DeepFaceLab 2.0 Pretraining Tutorial

Deepfakery
15 Feb 202311:38

TLDRThis tutorial guides viewers on how to expedite the deepfake process by pre-training models in DeepFaceLab 2.0. It offers a beginner-friendly introduction to the training settings and explains how to use the SAE HD trainer. The video covers modifying the default face set, navigating model pre-training settings, and optimizing VRAM usage. It also details the training process, including setting up the model, adjusting batch size for system stability, and interpreting loss values for training accuracy. The tutorial encourages community sharing of pre-trained models and provides tips for troubleshooting common errors.

Takeaways

  • 😀 Pre-trained models in DeepFaceLab can accelerate the deepfake process by using a diverse set of facial images.
  • 🔧 The tutorial focuses on the SAE HD trainer, which is the standard for most deep fakes and does not require additional images or videos.
  • 📁 Users can modify or replace the default pre-trained face set by using the unpack and pack scripts.
  • 💻 Pre-training settings are simplified, focusing on main model architecture and parameters, with many options overridden by the software.
  • 🖥️ The tutorial guides users to select settings based on their GPU's VRAM capacity and suggests starting with the 'liae' architecture.
  • 📊 The model settings table on deepphakedvfx.com helps users choose appropriate settings for their hardware.
  • 🔄 Batch size is a critical parameter that determines system resource usage and can be adjusted during training for stability.
  • 🖼️ Higher resolution generally leads to better clarity in deepfakes, but it's limited by the GPU's capabilities.
  • 🧠 The 'liae' architecture is recommended for its ability to capture destination image qualities, compared to the original 'DF' architecture.
  • 🔁 Pre-training can be stopped and resumed at any time, allowing for flexibility in training schedules.
  • 🤝 The community at deepfakevfx.com shares and archives pre-trained models, encouraging collaboration and improvement.

Q & A

  • What is the purpose of creating pre-trained models in DeepFaceLab?

    -The purpose of creating pre-trained models in DeepFaceLab is to speed up the DeepFake process by using a face set consisting of thousands of images with a wide variety of angles, facial expressions, color, and lighting conditions.

  • What is included in the default pre-trained face set in DeepFaceLab?

    -DeepFaceLab includes a default pre-trained face set derived from the Flickr Faces HQ dataset.

  • What are the requirements to start pre-training a model in DeepFaceLab?

    -The only requirement to start pre-training a model in DeepFaceLab is the DeepFaceLab software itself; no other images or videos are needed.

  • Which trainer does this tutorial focus on for pre-training models?

    -This tutorial focuses on the SAE HD trainer for pre-training models, as it is the standard for most DeepFakes and does not offer a pre-training option in the quick 96 and AMP models.

  • How can you modify or replace the default pre-trained face set in DeepFaceLab?

    -To modify or replace the default pre-trained face set, navigate to the internal pre-trained faces folder, copy the file to one of your aligned folders, and use the unpack script to check, add, or remove images. Then, use the pack script and place the resulting faceset.pac file into the pre-trained faces folder.

  • What is the recommended naming convention for the model when pre-training in DeepFaceLab?

    -A recommended naming convention for the model includes some of the model parameters for easy reference, keeping it short and avoiding special characters or spaces.

  • What is the significance of the batch size during the pre-training process in DeepFaceLab?

    -The batch size determines how many images are processed per iteration and is the main setting to adjust system resource usage to a stable level during pre-training.

  • How does the resolution setting affect the clarity and performance during pre-training in DeepFaceLab?

    -The resolution setting is a main determining factor in the clarity of the resulting DeepFake. Higher resolutions are better for clarity but have a limit based on GPU capabilities. It also impacts the performance, as higher resolutions require more VRAM.

  • What are the two types of model architectures mentioned in the tutorial, and what do they represent?

    -The two types of model architectures mentioned are DF (DeepFakes original architecture), which is more biased toward the source material, and LIAE, which has an easier time picking up the qualities of the destination images.

  • What does the 'U' option in the model architecture do, and how does it affect VRAM usage?

    -The 'U' option in the model architecture increases similarity to the source, which can improve the result quality but also increases VRAM usage.

  • How can you determine when to stop pre-training a model in DeepFaceLab?

    -You can determine when to stop pre-training a model by using the loss graph and preview image. Once the graph flattens out and the trained faces look similar to the original images, it's a good time to save, backup, and exit the trainer.

Outlines

00:00

🚀 Introduction to Pre-Training Deep Fake Models

This paragraph introduces the concept of pre-training models in Deep Face Lab to accelerate the deep fake process. It explains that a pre-trained model uses a diverse face set to learn various facial features, expressions, and lighting conditions. The paragraph emphasizes that no additional images or videos are needed for pre-training, as Deep Face Lab provides a default face set. It also guides users on how to access, modify, or replace the default face set using the unpack and pack scripts. The focus is on using the SAE HD trainer, which is the standard for most deep fakes, and it provides a brief overview of the pre-training settings and the process of getting started with training.

05:02

🛠 Navigating Deep Face Lab's Training Settings

The second paragraph delves into the specifics of setting up Deep Face Lab for model pre-training. It advises users to consult a guide on deepphakedvfx.com for model training settings tailored to their hardware. The paragraph outlines the steps for selecting the appropriate VRAM capacity, model architecture, and other parameters. It also covers how to start the training process, including naming the model, choosing the device, setting auto backup intervals, and adjusting batch size. The importance of using a batch size divisible by the number of GPUs and selecting the right resolution for the GPU's capabilities are highlighted. Additionally, it discusses model architecture options and the impact of various settings on VRAM usage and training outcomes.

10:03

📈 Monitoring and Adjusting Training Progress

The final paragraph focuses on the practical aspects of monitoring and adjusting the training process in Deep Face Lab. It describes the SAE HD trainer interface, explaining the model summary, loss values, and how to interpret the training progress displayed in the command prompt window. The paragraph provides instructions on how to save, back up, or stop training, as well as how to restart it. It also offers tips on increasing the batch size for faster training and how to handle out-of-memory errors by adjusting model parameters. The guidance extends to troubleshooting errors, optimizing the system, and the option to continue pre-training at a later time. Lastly, it encourages sharing pre-trained models with the community and ends with a call to action for viewer engagement.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is a software used for creating deepfake videos, which are synthetic media in which a person's face is replaced with another person's face. In the context of the video, DeepFaceLab is the primary tool used for pre-training models to speed up the deepfake process. The script mentions that no other images or videos are required for pre-training a model with DeepFaceLab, as it includes a default face set.

💡Pre-trained model

A pre-trained model in the video refers to a model that has been trained on a large dataset of images with various angles, expressions, and lighting conditions. This type of model can significantly speed up the deepfake process by providing a foundation that can be fine-tuned for specific tasks. The video serves as a tutorial on how to create such models using DeepFaceLab.

💡Flickr Faces HQ dataset

The Flickr Faces HQ dataset is a collection of high-quality face images used for training deep learning models. In the video, it is mentioned that DeepFaceLab includes a face set derived from this dataset, which is used for pre-training models. This dataset provides the variety of images needed to train models that can handle different facial features and expressions.

💡SAE HD trainer

SAE HD trainer is a specific training configuration within DeepFaceLab that is used for creating high-definition deepfake models. The video focuses on this trainer because it is the standard for most deepfakes and supports pre-training, unlike the quick 96 and amp models which do not offer pre-training options.

💡VRAM

VRAM, or Video Random Access Memory, is the memory used by a GPU (Graphics Processing Unit) to store image data for rendering. In the video, managing VRAM is crucial for running the model trainer on one's system. The script guides users on how to choose settings that work with their GPU's VRAM capacity to ensure smooth training of the deepfake models.

💡Batch size

Batch size in the context of the video refers to the number of images processed per iteration during model training. It is a key parameter that affects system resource usage and can be adjusted to maintain a stable training process. The script advises users to select a batch size that is divisible by the number of GPUs being used to ensure efficient training.

💡Resolution

Resolution in the video pertains to the clarity and detail of the deepfake images produced by the model. Higher resolution generally results in better quality deepfakes but also requires more VRAM and processing power. The script suggests choosing a resolution that is divisible by 16 or 32, depending on the model architecture, to optimize training.

💡Model architecture

Model architecture refers to the design and structure of the neural network used in DeepFaceLab for creating deepfakes. The video mentions two types of architectures: DF (DeepFakes) and LIAE, with LIAE being more versatile in capturing destination image qualities. The choice of architecture can influence the training process and the final output of the deepfake model.

💡Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. In the video, the dimensions of the autoencoder are adjustable parameters that affect the model's precision in detecting and reproducing facial features, colors, etc. Higher dimensions can improve model accuracy but at the cost of increased VRAM usage.

💡Pre-train mode

Pre-train mode in DeepFaceLab is a setting that enables the pre-training of models using a large dataset of images. The video explains how to enable this mode and emphasizes its importance for creating models that can be further fine-tuned for specific deepfake tasks. Pre-training helps in achieving better results by leveraging a wide variety of facial data.

💡OOM error

OOM error, short for 'Out of Memory' error, is a common issue encountered during model training when the system does not have enough VRAM to handle the current batch size or model settings. The video provides guidance on how to address this issue by adjusting the batch size or other model parameters to prevent the trainer from crashing.

Highlights

Tutorial on speeding up the Deep fake process by creating pre-trained models.

Introduction to Deep face lab training settings for beginners.

Pre-trained models are created with a diverse face set for better results.

Deep face lab includes a face set derived from the flicker faces HQ data set.

Pre-training a model requires only Deep face lab, no additional images or videos needed.

Focus on the SAE HD trainer, which is the standard for most deep fakes.

How to view, modify, or replace the default pre-trained face set.

Instructions on using your own images for pre-training.

Guidance on navigating to model pre-trained settings in Deep face lab.

Explanation of managing VRAM and getting the model trainer running on your system.

How to choose settings suggested by other Deep face lab users for your hardware.

Details on setting up the model name and choosing your device for training.

Recommendations for setting auto backup, batch size, and resolution.

Information on model architectures and options for better results.

The importance of choosing the right face type for training.

How to define the dimensions of the autoencoder for model precision.

Instructions on enabling pre-train mode and starting the training process.

Troubleshooting tips for out of memory errors during training.

How to use the SAE HD trainer interface and interpret model training progress.

Advice on raising the batch size for faster training.

Guidance on when to stop pre-training based on the loss graph and preview image.

Options for continuing pre-training at a later time.

Invitation to share your pre-trained model with the community.