Deepface Live Tutorial - How to make your own Live Model! (New Version Available)
TLDRIn this tutorial, the creator guides viewers through the process of making a live model using Deepface Lab for the Deepface Live application. The video assumes viewers have a basic understanding of Deepface Lab and focuses on using the pre-trained RTT model to quickly generate a DFM file. The creator shares tips on hardware requirements, specifically the need for an NVIDIA GPU with ample video memory, and walks through each step, from collecting footage to training the model. The tutorial also covers common issues like color transfer and provides solutions to improve facial expression details in the final live model.
Takeaways
- 😀 The tutorial provides a guide on creating a live model for the Deepface Live application.
- 🎥 It assumes viewers have a basic understanding of Deepface Lab, referencing a previous tutorial for more details.
- 💾 The video recommends having a GPU with at least 12GB of video memory for optimal training performance.
- 🕵️♂️ The presenter chooses Jim Varney's character for the tutorial, having collected footage from 'Ernest Goes to Jail'.
- 📁 The process involves using a pre-trained RTT model to speed up the training process, avoiding the need to start from scratch.
- 🔧 The tutorial covers the extraction of facial images from video footage, a crucial step for training the model.
- 🖼️ It highlights the importance of curating the source images to include only the character's face for accurate model training.
- 🛠️ The presenter discusses the use of various settings within Deepface Lab software to refine the model training.
- 🔗 The video mentions the need for the RTM face set, which contains diverse facial images to enhance the model's adaptability.
- 🔧 The process includes several stages of training with different settings, such as random warp and learning rate dropout, to improve the model's accuracy.
- 📹 The final step is testing the live model using Deepface Live software, demonstrating how the character overlays on the user's webcam feed.
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 on 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 overlay a character onto a live video feed from a webcam.
What is assumed knowledge for this tutorial?
-The tutorial assumes that viewers have watched the Deepface Lab tutorial or have an understanding of how Deepface Lab works.
Who is Jim Varney and why is he used in the tutorial?
-Jim Varney was an actor known for his role in the 'Ernest' series of movies. He is used in the tutorial because of his expressive facial movements and humor.
What is the recommended hardware for training the model?
-The recommended hardware is an NVIDIA GPU with at least 11-12 gigs of video memory, such as an RTX 3080, 3080 Ti, or an RTX 3090.
What is the RTT model and why is it used?
-The RTT model is a pre-trained model with 10 million iterations that allows for faster learning of the source and destination characters, speeding up the training process.
What is the purpose of the RTM face set in the tutorial?
-The RTM face set, containing about 63,000 images of various faces, is used to train the model against a diverse set of facial expressions and conditions, improving its adaptability.
Why is the tutorial not recommended for AMD cards?
-The tutorial does not recommend AMD cards due to compatibility issues and slower performance, as AMD has acknowledged problems with the software's operation on their hardware.
What is the process of creating a Deepface Live model?
-The process involves extracting and aligning facial images, training the model using the RTT model, and iteratively refining the model through stages of training with different settings.
How does the tutorial handle potential copyright issues?
-The tutorial avoids showing copyrighted material directly and advises users to be cautious when using copyrighted content, such as movie clips, for creating their models.
Outlines
🎥 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 DeepFaceModel (DFM) file, allowing users to overlay a character onto their webcam feed. The tutorial assumes viewers have some knowledge of DeepFaceLab, and references to a previous tutorial are made. The speaker also mentions their choice of using Jim Varney's character for the demonstration and briefly discusses the process of collecting footage.
💻 System Requirements and Software Setup
The speaker details the system requirements for the tutorial, recommending an NVIDIA GPU with at least 12GB of VRAM for optimal performance. They discuss the use of the RTT model, which is pre-trained for faster learning. The paragraph covers the necessary software and files, including DeepFaceLab, the RTM face set, and the RTT model files. Links to download these resources are promised in the video description. The speaker also touches on the incompatibility issues with AMD cards and suggests using NVIDIA for better results.
📂 Organizing Files and Extracting Footage
The speaker guides viewers through the process of extracting and organizing the necessary files for the tutorial. They discuss the structure of the DeepFaceLab software, the creation of a workspace folder, and the importance of having an empty 'aligned' folder. The paragraph also covers the extraction of video footage into frames and the deletion of irrelevant files to streamline the training process.
🖼️ Selecting and Preparing Source Images
The speaker emphasizes the importance of selecting high-quality source images for the training process. They discuss the process of extracting faces from the video frames and the initial curation of these images to ensure they contain only the desired character. The paragraph also covers the deletion of irrelevant or poor-quality images to improve the training efficiency.
🕵️♂️ Manual Review and Editing of Extracted Images
The speaker describes the manual review process of the extracted images, focusing on removing any images that do not contain the target character or have poor alignment. They discuss the use of the 'underscore' naming convention to identify and delete unwanted images. The paragraph also covers the speaker's personal experience with the extraction process and their decision to manually curate the image set to ensure quality.
🛠️ Advanced Image Editing and Model Training
The speaker moves on to advanced image editing, discussing the use of the 'xseg' tool for refining the facial recognition in the images. They cover the process of training the model using the refined images and the importance of using a high-quality image set for better results. The paragraph also includes the speaker's decision to manually edit some images for better training outcomes.
🔄 Iterative Training and Model Refinement
The speaker discusses the iterative nature of model training, emphasizing the need for multiple training sessions to improve the model's accuracy. They cover the process of applying the trained model to the source images and the use of various settings to enhance the training process. The paragraph also includes the speaker's decision to save and transfer the training progress to another machine for continued training.
🔍 Finalizing the Model and Preparing for Live Testing
The speaker接近s the final stages of model training, discussing the use of GAN (Generative Adversarial Networks) to further refine the model. They cover the process of saving the model and preparing it for live testing using the DeepFaceLive software. The paragraph also includes the speaker's anticipation of the model's performance and their plans for testing it in a live setting.
🎬 Live Testing and Model Evaluation
The speaker conducts live testing of the trained model using the DeepFaceLive software. They discuss the process of setting up the software, selecting the trained model, and adjusting various settings for optimal performance. The paragraph includes the speaker's real-time evaluation of the model's accuracy and their observations on the model's performance during the live test.
📝 Conclusion and Future Plans
The speaker concludes the tutorial by summarizing the process and sharing their thoughts on the model's performance. They discuss the potential for further training to improve the model and consider the possibility of creating models of other characters. The paragraph also includes the speaker's invitation for feedback and suggestions for future tutorial topics.
Mindmap
Keywords
Deepface Live
DFM file
Deep Face Lab
RTT model
GPU
VRAM
RTM face set
Training iterations
Color transfer mode
Gan
Highlights
Tutorial on creating a live model for the Deepface Live application.
Exporting a dfm file to overlay any character on yourself using a webcam.
Assumption of prior knowledge of Deepface Lab for this tutorial.
Recommendation of a GPU with at least 12GB of video memory for model training.
Introduction of 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 on extracting and preparing source footage for model training.
Importance of using high-resolution source material for better model training results.
Process of extracting facial images from the source video for Deepface Lab.
Curation of extracted facial images to ensure quality and relevance.
Techniques for aligning and preparing facial images for model training.
Description of the model training process and expected outcomes.
Recommendation for hardware specifications for efficient model training.
Importance of manual editing of facial images to improve model accuracy.
Explanation of the iterative process of model training and refinement.
Final testing and demonstration of the live model using Deepface Live software.