Difference between revisions of "DeepFaceLab"

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==Usage==
 
==Usage==
 +
*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.
 
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.
 
*Downloaded three video conference calls where the target of the prank was prominent.
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./3_extract_image_from_data_dst.sh
 
./3_extract_image_from_data_dst.sh
 
</pre>
 
</pre>
*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. 2.91s/it vs 3.22s/it.
+
*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.
 
<pre>
 
<pre>
 
./5_data_dst_extract_faces_S3FD.sh
 
./5_data_dst_extract_faces_S3FD.sh
 
</pre>
 
</pre>

Revision as of 09:53, 21 September 2020

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 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

Usage

  • 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
./env.sh
./1_clear_workspace.sh
  • I copied the source video to build/DeepFaceLab_Linux/scripts/data_src.mp4
  • Copied destination video to build/DeepFaceLab_Linux/scripts/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.
./2_extract_image_from_data_src.sh
  • Then I kicked off the facial extraction from the source, using defaults.
./4_data_src_extract_faces_S3FD.sh

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.
./3_extract_image_from_data_dst.sh
  • 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_data_dst_extract_faces_S3FD.sh