab is an open-source tool used for creating deepfakes. It utilizes deep learning to swap faces in videos. The video tutorial specifically mentions using DeepFaceLab 2.0 build 7182020, indicating a particular version of the software that includes certain features and presets for generating deepfake videos.

NVIDIA graphics card

An NVIDIA graphics card is a type of hardware that accelerates the creation of deepfake videos by providing the necessary computational power for the graphics-intensive tasks involved in training and processing. The video mentions that a Windows PC with an NVIDIA graphics card is required, highlighting the importance of this hardware for the deepfake creation process.

Quick96 preset trainer

The Quick96 preset trainer is a pre-configured setting within DeepFaceLab that optimizes the training process for creating deepfakes. It is mentioned in the video as a way to use default settings to train the deepfake model with minimal user input, streamlining the process for beginners.

Extract Images

Extracting images from a video is the first step in creating a deepfake, where each frame of the video is converted into a still image. The video script describes using a batch file to automate this process, which is essential for preparing the data needed to train the deepfake model.

Facesets

Facesets are collections of images that contain faces extracted from the source and destination videos. The video explains the process of extracting facesets as a crucial step in preparing the data for the deepfake model, which will learn to map one face onto another.

Training

Training in the context of deepfakes refers to the process of teaching the AI model to accurately swap faces in a video. The video describes using the 'train Quick96' file to initiate this process, where the model learns from the extracted facesets to generate a convincing deepfake.

Merge

Merging is the final step in creating a deepfake video, where the trained model's output is combined with the original video to produce the final fake. The video script details using the 'merge Quick96' file to apply the trained model to the video frames, creating the deepfake.

Erode mask value

The erode mask value is a parameter used during the merging process to refine the edges of the swapped face in a deepfake video. The video mentions adjusting this value to improve the realism of the deepfake by reducing the visibility of the face's border.

Blur mask value

The blur mask value is another parameter used during the merging process to control the level of blur around the edges of the swapped face. The video script instructs viewers to increase this value to smooth out the transition between the face and the background for a more realistic deepfake.

Loss values

Loss values are metrics that indicate how well the deepfake model is learning during the training process. The video explains that these values, represented in the preview window, approach zero as the model improves, signifying better results in the final deepfake.

Highlights

Tutorial on creating deepfake videos using DeepFaceLab 2.0 build 7182020.

Requires a Windows PC with an NVIDIA graphics card.

DeepFaceLab's Quick96 preset trainer is used with default settings.

Download DeepFaceLab from GitHub releases section.

No setup is required for DeepFaceLab; simply extract the files.

Workspace folder contains directories for images and trained model files.

Extract images from source and destination videos using default settings.

Process images to extract faces for the deepfake.

View and potentially remove unwanted faces from the source and destination facesets.

Begin training the deepfake model using the Quick96 preset.

Training accuracy and loss values are displayed in the preview window.

Use keyboard commands to navigate and adjust settings during training.

Merge the trained faces to create the final deepfake video.

Adjust erode and blur mask values for better face merging.

Apply settings to all frames and process the remaining frames.

Merge the deepfake frames into a video file with destination audio.

View the final deepfake video in the workspace folder.

Training can be restarted to improve the quality of the deepfake.

Experiment with merger settings to achieve desired results.

Create deepfakes from personal videos by following the same tutorial.

Casual Browsing

DeepFaceLab 2.0 Easy Tutorial | Part 1 [ 2023 ]

2024-08-31 04:13:00

Easy DeepFake Tutorial using DeepFaceLab | Part 1 [2023]

2024-08-31 04:32:00

DeepFaceLab 2.0 Xseg Tutorial

2024-08-31 03:31:00

DeepFaceLab 2.0 Pretraining Tutorial

2024-08-31 02:21:00

DeepFaceLab 2.0 Faceset Extract Tutorial

2024-08-31 02:34:00