Easy Deepfake tutorial for beginners Xseg

The speaker begins by addressing the audience and introducing an advanced deepfake tutorial. They mention that their previous tutorial was basic, and this one will be an improvement since it's the method they currently use. The speaker admits they are still learning and have only been practicing deepfaking for about two weeks. They give a shoutout to '10 Deep Fakery' for providing tips and data sources. The speaker also encourages the audience to vote for their CGI-animated short film in a competition and mentions they will share winnings with subscribers. They discuss their plans to submit a live-action short film and provide an update on its post-production status. The tutorial focuses on using NVIDIA Deep Face Lab and creating data sources with proper lighting and facial variations for better results. The speaker points out issues with shadows in their current data source and advises on how to avoid such problems.

The speaker proceeds with the tutorial by explaining the process of setting up data sources for deepfaking using NVIDIA Deep Face Lab. They guide the audience on how to create a data destination and source, emphasizing the need to delete and replace files as necessary. The tutorial continues with instructions on extracting images from video data sources at a specific frame rate to balance quality and file count. The speaker demonstrates the extraction process and advises on using the PNG format for better image quality. They also show how to view the extracted images and prepare for the next steps in the workflow.

The tutorial moves on to the automated extraction of faces from the source files using Deep Face Lab. The speaker opts for GPU acceleration and provides specific settings for the extraction process. They explain the importance of reviewing the extracted faces for quality and consistency, ensuring there are no obstructions or blurriness. The audience is shown how to delete any unsuitable faces and retain those with eyes closed, as they are crucial for the deepfaking process. The speaker also demonstrates how to sort the aligned faces using histogram similarities for better organization. The tutorial then covers the process of extracting faces from the data destination video, with a focus on ensuring the accuracy of facial alignment.

The speaker introduces the manual masking process, which is crucial for achieving better deepfake results. They use the 'egg sag editor' to create masks around the faces in the data source and destination, focusing on jawlines and facial variations. The tutorial emphasizes the importance of thorough masking, including moments when the subject's eyes are closed. The speaker demonstrates how to train the masks using the trained masks to improve the deepfake model's learning process. They advise running the training for a significant number of iterations to ensure the model learns the masks effectively.

The tutorial concludes with the final steps of training the deepfake model and merging the results into a single video file. The speaker discusses the settings for training the model, including the use of masks and other parameters that affect the learning process. They highlight the importance of choosing the right resolution and batch size based on the available hardware capabilities. The speaker also demonstrates the merging process, applying the trained masks, and adjusting settings for color transfer and mask blurring to improve the final output. The tutorial ends with a preview of the deepfake video, showcasing the effectiveness of the process and the potential need for further refinement in post-production software.