Master the Art of DeepFaceLab 2.0 with Xseg Tutorial (2024)

Master the Art of DeepFaceLab 2.0 with Xseg Tutorial

Introduction to Deep Face Lab 2.0 x Egg
Understanding X-Egg and Masking
The Importance of Custom Masks
Applying Pre-Trained Masks
Labeling Faces with Mask Polygons
Dealing with Obstructions
Modifying and Refining Masks
Fetching and Backing Up Labeled Faces
Training the X-Seg Model
Applying the Trained Mask
Checking and Cleaning the Applied Mask
Removing the Applied Mask
Conclusion

Deep Face Lab 2.0 x Egg is an advanced tool for deepfake creation and face swapping. This tutorial will guide You on how to effectively use the x Egg editor to draw masks on faces and train the model for applying it to face sets. Additionally, you will learn how to deal with obstructions, make backups of masks, and utilize pre-trained X-Seg masks to enhance the deepfake composition and likeness to the source face set. Whether you're a beginner or an experienced deepfake enthusiast, this tutorial will help you improve your deepfake creation skills.

To fully utilize Deep Face Lab 2.0 x Egg, it's essential to understand the concept of masking. X-Egg is a powerful masking tool included in Deep Face Lab, allowing you to specify the facial area and separate it from the background in an image. This mask plays a crucial role during model training and merging of the final image. By using X-Egg, you can achieve better composition, increased likeness to the source face set, realistic eye and mouth movement, and improved skin Detail and color.

While Deep Face Lab provides a default mask generated during face extraction, it is recommended to use custom masks for better results, especially for larger face types like whole face and head. Custom masks are particularly useful when utilizing model training, style powers, and color transfer modes. Deep Face Lab also includes a generic whole face X-Seg mask, which can serve as a starting point for many projects. However, extreme angles, dark or blurry images, and heavily obstructed faces may require a more refined custom mask for optimal performance.

The fastest way to start working with X-Egg is by applying pre-trained masks to your face sets. Deep Face Lab includes a generic whole face X-Seg mask, which can be readily applied. You also have the option to pre-train your own masks or download pre-trained ones. The generic mask files can be found in the internal/model/generic_exec folder. To apply the masks to your images, run the command "5.x_sig_generic_data_dst_whole_face_mask_apply" for both the face set and the source. However, keep in mind that the generic whole face mask may not deliver the same results for other face types.

To Create your own X-Seg mask, you need to label faces with mask polygons and then train the X-Seg model. This labeling process is essential for defining the lines and points that determine the mask Shape. To open the X-Seg editor, run the command "5.x_seg_data_dst_mask_edit". The editor interface consists of a canvas for labeling polygons, image carousel for navigation, polygon tools for drawing and editing, mask preview tools, and Cursor lock for easier image manipulation. By following the edge of the face and hairline, you can label the entire face with a mask. The editor allows you to undo and redo points, add or delete polygons, and switch between drawing modes.

Obstructions such as hands, hair, glasses, piercings, and tattoos can interfere with the mask's accuracy. Deep Face Lab provides two methods to exclude obstructions from the mask area. The first method involves drawing the polygon around the face while also including the edge of the obstruction. The Second method is to draw the face polygon as usual and then draw another polygon in exclusion mode around the obstruction. This method removes any part of the obstruction that intersects with or is inside the face polygon. It is crucial to remember to draw an occlusion mask around the face when using exclusion mode, as excluding the face alone is not sufficient.

After labeling a significant number of faces, you can modify and refine the mask polygons. By moving, adding, or deleting points, you can adjust the mask shape as needed. It's advisable to maintain consistency in the mask shape by following a similar path around the jaw and hairline in each labeled image. For smaller face types, focusing on the jawline and cutting across the forehead just above the eyebrows is recommended. In the case of head-type faces, it's important to include the entire face, ears, hair, and optionally part of the neck. Keep in mind that deep faking thin or moving hair can be challenging, and additional refinement may be required during post-processing.

To ensure the safety of your labeled faces, it's crucial to fetch a backup of them. Deep Face Lab provides a convenient command for this called "5.x_eg_data_dst_mask_fetch". Running this command will copy all labeled files to the "data_dst/aligned_exec" folder. You will be prompted to delete the original files, and it is recommended to do so to avoid confusion. If needed, you can remove all the X-Seg labels by using the command "5.x_sig_data_dst_mask_remove". This action will delete the drawn polygons and restore the default mask. However, it's important to note that this action cannot be undone.

Once you have labeled the faces and defined the mask polygons, the next step is to train the X-Seg model. Training the model allows it to create a mask Based on the provided labels. To start the training process, run the command "5.x_eg_train". Choose the hardware device from the list and set the face Type based on your face set. It's recommended to use the highest batch size, but if training fails, you can try a lower batch size. The training progress will be displayed in the command window, showing numerical values, while the preview window will show the images and masks being trained. You can cycle through previews using the space bar and generate the Current preview using the "p" key. As the training progresses, you'll Notice the mask taking on a consistent shape.

After training the X-Seg model, you can Apply the trained mask to the face set images. Running the command "5.x_sig_data_dst_trained_mask_apply" and selecting the device will apply the mask to the face set. If you have multiple GPUs, you can apply the source face set mask simultaneously. Otherwise, you can apply the source mask separately using the command "5.x_sig_data_src_trained_mask_apply". Before proceeding with model training, it is recommended to check the applied mask's cleanliness. You can reopen the X-Seg editor and toggle the applied mask using the backtick or tilde key. By scrolling through the images and creating new face labels if needed, you can refine the mask further. Remember that the applied mask doesn't have to be perfect, as it will be trained in the deepfake trainer.

To ensure the quality of the applied mask, it is advisable to examine it and clean any imperfections. By reopening the X-Seg editor and checking the applied mask, you can determine if any further adjustments are required. The command "5.x_sig_data_dst_trained_mask_remove" can be used to remove the applied mask and return to the default mask. Similarly, the command "5.x_sig_data_src_trained_mask_remove" removes the applied source mask. Removing the applied mask does not affect the labeled polygons, only the applied mask itself.

If necessary, you can completely remove all applied masks using the commands "5.x_sig_data_dst_trained_mask_remove" and "5.x_sig_data_src_trained_mask_remove". This action will delete the applied masks entirely and restore the default masks, allowing you to start fresh if needed.

Deep Face Lab 2.0 x Egg offers advanced capabilities for creating realistic deepfake content. By understanding the masking process, labeling faces with mask polygons, dealing with obstructions, and training the X-Seg model, you can create high-quality deepfakes that closely Resemble the source face set. Experiment with different techniques, refine your masks, and enhance your deepfake creation skills with Deep Face Lab 2.0 x Egg.