Easy Deepfake tutorial for beginners Xseg

JSFILMZ
22 Sept 202025:17

TLDRThis tutorial offers an advanced deepfake guide using Nvidia Deep Face Lab. The creator emphasizes the importance of varied facial expressions and consistent lighting in data sources for better results. The video demonstrates extracting images from videos, automatic face extraction, manual masking, and training the model. It concludes with a preview of the deepfake result, promising a follow-up on compositing in software like DaVinci Resolve and After Effects.

Takeaways

  • 😀 This tutorial is an advanced deepfake guide, building upon a previous quick tutorial by the same creator.
  • 👨‍🏫 The creator acknowledges being a learner in deepfaking, having started only two weeks prior, and credits '10 Deep Fakery' for assistance.
  • 🎬 The creator encourages viewers to vote for his CGI animated short film and hints at submitting a live-action short film to My Road Reel 2020.
  • 💻 The tutorial uses Nvidia's Deep Face Lab software and emphasizes the need for even lighting and varied facial expressions in the source video.
  • 📸 The process involves extracting images from videos, with a preference for PNG format over JPEG for better quality.
  • 🔍 Automatic face extraction is demonstrated, with a manual check and cleanup step to ensure quality.
  • 🎭 The tutorial introduces the use of masks to refine the facial areas being deepfaked, which is crucial for better results.
  • 🤖 The importance of training the model with the masks is highlighted, with the creator suggesting 10,000 iterations as a starting point.
  • 🎨 Adjustments such as color transfer and mask blurring are discussed to improve the final deepfake video's realism.
  • 🔗 The creator plans to cover compositing in software like DaVinci Resolve and After Effects in a follow-up tutorial.

Q & A

  • What is the main focus of the video tutorial?

    -The main focus of the video tutorial is to provide a more advanced deepfake tutorial using Nvidia Deep Face Lab, with the aim of creating a better quality result by following a detailed process.

  • What is the significance of having a variety of facial expressions in the data source?

    -Having a variety of facial expressions in the data source ensures that the deepfake software has a diverse range of expressions to learn from, which can improve the realism and quality of the final output.

  • Why is it important to have even lighting when creating a data source?

    -Even lighting is crucial to avoid shadows and unevenness in the facial features, which can interfere with the deepfake software's ability to accurately map and replace faces.

  • What does the term 'data underscore dst' refer to in the context of the tutorial?

    -In the context of the tutorial, 'data underscore dst' refers to the destination data, which is the video or images where the face replacement will be applied.

  • What file formats are recommended for extracting images from the video in the tutorial?

    -The tutorial recommends using the PNG file format for extracting images from the video due to its better quality compared to JPEG.

  • What is the purpose of the 'extract faces' step in the deepfake process?

    -The 'extract faces' step is used to identify and isolate faces from the source and destination videos, which is essential for the deepfake software to accurately replace the original faces with the desired ones.

  • Why is it necessary to review and potentially delete certain extracted faces during the alignment process?

    -Reviewing and deleting certain extracted faces during the alignment process is necessary to ensure that only clear and relevant facial images are used, which helps in creating a more accurate and higher quality deepfake.

  • What is the role of the 'mask edit' step in the deepfake tutorial?

    -The 'mask edit' step involves manually creating masks around the faces in the videos, which helps the deepfake software to better understand which parts of the image to focus on during the face replacement process.

  • How does the training of masks improve the deepfake result?

    -Training the masks helps the deepfake software to learn and recognize the specific facial features and variations, leading to a more precise and realistic face replacement in the final output.

  • What is the final step in the deepfake process as described in the tutorial?

    -The final step in the deepfake process described in the tutorial is merging the trained deepfake model with the original video to create a seamless and realistic face replacement.

Outlines

00:00

🎥 Introduction to Advanced Deepfake Tutorial

The speaker introduces an advanced deepfake tutorial, mentioning that they have been learning and experimenting with deepfakes for about two weeks. They give credit to '10 deep fakery' for providing tips and data sources. The speaker encourages viewers to vote for their CGI animated short film on My Road Reel 2020 and hints at submitting a live-action short film. They also discuss the importance of having varied facial expressions and even lighting when creating data sources for deepfakes.

05:01

🖥️ Setting Up Deep Face Lab and Extracting Images

The tutorial continues with setting up Deep Face Lab, explaining the need for a data source and a data destination. The speaker demonstrates how to extract images from a video, choosing a frame rate and file format. They emphasize the importance of having a good variety of facial expressions and even lighting in the source material. The process of extracting images from both the data source and data destination videos is detailed, including the use of batch files to automate the process.

10:03

🔍 Extracting and Aligning Faces

The speaker proceeds to explain how to extract faces from the source files using Deep Face Lab's automated feature. They discuss the options for choosing between CPU or GPU processing and the importance of selecting the right image size and quality. The tutorial then moves on to aligning the extracted faces, where the speaker shows how to view and manually delete any unwanted or poor-quality faces. They also mention the importance of including faces with eyes closed for better training results.

15:03

✂️ Masking and Training the Deepfake Model

The tutorial delves into the manual process of masking, where the speaker uses the egg sag editor to create masks around the faces in the video frames. They emphasize the need for consistency and detail in this step, as it greatly affects the final deepfake result. The speaker then demonstrates how to train the masks using the '5x segtrain bat' command in Deep Face Lab, explaining the process of training the model to recognize the masked faces.

20:09

🚀 Finalizing the Deepfake and Post-Processing

The final part of the tutorial covers the process of training the actual deepfake model, which involves using the masks that were previously trained. The speaker walks through the settings and options in Deep Face Lab, discussing the importance of training iterations and the impact of different settings on the training process. They also touch on the challenges of skin tone matching and the potential need for further post-processing in software like After Effects or Davinci Resolve.

Mindmap

Keywords

💡Deepfake

Deepfake refers to a synthetic media in which a person's likeness is superimposed onto someone else's body in a video or image. In the context of the video, the creator is teaching viewers how to create a deepfake using a specific software, highlighting the process of swapping faces in videos while discussing the nuances and technicalities involved.

💡Deep Face Lab

Deep Face Lab is a software application used for creating deepfakes. The video script mentions using NVIDIA Deep Face Lab, indicating that it's a tool that the creator uses to extract faces from videos and train the AI to recognize and replicate facial features for deepfake creation.

💡Data Source

In the script, 'data source' refers to the original video or set of images from which faces are being extracted for the deepfake. The creator emphasizes the importance of having a good data source with varied facial expressions and even lighting to ensure a more convincing deepfake result.

💡Data Destination

This term is used to describe the video or image onto which the face from the data source will be superimposed. The video tutorial explains how to prepare the data destination by extracting frames and aligning them correctly to match the facial features of the data source.

💡GPU

GPU stands for Graphics Processing Unit, which is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the video, the creator chooses to use the GPU for processing the deepfake because it can handle the intensive computational tasks more efficiently than a CPU.

💡Iterations

In the context of the video, 'iterations' refers to the number of times the AI training process is run. The creator mentions letting the training run for around 10,000 iterations, indicating that the AI needs to 'learn' by processing the data multiple times to improve the accuracy and quality of the deepfake.

💡Masking

Masking in the video script refers to the process of selecting and outlining the areas of the image that will be used in the deepfake, typically the face. The creator discusses using a tool to manually create masks around the face to ensure that only the desired parts are used in the final deepfake video.

💡Training

Training in this context means teaching the AI model to recognize and replicate facial features accurately. The video script describes training the AI with masks and through iterations to improve the deepfake result. The more training the AI undergoes, the better it becomes at creating convincing deepfakes.

💡XSeg

XSeg likely refers to a specific method or feature within the deepfake software for extracting and segmenting faces from the video frames. The creator mentions 'x segtrain bat', which could be a script or command used to initiate the training process for facial segmentation.

💡Color Transfer

Color transfer in the video script is a process used to match the color and lighting of the face in the data source to that of the data destination. This is done to make the deepfake appear more natural and seamless. The creator discusses using different settings for color transfer to improve the final result.

💡Merging

Merging in the context of the video refers to the final step of combining the trained deepfake face with the data destination video. The creator explains using a tool to merge the AI-generated face onto the video, adjusting for factors like skin tone and facial features to create a convincing deepfake.

Highlights

Introduction to an advanced deepfake tutorial for beginners.

The presenter shares their learning journey in deep faking, having started two weeks prior.

Mention of 10 Deep Fakery, who has been a helpful resource for the presenter.

A call to action for viewers to vote for the presenter's CGI animated short film.

Details on submitting a live-action short film to My Road Reel 2020.

Explanation of the need for even lighting and varied facial expressions in creating data sources.

The presenter demonstrates how to use NVIDIA Deep Face Lab for deepfake creation.

Instructions on extracting images from video at 12 frames per second for quality.

Importance of reviewing extracted images for quality control.

Process of extracting faces from the source file using automatic methods.

The necessity of sorting the extracted faces for better training results.

Techniques for manually masking faces in the Egg Sag Editor for precision.

The significance of training the masks to improve deepfake results.

How to apply the trained masks to the data source and destination.

The deepfake training process using the NVIDIA GPU.

Iterative improvement of deepfake results through training over 800,000 iterations.

Final steps in merging the deepfake into a single file and exporting it.

Challenges in achieving consistent skin tones in deepfakes.

Teaser for part two of the tutorial focusing on compositing in DaVinci Resolve and After Effects.