Easy Deepfake Tutorial: DeepFaceLab 2.0 Quick96

Deepfakery
27 Jul 202006:39

TLDRThis tutorial demonstrates how to create deepfake videos using DeepFaceLab 2.0 build 7182020 on a Windows PC with an NVIDIA graphics card. It guides through downloading and installing the software, extracting images and facesets from source and destination videos, training the deepfake model with default settings, and merging the faces to produce the final video. The process concludes with tips for enhancing the deepfake quality and experimenting with custom videos.

Takeaways

  • 📥 Download and install DeepFaceLab 2.0 from GitHub. No setup is required after extraction.
  • 💻 Ensure you have access to a Windows PC with an NVIDIA graphics card to run DeepFaceLab effectively.
  • 🔄 Extract images from both the source (data src) and destination (data dst) videos to prepare for deepfake creation.
  • 🖼️ Extract facesets from the extracted images for both source and destination videos. This process identifies and separates faces in each frame.
  • 👀 Review the extracted facesets to remove any unwanted faces or images from the deepfake project.
  • 🧠 Begin training the deepfake model using the Quick96 preset in DeepFaceLab. Training involves multiple iterations and previews to refine the results.
  • 🔍 Use the preview window during training to monitor progress and decide when to end the training based on the accuracy and quality of the model.
  • 🔄 Merge the trained faces with the destination video to create the deepfake video using the 'merge Quick96' file.
  • 🎬 Finalize the deepfake by merging the new frames into a video file, retaining the original destination audio.
  • 🎉 Experiment with different settings and continue training to improve the quality of your deepfake video. Custom videos can be used by replacing the source and destination video files.

Q & A

  • What software is used in the tutorial to create deepfake videos?

    -The tutorial uses DeepFaceLab 2.0 build 7182020 to create deepfake videos.

  • What are the system requirements for running DeepFaceLab as mentioned in the tutorial?

    -The system requirements include a Windows PC with an NVIDIA graphics card.

  • How can one obtain DeepFaceLab for the tutorial?

    -DeepFaceLab 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?

    -This step involves processing the video files to create a .png file for each frame, using the default settings.

  • How are facesets extracted from the images?

    -Facesets are extracted by running the 'data src faceset extract' and 'data dst faceset extract' files with default values.

  • What can be done in the 'View Facesets' step?

    -In this step, users can view the source and destination facesets, and remove unwanted faces from the project.

  • What happens during the 'Training' step of 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 process?

    -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 involves merging the trained faces with the original video to create the final deepfake video.

  • How are the deepfake frames combined into a video file with destination audio?

    -The deepfake frames are merged into a video file with destination audio by running the 'merge to mp4' file and pressing enter.

  • What is the final output of the deepfake creation process as described in the tutorial?

    -The final output is a deepfake video file named 'result.mp4' located in the workspace folder.

Outlines

00:00

🎥 Introduction to Creating Deepfake Videos

The video tutorial begins with an introduction to creating deepfake videos using DeepFaceLab 2.0. The instructor outlines the requirements, which include a Windows PC with an NVIDIA graphics card. The process starts with downloading and installing DeepFaceLab from GitHub, followed by extracting the necessary files. The workspace is set up with folders for images and trained model files. The tutorial then proceeds to the steps of extracting images from source and destination videos, extracting facesets from these images, viewing these facesets, and finally, training the deepfake model using default settings. The training process is monitored through a preview window that shows loss values and model images, indicating the accuracy of the training.

05:03

🔧 Finalizing the Deepfake Video

After the training is complete, the tutorial moves on to merging the faces to create the final deepfake video. The instructor demonstrates how to use the merging tool with default settings, adjusting the erode and blur mask values for better results. The settings are applied to all frames, and the process is completed by merging the new deepfake frames into a video file with the destination audio. The video concludes with the viewer being directed to the workspace folder to play the final 'result.mp4' file. The instructor encourages viewers to experiment with training and merging settings to achieve desired results and to create deepfakes from personal videos by following the same steps.

Mindmap

Keywords

💡Deepfake

A deepfake refers to a synthetic media in which a person's likeness is superimposed onto another individual's body with the help of artificial intelligence, particularly deep learning techniques. In the context of the video, deepfakes are created by using DeepFaceLab software to swap faces in videos, which is demonstrated through the tutorial.

💡DeepFaceLab

DeepFaceLab is an open-source tool used for creating deepfakes. The software utilizes machine learning algorithms to generate highly realistic face swaps in videos. The video tutorial specifically uses DeepFaceLab 2.0 build 7182020, indicating a particular version of the software that the instructor is teaching.

💡NVIDIA graphics card

An NVIDIA graphics card is a type of hardware acceleration device that is essential for the video's deepfake creation process. Graphics cards, particularly from NVIDIA, are known for their CUDA capabilities, which allow for parallel processing and are crucial for the computationally intensive tasks involved in training deepfake models.

💡Quick96 preset trainer

The Quick96 preset trainer is a pre-configured setting within DeepFaceLab that optimizes the training process for creating deepfakes. 'Quick96' suggests a balance between speed and quality, likely referring to the resolution or some other parameter set to '96'. The video mentions using this preset with default settings for simplicity.

💡Extract Images

Extracting images from a video is the first step in the deepfake creation process outlined in the video. It involves breaking down the video into individual frames, which are then saved as image files. This step is necessary to prepare the data for face detection and subsequent face swapping.

💡Facesets

A faceset refers to a collection of facial images that are used to train the deepfake model to recognize and replicate facial features. In the video, facesets are extracted from the source and destination videos, which are then used to train the model to swap faces accurately.

💡Training

Training in the context of the video refers to the process of teaching the deepfake model to generate realistic face swaps. This is done by feeding it a large number of images from the facesets, allowing the model to learn the nuances of the faces involved.

💡Loss values

Loss values are metrics used in machine learning to measure the difference between the model's predictions and the actual data. In the video, loss values are mentioned as indicators of the model's performance during training, with the goal of these values approaching zero for optimal results.

💡Merging

Merging in the deepfake process refers to the final step where the trained model's output is combined with the original video to create the final deepfake video. The video tutorial demonstrates how to use the 'merge Quick96' function to apply the trained model to the video frames.

💡Erode mask value

The erode mask value is a parameter used during the merging process to refine the edges of the swapped face in the deepfake video. In the video, adjusting this value helps to contract the border around the face, ensuring a more seamless integration of the fake face onto the original video.

💡Blur mask value

The blur mask value is another parameter used in the merging process to control the level of blur applied around the edges of the face in the deepfake video. Raising this value, as demonstrated in the video, can help to smooth out the transition between the face and the background for a more realistic appearance.

Highlights

Tutorial on creating deepfake videos using DeepFaceLab 2.0 build 7 18 2020.

Requires a Windows PC with an NVIDIA graphics card.

Quick96 preset trainer with default settings is used.

DeepFaceLab can be downloaded from GitHub releases.

No setup is needed for DeepFaceLab; just extract the files.

Workspace folder contains folders for images and trained model files.

Extract images from source and destination videos using default settings.

Extracted faces are processed for deepfake creation.

View and potentially remove unwanted faces from the facesets.

Training begins with the Quick96 preset, using default settings.

Training accuracy is monitored through a preview window.

Loss values in training approach zero, indicating better results.

Training can be saved and restarted at any time.

Merging faces and creating the final deepfake video with default settings.

Adjust erode and blur mask values for better face merging.

Apply settings to all frames and process remaining frames.

Merge deepfake frames into a video file with destination audio.

View the final deepfake video in the workspace folder.

Quality of the deepfake can be improved by restarting training.

Experiment with merger settings for desired results.

Create deepfakes from personal videos by following the same steps.