Deepface Live Tutorial - How to make your own Live Model! (New Version Available)

Druuzil Tech & Games
14 Apr 202297:19

TLDRIn this tutorial, the creator guides viewers through the process of making a live model for the Deepface application using the RTT model. The video covers the necessary hardware requirements, downloading and setting up the software, and the training process. With a focus on using Jim Varney's character, the creator demonstrates how to collect footage, extract and align faces, train the model, and troubleshoot common issues. The tutorial is aimed at those with some understanding of Deepface Lab and is designed to help users create a viable Deepface Live model in a few hours.

Takeaways

  • 😀 The tutorial provides a guide on creating a live model for the Deepface Live application.
  • 🎥 The process involves exporting a 'dfm' file which allows users to overlay a character on themselves using a webcam.
  • 💻 Prior knowledge of Deepface Lab is assumed, and a tutorial video on it is available on the channel.
  • 🖥️ A GPU with at least 12GB of video memory is recommended for efficient model training.
  • 💾 The tutorial uses an 'rtt' model pre-trained for 10 million iterations to expedite the learning process.
  • 🔧 Detailed steps are provided for extracting and preparing source footage, such as using Jim Varney's character.
  • 🌟 The 'rtm face set', containing 63,000 diverse faces, is crucial for training the model to recognize various facial features.
  • 🛠️ The video outlines the necessary software and files, including Deepface Lab and Deepface Live, and provides links for downloading.
  • 🔎 The importance of curating and manually editing the source material to ensure only the desired character's face is included is emphasized.
  • 🔁 The iterative process of training, extracting, and refining the model is discussed, highlighting the stages of training like random warp, learning rate dropout, and GAN.
  • 📹 The final step includes testing the live model using Deepface Live software and making adjustments to settings like color transfer mode and face merger for optimal results.

Q & A

  • What is the tutorial about?

    -The tutorial is about creating a live model for the Deepface Live application, which allows users to overlay a character onto themselves using a webcam.

  • What is a DFM file mentioned in the tutorial?

    -A DFM file is a Deepface Live model file that can be exported and used to apply a character overlay onto a live webcam feed.

  • Why is Deepface Lab knowledge considered a prerequisite for this tutorial?

    -Deepface Lab knowledge is considered a prerequisite because the tutorial assumes viewers have a basic understanding of how Deepface Lab works, which is crucial for comprehending the process of creating a live model.

  • What is the significance of the RTT model in the tutorial?

    -The RTT model is significant because it is a pre-trained model that has undergone 10 million iterations, allowing for faster learning and quicker setup of the live model compared to starting from scratch.

  • What hardware is recommended for training the live model?

    -The tutorial recommends an NVIDIA GPU with at least 11-12 gigs of video memory for efficient training of the live model.

  • Why is the RTM face set used in the tutorial?

    -The RTM face set, containing around 63,000 faces, is used to train the model against a diverse range of facial images, ensuring the final model can work with different users and lighting conditions.

  • What does the acronym 'XSEG' stand for in the context of the tutorial?

    -XSEG refers to the process of extracting and segmenting the face from the source material, which is crucial for training the model to recognize and apply the character overlay accurately.

  • Why is it important to curate the source material before extraction in the tutorial?

    -Curating the source material before extraction is important to ensure that only relevant frames containing the desired character are used, which helps in reducing training time and improving the model's accuracy.

  • What is the purpose of the generic XSEG training in the tutorial?

    -The purpose of the generic XSEG training is to initially segment the faces in the source material automatically, providing a base for further refinement and training to improve the model's facial recognition and overlay accuracy.

  • Why might the tutorial creator suggest training the model for a longer time?

    -The tutorial creator might suggest training the model for a longer time to achieve better results, as more iterations can lead to the model learning the source and destination characters more thoroughly, resulting in a more accurate and refined live model.

Outlines

00:00

🎥 Introduction to DeepFaceLab Tutorial

The speaker begins by introducing a tutorial on creating a custom model for the Face Live application using DeepFaceLab. They plan to demonstrate how to export a DFM file, allowing users to overlay any character onto their webcam feed. The tutorial assumes viewers have a basic understanding of DeepFaceLab, and references a previous tutorial for more details. The speaker also mentions using Jim Varney's character for the tutorial and provides a brief backstory on the actor.

05:01

💾 Preparing Source Character Footage

The speaker details the process of collecting footage for the source character, in this case, Jim Varney. They discuss the importance of having a GPU with sufficient video memory, recommending at least 12 gigs for the model files. The tutorial references the RTT model, which is pre-trained for faster learning. The speaker also provides advice on hardware requirements and the process of obtaining source character footage.

10:02

📚 Understanding DeepFaceLab Prerequisites

The speaker emphasizes the need for viewers to have prerequisite knowledge of DeepFaceLab, suggesting that they refer to a previous tutorial for a comprehensive understanding. They discuss the process of collecting source footage, mentioning the use of Blu-ray ripping as a method to obtain high-quality video. The speaker also touches on the importance of having the right hardware, specifically an NVIDIA GPU with ample video memory, and provides recommendations for suitable graphics cards.

15:05

💻 Setting Up DeepFaceLab Workspace

The speaker guides viewers through setting up the DeepFaceLab workspace, including the necessary files and folders. They discuss the RTM face set, which contains a variety of faces to help the model learn different appearances. The tutorial covers the process of extracting and aligning the source footage, as well as preparing the model folder with pre-trained files. The speaker also addresses potential issues with AMD cards and reiterates the preference for NVIDIA GPUs.

20:08

🔍 Extracting and Aligning Facial Images

The speaker demonstrates how to extract and align facial images from the source footage using DeepFaceLab. They discuss the process of curating the images to ensure they contain only the source character's face and deleting any irrelevant frames. The tutorial covers the use of batch files for extraction and alignment, and the speaker provides tips for managing the large number of images generated.

25:08

🖥️ Training the DeepFaceLab Model

The speaker begins the model training process in DeepFaceLab, explaining the settings and options involved. They discuss the use of the RTT model for faster training and the importance of training iterations. The tutorial covers the training process, including the use of learning rate dropout and random warp settings, and the speaker shares their approach to training based on observed results.

30:09

📉 Analyzing Training Progress and Loss Values

The speaker analyzes the training progress, focusing on loss values to determine how well the model is learning the source character. They discuss the importance of low loss values for both the source and destination characters, indicating a successful transfer. The tutorial covers the use of loss value graphs to monitor training and the speaker shares their observations on the model's performance.

35:10

🔧 Fine-Tuning and Finalizing the Model

The speaker fine-tunes the model by enabling additional training features such as GAN and color transfer mode. They discuss the impact of these settings on the model's performance and detail the process of finalizing the model. The tutorial covers the steps to export the DFM file for use in Face Live and the speaker shares their thoughts on the model's quality after training.

40:10

🎉 Conclusion and Testing the Live Model

The speaker concludes the tutorial by testing the live model in DeepFace Live. They discuss the results, highlighting the model's effectiveness and areas for potential improvement. The tutorial ends with a demonstration of the live model in action, showcasing the speaker's success in creating a custom Face Live character.

Mindmap

Keywords

💡Deepface Live

Deepface Live is a software application that allows users to overlay a character or person's face onto their own in real-time using a webcam. In the context of the video, the tutorial focuses on teaching viewers how to create their own live model for Deepface Live, which can be used for various purposes such as entertainment or content creation.

💡DFM file

A DFM file is a Deepface Model file, which is an essential component for Deepface Live. It contains the trained model data that enables the software to recognize and apply the facial features of a chosen character onto the user's face in real-time. The video tutorial guides users through the process of exporting a DFM file after training the model with their desired character.

💡Deep Face Lab

Deep Face Lab is a prerequisite software mentioned in the video that is used for creating deepfake videos. It is a tool that allows users to swap faces in videos with a high degree of accuracy. The video assumes viewers have a basic understanding of Deep Face Lab, as it builds upon those skills to teach creating live models for Deepface Live.

💡RTT model

The RTT model, or Ready To Train model, is a pre-trained model used in the tutorial to speed up the training process for creating a Deepface Live model. It has been pre-trained for 10 million iterations, allowing users to start training with a model that already has a substantial learning foundation, thus reducing the time and resources needed for training.

💡GPU

GPU, or Graphics Processing Unit, is a critical hardware component for training models in Deepface Live due to its parallel processing capabilities. The video recommends a GPU with at least 11-12 gigs of video memory for optimal training performance, as the high-resolution model files require significant graphical processing power and memory.

💡Training iterations

Training iterations refer to the number of times a model goes through the entire dataset during the training process. In the context of the video, the tutorial explains that using the RTT model allows for quick learning, with the model capable of achieving viable results within a few hours of training, depending on the hardware used.

💡XSeg

XSeg, or eXtended Segmentation, is a process mentioned in the video that involves refining the model's ability to accurately detect and segment facial features. It is part of the training process where the model learns to distinguish between the face and the background, which is crucial for precise face swapping in Deepface Live.

💡Color transfer mode

Color transfer mode is a feature in Deepface Live that helps to improve the realism of the facial swap by adjusting the coloration of the source face to better match the lighting and coloration of the destination face. The video discusses encountering issues with this mode, but it is generally used to enhance the final output's visual coherence.

💡Gan

GAN, or Generative Adversarial Network, is a concept related to deep learning that is used in the final stages of model training to refine the facial details and improve the overall quality of the deepfake. The video mentions enabling GAN to achieve sharper and more detailed results in the live model.

💡Loss value

Loss value is a metric used in machine learning to evaluate the performance of a model. In the video, the tutorial discusses monitoring loss values to determine how well the model is learning the source and destination faces. Lower loss values indicate that the model is effectively learning and improving.

Highlights

Tutorial on creating a live model for the Deepface application.

Exporting a 'dfm' file to overlay any character on oneself using a webcam.

Assumption of prior knowledge of Deepface Lab for this tutorial.

Recommendation of a GPU with 12GB of video memory for model training.

Introduction to the RTT model, pre-trained for 10 million iterations for faster learning.

Explanation of the RTM face set, containing 63,000 faces for model training diversity.

Details on downloading and setting up Deepface Lab and Deepface Live software.

Instructions for extracting video frames and aligning faces for source material.

Curation tips for selecting high-quality and relevant source images.

Process of extracting faces automatically from the source video.

Manual deletion of irrelevant or poorly aligned faces to improve training accuracy.

Training the model using the extracted and curated face images.

Use of the RTT model files to initialize training and the settings recommended for training.

Description of the iterative training process and the stages of model refinement.

Enabling advanced training features like GAN for improved model quality.

Final testing of the live model using Deepface Live software and webcam.

Troubleshooting tips and considerations for model training and live application.