RHEL 8 Installation
   As of this writing, pip3.6 will install tensorflow 2.3.0 which does not support configproto. 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 https://git.ffmpeg.org/ffmpeg.git ffmpeg && cd ffmpeg ./configure --disable-x86asm make sudo make install cd ../ git clone https://github.com/nagadit/DeepFaceLab_Linux.git && cd DeepFaceLab_Linux/scripts chmod +x * git clone https://github.com/iperov/DeepFaceLab.git 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.
*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.
./6_train_SAEHD_no_preview.sh ... ==---------- 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 == == ==
DeepFaceLab_Linux-master/scripts/DeepFaceLab/core/leras/nn.py tf.compat.v1.ConfigProto tf.compat.v1.Session()