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RHEL 8 Installation

[1] [2] [3] As of this writing, pip3.6 will install tensorflow 2.3.0 which does not support configproto[4]. The fix is to install an older version, in this case I just used what was listed on the stackoverflow post.

mkdir build && cd build
sudo dnf groupinstall "Development Tools"
sudo dnf install git
git clone ffmpeg && cd ffmpeg
./configure --disable-x86asm
sudo make install
cd ../
git clone && cd DeepFaceLab_Linux/scripts
chmod +x *
git clone
sudo pip3.6 install --upgrade pip
pip3.6 install --user colorama
pip3.6 install --user numpy
pip3.6 install --user scikit-build
pip3.6 install --user opencv-python-headless
pip3.6 install --user tqdm
pip3.6 install --user ffmpeg-python
pip3.6 install --user tensorflow==1.14
pip3.6 install --user pillow
pip3.6 install --user scipy
pip3.6 install --user tensorflow-gpu==1.13.2
pip3.6 install --user tensorflow-auto-detect


  • NOTE : AS of this writing opencl is not fully supported due to missing configproto conversion for newer versions of tesnorflow. This means no AMD or Intel GPU support.

My use case was a prank, which was an excuse to play around with the technology.

  • Downloaded three video conference calls where the target of the prank was prominent.
  • Using kdenlive; I removed all video containing other people, merged the remaining into one video, then removed all instances of the target covering their face. In the end I had almost 30 minutes of video.
  • I downloaded the destination video from youtube. It was an interview with someone that the target doesn't like, upon which the targets face will be placed. I will also play around with head swapping, but the destination has a lot more hair than the target.
  • The destination was a very short clip, but had other people in it. I cut out anything with other people, but will add them back in post swap.
  • I ran the following to get started
  • I copied the source video to build/DeepFaceLab_Linux/scripts/workspace/data_src.mp4
  • Copied destination video to build/DeepFaceLab_Linux/scripts/workspace/data_dst.mp4
  • At this point I extracted the frames from the source using defaults. This ran at .99x, so it took slightly longer than the video length.
  • Then I kicked off the facial extraction from the source, using defaults.

On my Dell Opiplex 9020M with i5-4590T and no video card, I was able to extract faces at ~3.22s/it. I have 51,368 frames and it appears to process each one at 3.22 seconds each. After 17 hours I was at ~37%.

  • Now extract the frames from the destination. In my case I edited the destination video to only contain the target face.
  • Now extract the faces. At this point I moved the process to my workstation at the office as it does have a GPU. However it only ran on my CPU. It is an AMD Ryzen 5 3400G with eight threads, but I wasn't running much faster. 2.90s/it vs 3.22s/it.

[5]*Now we can work on training. After I trained for several days I had a good start. However I noticed that the skin tones were not matching up, so I went back and did some editing of the source material to make it easier. I also noticed that using the whole face was messing up the hair on the target. So after running back over the source steps I can begin training again using partial face instead.



==---------- Model Options -----------==
==                                    ==
==            resolution: 256         ==
==             face_type: f           ==
==     models_opt_on_gpu: False       ==
==                 archi: df-u        ==
==               ae_dims: 256         ==
==                e_dims: 256         ==
==                d_dims: 256         ==
==           d_mask_dims: 84          ==
==       masked_training: True        ==
==             eyes_prio: True        ==
==           uniform_yaw: False       ==
==            lr_dropout: cpu         ==
==           random_warp: False       ==
==             gan_power: 0.0         ==
==       true_face_power: 0.0         ==
==      face_style_power: 0.0         ==
==        bg_style_power: 0.0         ==
==               ct_mode: none        ==
==              clipgrad: False       ==
==              pretrain: False       ==
==       autobackup_hour: 6           ==
== write_preview_history: False       ==
==           target_iter: 50000       ==
==           random_flip: True        ==
==            batch_size: 4           ==
==                                    ==

Messing Around