DeepFaceLab 2.0 Easy Tutorial | Part 1 [ 2023 ]
TLDRThis tutorial guides viewers through using DeepFaceLab 2.0 for deepfaking. It covers downloading the software, selecting the appropriate version based on GPU, and extracting images from source and destination folders. The video explains the process of training a new model, adjusting settings like batch size and resolution, and emphasizes the importance of a powerful GPU for better results. The tutorial is designed for beginners, with plans for an advanced guide in the future.
Takeaways
- ๐ DeepFaceLab is a deep faking software that can convert faces from a source to a destination.
- ๐ง The software can be downloaded from various sources like GitHub, torrent, or mega, with specific builds for different GPU types.
- ๐พ After downloading, users need to extract DeepFaceLab to a chosen location and organize the workspace with 'source' and 'destination' folders.
- ๐ A 'model' file is present, which can be empty initially, but users can download pre-trained models from the face VFX website to speed up the process.
- ๐ผ๏ธ The software allows users to extract images from the source and destination folders, with options to set FPS and image format.
- ๐ค The face detection feature uses an algorithm to detect and extract faces from the images.
- ๐ป The training process involves creating a new model or using a pre-trained one, with settings adjustable based on the user's GPU capabilities.
- ๐๏ธ Users can adjust various settings during the training process, including batch size, resolution, and face type, to optimize results.
- โฑ๏ธ The training time can be extensive, especially when creating a model from scratch, but using pre-trained models can speed this up.
- ๐๏ธ After training, users can merge the frames and convert them into an MP4 file, representing the final deepfake video.
Q & A
What is DeepFaceLab and what does it do?
-DeepFaceLab is a deepfaking software that can convert any face from a source to a destination. It's used to replace faces in videos or images with a different face.
Where can I download DeepFaceLab from?
-DeepFaceLab can be downloaded from several sources including the GitHub repository, a torrent, or the Mega cloud storage service. The tutorial specifically mentions using a Mega link.
What are the different ways to download DeepFaceLab and how do they depend on my GPU?
-DeepFaceLab offers different download options based on the user's GPU. For instance, if you have an NVIDIA GPU, you might choose the version compatible with your GPU model, such as 'Nvidia up to 3080 ti'. The tutorial uses the 'RDX 3000 Series' build for a 360 ti GPU.
What is the purpose of the 'destination' and 'source' folders in DeepFaceLab?
-In DeepFaceLab, the 'destination' folder is where you put the face you want to change, and the 'source' folder contains the face you want to apply. Essentially, you are replacing the destination face with the source face.
Can you explain the role of the 'Model' file in DeepFaceLab?
-The 'Model' file in DeepFaceLab is initially empty and can be populated with pre-trained models downloaded from websites like facevfx.com. These models help to significantly increase the speed and quality of the deepfaking process.
What is the 'clear workspace' function in DeepFaceLab and why is it used with caution?
-The 'clear workspace' function in DeepFaceLab deletes the model, destination, and source folders. It's used with caution because accidental use can result in the loss of your work, so it's important to be confident before clearing the workspace.
How does the 'extract images from the source' step work in DeepFaceLab?
-This step extracts frames from the source video or image folder. The user is prompted to enter the FPS (frames per second) value, which determines how many frames are extracted. A value of zero means extracting every frame.
What is the significance of the 'data source face extract' step in the DeepFaceLab process?
-The 'data source face extract' step uses an algorithm to detect and extract faces from the source data. The user can choose to extract the whole face or just the face, and can adjust parameters like image resolution to improve extraction quality.
Why is GPU important in the DeepFaceLab process and how does it affect the speed?
-A powerful GPU is crucial for the DeepFaceLab process as it accelerates the computation required for tasks like face extraction and model training. A better GPU means faster processing times and the ability to handle higher resolutions and more complex models.
What is the 'train SE HD' step in DeepFaceLab and why is it time-consuming?
-The 'train SE HD' step involves creating a new model from scratch or using a pre-trained model. It's time-consuming because the software learns to map the source face onto the destination face, which requires significant computational power and can take hours depending on the complexity and the GPU's capabilities.
How does the 'merge to MP4' step work in DeepFaceLab?
-The 'merge to MP4' step is the final process in DeepFaceLab where the frames with the deepfaked faces are merged into a video file. The user can adjust settings like bitrate to improve the output video quality.
Outlines
๐ Introduction to DeepFaceLab
The video begins with an introduction to DeepFaceLab, a deepfake software that allows users to swap faces in videos. The presenter mentions that they will guide viewers through the process of downloading and using the software. They provide information on where to download DeepFaceLab, including options like GitHub, torrent, and mega. The presenter opts for the mega download and explains the various download options available based on the user's GPU. They detail the process of downloading and extracting the software, emphasizing the importance of choosing the correct version to match the user's graphics card. The video promises further guidance on advanced features and pre-trained models in upcoming videos.
๐ง Setting Up DeepFaceLab
The presenter walks through the initial setup of DeepFaceLab, explaining the workspace and the importance of the 'source' and 'destination' folders. They describe the process of extracting images from the source and destination folders, emphasizing the impact of the user's GPU on the speed of extraction. The video demonstrates how to extract frames from a video source, with the presenter advising on frame rates and image formats. They also touch on the use of pre-trained models from FaceVFX to enhance the deepfaking process and provide guidance on how to handle the extraction process, including options for whole face or face only, and the implications of these choices on the deepfake's quality.
๐ DeepFaceLab Training Basics
The video segment focuses on the training process within DeepFaceLab. The presenter explains the importance of training the software to create a model that can convincingly swap faces. They discuss the creation of a new model and the implications of training from scratch versus using a pre-trained model. The presenter provides insights into the training settings, such as batch size and resolution, and how these can affect the training time and quality. They also discuss the use of different model architectures and their impact on the deepfake's outcome. The video gives a live demonstration of the training process, showing the software's interface and the real-time updates as the model learns.
๐ Advanced Training and Model Saving
This part of the video delves into more advanced aspects of training in DeepFaceLab. The presenter discusses the various settings and options available for fine-tuning the training process, such as encoder dimensions, optimizers, and learning rates. They also explain how to save the model and create backups during training to prevent loss of progress. The video shows the console output during training, highlighting key metrics such as iteration time and deviation from the source face. The presenter demonstrates how to save the training progress and create backups, emphasizing the importance of these steps for long training sessions.
๐ฌ Finalizing the DeepFake Video
The final segment of the video covers the process of merging the trained model with the video frames to create the final deepfake video. The presenter explains the steps involved in merging the frames and converting them into a video file. They discuss the importance of resolution and processing time, and how these factors can affect the quality of the final output. The video shows a quick demonstration of the merging process, with the presenter emphasizing the potential for high-quality results with sufficient training and the right settings. The presenter concludes the tutorial by encouraging viewers to experiment with different settings and to look forward to more advanced tutorials in the future.
Mindmap
Keywords
๐กDeepFaceLab
๐กDeepfaking
๐กGPU
๐กSource
๐กDestination
๐กModel
๐กExtracting Images
๐กFPS
๐กTraining
๐กBatch Size
๐กResolution
Highlights
Introduction to DeepFaceLab, a deepfaking software.
DeepFaceLab can convert any face to another using deepfake technology.
The tutorial covers downloading DeepFaceLab using Mega.
Different download options are available based on GPU type.
Explanation of the workspace and the roles of 'source' and 'destination'.
Downloading pre-trained models from facevfx.com can speed up the process.
Clearing the workspace will delete the model, destination, and source.
Extracting images from the source at zero FPS.
Choosing the image format (PNG) for extraction.
Extracting images from the destination folder is crucial for smooth deepfakes.
Data source facer extract detects faces in the data source.
Options for whole face processing vs. face only.
Increasing image resolution to 100 for better face detection quality.
The training process creates a model from extracted faces.
Not recommended to train from scratch due to the long training time.
Settings for training, including batch size and resolution.
Using the DF model for better results at the cost of GPU power.
The training preview shows the faces being altered in real-time.
Saving the training progress and creating backups.
Merging the SE HD to apply the trained model to the frames.
Final step of merging to MP4 to complete the deepfake video.
The importance of GPU in the quality and speed of deepfakes.
The tutorial concludes with a basic deepfake result and a promise of more advanced tutorials.