Machine learning essentially involves a ton of trial and error. You're letting a program try millions of different settings to land on an algorithm that sort of does what you want it to do. This process is really really slow unless you have the hardware required to speed this up.
The type of computations that the process does are well suited for graphics cards, rather than regular processors. It is pretty much required that you run the training process on a desktop or server capable GPU. Running this on your CPU means it can take weeks to train your model, compared to several hours on a GPU.
TL;DR: you need at least one of the following:
- A powerful CPU
- Laptop CPUs can often run the software, but will not be fast enough to train at reasonable speeds
- A powerful GPU
- Currently only Nvidia GPUs are supported. AMD graphics cards are not supported. This is not something that we have control over. It is a requirement of the Tensorflow library.
- The GPU needs to support at least CUDA Compute Capability 3.0 or higher. To see which version your GPU supports, consult this list: https://developer.nvidia.com/cuda-gpus Desktop cards later than the 7xx series are most likely supported.
- A lot of patience
- Windows 10 Windows 7 and 8 might work. Your milage may vary
- Linux Most Ubuntu/Debian or CentOS based Linux distributions will work.
- macOS GPU support on macOS is limited due to lack of drivers/libraries from Nvidia.
Alternatively there is a docker image that is based on Debian.
In its current iteration, the project relies heavily on the use of the command line, although a gui is available. if you are unfamiliar with command line tools, you may have difficulty setting up the environment and should perhaps not attempt any of the steps described in this guide. This guide assumes you have intermediate knowledge of the command line.
The developers are also not responsible for any damage you might cause to your own computer.
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Python >= 3.2
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virtualenv and virtualenvwrapper may help when you are not using docker.
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If you are using an Nvidia graphics card You should install CUDA (https://developer.nvidia.com/cuda-zone) and CUDNN (https://developer.nvidia.com/cudnn). If you do not plan to build Tensorflow yourself, make sure you install no higher than version 9.0 of CUDA and 7.0.x of CUDNN
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dlib is required for face recognition and is compiled as part of the setup process. You will need the following applications for your os to successfully install dlib (nb: list may be incomplete. Please raise an issue if another prerequisite is required for your OS):
- Windows: Visual Studio 2015, CMake v3.8.2
- Linux: build-essential, cmake
- macOS: xquartz
Simply download the code from http://github.com/deepfakes/faceswap - For development it is recommended to use git instead of downloading the code and extracting it.
For now, extract the code to a directory where you're comfortable working with it. Navigate to it with the command line. For our example we will use ~/faceswap/ as our project directory.
Enter the folder that faceswap has been downloaded to and run:
python setup.pyIf setup fails for any reason you can still manually install the packages listed within requirements.txt
- CUDA: For acceleration. Requires a good nVidia Graphics Card (which supports CUDA inside)
- Docker: Provide a ready-made image. Hide trivial details. Get you straight to the project.
- nVidia-Docker: Access to the nVidia GPU on host machine from inside container.
CUDA with Docker in 20 minutes.
INFO The tool provides tips for installation
and installs required python packages
INFO Setup in Linux 4.14.39-1-MANJARO
INFO Installed Python: 3.6.5 64bit
INFO Installed PIP: 10.0.1
Enable Docker? [Y/n]
INFO Docker Enabled
Enable CUDA? [Y/n]
INFO CUDA Enabled
INFO 1. Install Docker
https://www.docker.com/community-edition
2. Install latest CUDA
CUDA: https://developer.nvidia.com/cuda-downloads
3. Install Nvidia-Docker & Restart Docker Service
https://github.com/NVIDIA/nvidia-docker
4. Build Docker Image For Faceswap
docker build -t deepfakes-gpu -f Dockerfile.gpu .
5. Mount faceswap volume and Run it
# without gui. tools.py gui not working.
docker run -p 8888:8888 \
--hostname faceswap-gpu --name faceswap-gpu \
-v /opt/faceswap:/srv \
faceswap-gpu
# with gui. tools.py gui working.
## enable local access to X11 server
xhost +local:
## enable nvidia device if working under bumblebee
echo ON > /proc/acpi/bbswitch
## create container
nvidia-docker run -p 8888:8888 \
--hostname faceswap-gpu --name faceswap-gpu \
-v /opt/faceswap:/srv \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
-e AUDIO_GID=`getent group audio | cut -d: -f3` \
-e VIDEO_GID=`getent group video | cut -d: -f3` \
-e GID=`id -g` \
-e UID=`id -u` \
faceswap-gpu
6. Open a new terminal to interact with the project
docker exec faceswap-gpu python /srv/tools.py gui
A successful setup log, without docker.
INFO The tool provides tips for installation
and installs required python packages
INFO Setup in Linux 4.14.39-1-MANJARO
INFO Installed Python: 3.6.5 64bit
INFO Installed PIP: 10.0.1
Enable Docker? [Y/n] n
INFO Docker Disabled
Enable CUDA? [Y/n]
INFO CUDA Enabled
INFO CUDA version: 9.1
INFO cuDNN version: 7
WARNING Tensorflow has no official prebuild for CUDA 9.1 currently.
To continue, You have to build your own tensorflow-gpu.
Help: https://www.tensorflow.org/install/install_sources
Are System Dependencies met? [y/N] y
INFO Installing Missing Python Packages...
INFO Installing tensorflow-gpu
INFO Installing pathlib==1.0.1
......
INFO Installing tqdm
INFO Installing matplotlib
INFO All python3 dependencies are met.
You are good to go.
Once all these requirements are installed, you can attempt to run the faceswap tools. Use the -h or --help options for a list of options.
python faceswap.py -hor run with gui to launch the GUI
python faceswap.py guiProceed to ../blob/master/USAGE.md
This guide is far from complete. Functionality may change over time, and new dependencies are added and removed as time goes on.
If you are experiencing issues, please raise them in the faceswap-playground repository instead of the main repo.