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 for the Deepface application using a pre-trained RTT model. The video covers the necessary hardware requirements, downloading and setting up the required software, and the step-by-step process of training the model with a focus on Jim Varney's character. The creator shares tips for curating source footage, extracting and aligning facial images, and adjusting training parameters for optimal results. The tutorial also includes troubleshooting advice and concludes with a live test of the Deepface Live model.
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's assumed that viewers have prior knowledge of Deepface Lab or have watched previous tutorials on the subject.
- 📹 The tutorial uses Jim Varney's character for demonstration, utilizing footage from the movie 'Ernest Goes to Jail'.
- 💾 The video covers how to collect source footage, recommending around 10-11 minutes for adequate training material.
- 💿 The presenter details the hardware requirements, suggesting an NVIDIA GPU with at least 12GB of VRAM for optimal training.
- 🔧 The tutorial introduces the 'RTT model', a pre-trained model that expedites the learning process for the source and destination characters.
- 📂 It walks through the necessary files needed for the process, including Deepface Lab software, RTM face set, and RTT model files.
- 🛠️ The extraction and alignment of facial images from the source footage is a key step, with the video demonstrating how to do this effectively.
- 🔧 The video also covers the training process, including the use of various settings and the significance of iteration counts in refining the model.
- 🔗 Links to download necessary software and files are provided in the video description for ease of access.
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 the prerequisite knowledge for this tutorial?
-Understanding of how Deepface Lab works is a prerequisite, as the tutorial assumes viewers have watched a Deepface Lab tutorial or have knowledge of its workings.
Who is Jim Varney and why is he used in the tutorial?
-Jim Varney was an actor known for his role in the 'Ernest' movie series. He is used in the tutorial because of his distinct facial expressions and humor, which are suitable for demonstrating the facial recognition capabilities of the software.
What hardware is recommended for training the model?
-A GPU with at least 11-12 gigs of video memory is recommended, with specific mention of the RTX 3090 and RTX A6000, as they can handle the high dimensions and resolutions required for training.
What is the RTT model and why is it used in the tutorial?
-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 process of creating a viable Deepface Live model.
What is the purpose of the RTM face set in the tutorial?
-The RTM face set, containing about 63,000 faces, is used to train the model against a diverse range of facial images to ensure it can work with different people and lighting conditions.
What file formats are mentioned for source material in the tutorial?
-The tutorial mentions using MPEG-4 and MKV file formats for source material, with a note that MKVs need to be converted to MPEG-4 for use in Adobe Premiere Pro.
Why is manual curation of extracted video frames suggested in the tutorial?
-Manual curation of extracted video frames is suggested to remove frames that do not contain the source character's face or are of poor quality, which helps improve the training process and final model accuracy.
What is the significance of the 'xseg' training mentioned in the tutorial?
-The 'xseg' training is used to improve the model's ability to accurately detect and segment the face from the source material, which is crucial for a high-quality facial swap in Deepface Live.
Why is the color transfer mode used in the tutorial?
-The color transfer mode is used to adapt the model better to different lighting conditions, ensuring the final facial swap looks natural and consistent across various environments.
Outlines
🎥 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 a character using a webcam. The tutorial assumes viewers have a basic understanding of Deep Face Lab, and the speaker plans to test the model after explaining the process. The speaker also mentions using Jim Varney's character for the tutorial and briefly discusses the importance of having a GPU with sufficient video memory for the task.
💻 Prerequisites and Software Requirements
The speaker outlines the prerequisites for the tutorial, including having a NVIDIA GPU with at least 11-12 GB of video memory, and having watched previous Deep Face Lab tutorials. The necessary software includes Deep Face Lab, the RTM face set, and the RTT model files. The speaker also provides links to these resources and explains the purpose of each, emphasizing the importance of the RTT model for faster training.
📁 Setting Up the Workspace and Extracting Files
The speaker guides viewers through setting up the workspace for the Deep Face Live project. This involves extracting the Deep Face Lab software, the RTM face set, and the RTT model files. The speaker also discusses the importance of having a clean workspace with empty folders for source, destination, and model files, and provides instructions on how to rename and organize the extracted files.
🎞️ Preparing Source Material and Extracting Frames
The speaker explains the process of preparing the source material, which involves collecting footage of the character to be used. In this case, the speaker has collected footage of Jim Varney from the movie 'Ernest Goes to Jail'. The speaker also demonstrates how to extract frames from the video source to be used for training the model.
🖼️ Curating and Extracting Faces from Source Material
The speaker discusses the importance of curating the extracted frames to ensure only the desired character's face is included in the training data. This involves manually reviewing and deleting frames that do not contain the character's face or are of poor quality. The speaker also demonstrates how to extract faces from the curated frames using Deep Face Lab.
🤖 Training the Deep Face Live Model
The speaker begins the training process for the Deep Face Live model, starting with the application of a generic x-seg to the source material. The speaker explains the iterative process of training, involving the use of different settings and techniques to improve the model's accuracy. The speaker also discusses the use of the RTM face set for training the destination character.
🖱️ Manual Editing and Further Training
The speaker addresses the need for manual editing of the training data to improve the model's performance. This includes manually tracing faces that were not accurately detected and retraining the model. The speaker also discusses the iterative nature of training, explaining how to adjust settings and continue training for better results.
🔄 Packing the Face Set and Finalizing Training
The speaker demonstrates how to pack the curated and trained face set into an archive for faster loading during training. The speaker also discusses the final steps of training, including the use of advanced settings and the importance of saving the model at different stages of training.
🔍 Reviewing Training Progress and Testing the Model
The speaker reviews the progress of the training, discussing the improvements in the model's performance and the details captured in the character's face. The speaker also explains the process of testing the model using Deep Face Live software and provides a live demonstration of the model's effectiveness.
🔧 Troubleshooting and Adjusting Training Settings
The speaker encounters issues during the training process, such as errors related to color transfer mode. The speaker troubleshoots these issues and adjusts the training settings accordingly. The speaker also discusses the importance of monitoring loss values and other performance metrics during training.
🌐 Finalizing the Model and Preparing for Upload
The speaker finalizes the model after achieving satisfactory results from the training. The speaker discusses the process of exporting the model as a Deep Face Live compatible file and prepares it for upload. The speaker also reflects on the overall training process and the quality of the final model.
📹 Testing the Live Model and Conclusion
The speaker tests the live model using Deep Face Live software and a webcam. The speaker evaluates the model's performance in real-time and makes adjustments to the settings for optimal results. The speaker concludes the tutorial by summarizing the process and inviting viewers to ask questions or request future tutorials.
Mindmap
Keywords
💡Deepface Live
💡DFM file
💡Deep Face Lab
💡GPU
💡RTT Model
💡Training iterations
💡VRAM
💡Source character
💡Destination footage
💡XSeg
Highlights
Tutorial on creating a live model for the Face Live application.
Exporting a dfm file to overlay a character on oneself using a webcam.
Assumption of prior knowledge of Deep Face Lab for this tutorial.
Using Jim Varney's character for the tutorial example.
Collection of footage from 'Ernest Goes to Jail' movie.
Recommendation of a GPU with at least 12 gigs of video memory for 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.
Description of the files needed for the tutorial.
Instructions on downloading and setting up Deep Face Lab software.
Details on using the RTM face set for training diversity.
Process of extracting images from the source video.
Importance of curating source images for effective training.
Extraction of facial features from the curated images.
Manual deletion of unwanted faces and bad alignments.
Training the model using the extracted and curated facial images.
Adjustment of training settings for optimal results.
Enabling advanced training features like GAN for improved model quality.
Final testing of the live model using Deep Face Live software.