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 Live application. The video assumes prior knowledge of Deepface Lab and focuses on using the pre-trained RTT model for faster training. It covers the necessary hardware requirements, downloading and setting up the software, preparing source footage, and training the model. The creator shares tips for optimizing training, including using a high-performance GPU and adjusting settings for better results. The tutorial also addresses common issues and provides solutions, culminating in a test of the live model using a webcam.

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 onto themselves using a webcam.
  • 💻 It is assumed that viewers have a basic understanding of Deepface Lab, as the tutorial does not cover it extensively.
  • 📹 The tutorial uses Jim Varney's character for demonstration, utilizing footage from 'Ernest Goes to Jail'.
  • 💾 The video recommends having a GPU with at least 12GB of video memory for optimal training performance.
  • 🔧 The tutorial covers the use of the RTT model, which is pre-trained for 10 million iterations to speed up the learning process.
  • 🔗 Links to necessary software and resources like Deepface Lab and the RTM face set are provided in the video description.
  • 🛠️ The process includes extracting frames from video, aligning faces, and training the model through several stages.
  • 🔧 Emphasis is placed on the importance of using high-quality source material and manually curating the dataset for best results.
  • 🕒 The tutorial acknowledges that training can take several hours to a few days, depending on hardware capabilities.
  • 🎓 The final part of the tutorial demonstrates how to export the trained model as a 'dfm' file for use in Deepface Live.

Q & A

  • What is the tutorial about?

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

  • What is a DFM file in the context of Deepface Live?

    -A DFM file is a file format used in Deepface Live that contains the trained model, which enables the application to recognize and overlay the chosen character onto a live video 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, including how to obtain and process source footage for character modeling.

  • 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.

  • Who is the character used as an example in the tutorial?

    -The character used as an example in the tutorial is Jim Varney, known for his role in the 'Ernest' movie series.

  • What is the RTT model mentioned in the tutorial?

    -The RTT model is a pre-trained model with 10 million iterations that accelerates the training process for Deepface Live, allowing for quicker learning of the source and destination characters.

  • How long does it typically take to train a viable Deepface Live model with the RTT model?

    -With the RTT model and appropriate hardware, a viable Deepface Live model can be trained in a couple of hours.

  • What is the purpose of the RTM face set in the training process?

    -The RTM face set, containing about 63,000 faces, is used to train the model against various facial expressions, lighting conditions, and skin colors, ensuring the final model works well with different users and lighting environments.

  • Why is it important to curate the source material before training?

    -Curating the source material before training is important to ensure that the model learns accurately from relevant and high-quality images, which can significantly improve the final model's performance.

  • What is the significance of the 'xseg' training mentioned in the tutorial?

    -The 'xseg' training is significant as it helps the model learn to accurately segment and recognize the face in the source material, which is crucial for a precise facial overlay in Deepface Live.

Outlines

00:00

🎥 Introduction to Deep Face Live Tutorial

The speaker introduces a tutorial on creating a custom model for the Face Live application, allowing users to overlay their face with any character in real-time using a webcam. The tutorial assumes viewers have prior knowledge of Deep Face Lab, and the speaker references a previous tutorial for detailed instructions. The focus is on using the pre-trained RTT model for faster training, and the speaker mentions the need for a GPU with sufficient video memory for optimal performance.

05:01

💻 System Requirements and Software Setup

The speaker discusses the system requirements for the tutorial, emphasizing the need for an NVIDIA GPU with at least 11-12 GB of VRAM. They advise against using AMD cards due to compatibility issues. The necessary software includes Deep Face Lab, the RTM face set for training diversity, and the RTT model files. Links to download these are provided in the video description. The speaker also outlines the process of downloading and preparing the files for the tutorial.

10:02

📂 Organizing Files and Extracting Footage

The speaker guides viewers through organizing the downloaded files, creating a workspace for the tutorial, and extracting footage for the source character. They demonstrate how to set up the Deep Face Lab software, prepare the RTM face set, and organize the model files. The extraction of video frames from a movie is discussed, along with the speaker's choice of using Jim Varney as the source character for the tutorial.

15:05

🖼️ Curating and Extracting Source Images

The speaker explains the process of curating the extracted video frames to ensure only the desired character's images are used for training. They discuss the importance of manual curation to remove irrelevant frames and focus on the quality of the source material. The tutorial continues with the extraction of faces from the curated images, which is a time-consuming process but essential for accurate facial recognition and training.

20:08

🛠️ Configuring Training Settings and Starting the Process

The speaker configures the training settings in Deep Face Lab, discussing the options for model training, including the use of random warp and learning rate dropout. They start the training process and explain the significance of the training iterations, which will improve the model's accuracy. The speaker also shares their approach to training, which differs slightly from the official tutorial by Iperov.

25:08

🔍 Reviewing and Refining Training Results

The speaker reviews the initial training results, discussing the need for further refinement through manual editing of the training mask. They demonstrate how to use the Deep Face Lab's mask editing tools to improve the facial recognition accuracy. The tutorial highlights the importance of a good training mask for achieving a convincing facial swap in the final output.

30:09

🔧 Finalizing the Training and Preparing for Deep Face Live

The speaker finalizes the training process by applying the refined mask and continuing the training iterations. They discuss the use of the RTT model and the impact of training duration on the model's performance. The tutorial concludes with the preparation for exporting the trained model for use in Deep Face Live, setting the stage for the real-time facial swap testing.

35:10

📹 Testing the Deep Face Live Model

The speaker tests the trained model using the Deep Face Live software, adjusting settings to optimize the real-time facial swap. They encounter some issues with the color transfer mode but manage to resolve them. The tutorial demonstrates the effectiveness of the trained model in applying Jim Varney's face onto the live video feed, showcasing the potential for creating realistic and dynamic facial swaps.

40:10

🔄 Iterating and Enhancing the Facial Swap Model

The speaker iterates over the training process, making adjustments and retraining to enhance the facial swap model's performance. They discuss the iterative nature of the training, highlighting the need for patience and fine-tuning to achieve the best results. The tutorial emphasizes the importance of testing and refining the model to ensure a seamless and convincing facial swap in Deep Face Live.

45:12

🎉 Conclusion and Future Plans

The speaker concludes the tutorial by summarizing the process and sharing their thoughts on the final results. They express satisfaction with the facial swap model's performance and discuss potential areas for improvement. The speaker also hints at future projects, including plans to train models of other characters and further explore the capabilities of Deep Face Lab and Deep Face Live.

Mindmap

Keywords

💡Deepface Live

Deepface Live is a software application that allows users to create live facial swap models, enabling them to overlay their face with a character in real-time using a webcam. In the context of the video, the tutorial focuses on teaching viewers how to use Deepface Live to create their own facial swap models, which can be used for various purposes such as entertainment or special effects.

💡DFM file

A DFM file, or Deepface Model file, is an output generated by the Deepface software suite. It contains the trained model data required for facial recognition and facial swapping. In the video, the creator guides users through the process of exporting a DFM file, which is a crucial step in making a character overlay work with Deepface Live.

💡Deep Face Lab

Deep Face Lab is a software that uses deep learning to create high-quality facial swap models. It is mentioned in the video as a prerequisite knowledge for users who want to follow the Deepface Live tutorial. The video assumes that viewers have some understanding of Deep Face Lab, as it is foundational for working with Deepface Live.

💡RTT model

The RTT model, or Ready-To-Train model, is a pre-trained model in Deepface Live that has undergone 10 million iterations of training. It is designed to accelerate the learning process for new models. The video emphasizes the use of the RTT model to quickly train the facial swap model with a character, reducing the time required for training compared to starting from scratch.

💡GPU

A GPU, or Graphics Processing Unit, 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 recommends a GPU with at least 11-12 GB of video memory for training the Deepface Live model due to the high computational demands of the process.

💡VRAM

VRAM, or Video Random Access Memory, is a type of computer memory used for storing images and is specific to the needs of 3D rendering and video editing. The video script mentions the importance of having sufficient VRAM, particularly when working with high-resolution and high-dimension models in Deepface Live.

💡Training iterations

Training iterations refer to the number of times a deep learning model goes through the entire dataset during the training process. The video discusses the use of a pre-trained model with 10 million iterations as a starting point, and then additional iterations to fine-tune the model for the user's specific character and footage.

💡XSeg

XSeg, or eXtended Segmentation, is a process within the Deepface software that involves training the model to accurately detect and segment the face in the source material. The video describes using XSeg to improve the facial recognition and overlay quality by manually editing and training the mask to ensure precise facial alignment.

💡Color transfer mode

Color transfer mode is a feature in Deepface Live that adjusts the color and lighting of the source material to better match the destination footage, resulting in a more seamless facial swap. The video script describes issues the creator faced with this mode, but it is generally used to improve the realism of the final output.

💡Gan

GAN, or Generative Adversarial Network, is a type of artificial intelligence algorithm used in the final stages of training to refine the model and improve the quality of the facial swap. The video mentions enabling GAN to achieve a more detailed and realistic representation of the source character on the destination footage.

Highlights

Tutorial on creating a live model for the Face Live application

Exporting a dfm file to overlay any character on yourself using a webcam

Assumption of prior knowledge of Deep Face Lab

Collection of 10-11 minutes of footage from 'Ernest Goes to Jail' movie

Recommendation of a GPU with 12 gigs of video memory for model training

Introduction of the RTT model, pre-trained for 10 million iterations

Explanation of the RTT model's faster learning capabilities

Hardware recommendations for optimal training performance

Details on downloading necessary files for Deep Face Live

Instructions on extracting and setting up Deep Face Lab software

Description of the RTM face set and its role in model training

Process of extracting images from the source video

Curation of extracted images to ensure quality training data

Extraction of facial features from the curated images

Dealing with incorrect or poor facial alignments in the extraction process

Importance of using only the correct character's images to avoid training errors

Training the model using the prepared source and destination datasets

Adjusting training settings for optimal results

Enabling advanced training features like GAN for enhanced model performance

Final testing and demonstration of the live model using Deep Face Live software