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kvrooman committed Dec 28, 2018
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22 changes: 20 additions & 2 deletions .gitignore
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*
!setup.cfg
!*.keep
!*.py
!*.md
!*.txt
!*.png
!*.h5
!*.pb
!*.dat
!*.npy
!Dockerfile*
!requirements*
!icons
!.cache
!lib
!lib/face_alignment
!lib/gui
!lib/gui/.cache/preview
!lib/gui/.cache/icons
!scripts
!plugins
!plugins/
!plugins/*
!plugins/extract/*
!plugins/model/*
!tools
!tools/lib*

*.pyc
__pycache__/
204 changes: 188 additions & 16 deletions INSTALL.md
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# Installing Faceswap
- [Installing Faceswap](#installing-faceswap)
- [Prerequisites](#prerequisites)
- [Hardware Requirements](#hardware-requirements)
- [Supported operating systems](#supported-operating-systems)
- [Important before you proceed](#important-before-you-proceed)
- [General Install Guide](#general-install-guide)
- [Installing dependencies](#installing-dependencies)
- [Getting the faceswap code](#getting-the-faceswap-code)
- [Setup](#setup)
- [About some of the options](#about-some-of-the-options)
- [Run the project](#run-the-project)
- [Notes](#notes)
- [Windows Install Guide](#windows-install-guide)
- [Prerequisites](#prerequisites-1)
- [Microsoft Visual Studio 2015](#microsoft-visual-studio-2015)
- [Cuda](#cuda)
- [cuDNN](#cudnn)
- [CMake](#cmake)
- [Anaconda](#anaconda)
- [Git](#git)
- [Setup](#setup-1)
- [Anaconda](#anaconda-1)
- [Set up a virtual environment](#set-up-a-virtual-environment)
- [Entering your virtual environment](#entering-your-virtual-environment)
- [Faceswap](#faceswap)
- [Easy install](#easy-install)
- [Manual install](#manual-install)
- [Running Faceswap](#running-faceswap)
- [Create a desktop shortcut](#create-a-desktop-shortcut)
- [Updating faceswap](#updating-faceswap)
- [Dlib](#dlib)
- [Build Latest Dlib with GPU Support](#build-latest-dlib-with-gpu-support)
- [Easy install of Dlib without GPU Support](#easy-install-of-dlib-without-gpu-support)

# Prerequisites
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.

Expand All @@ -16,7 +51,7 @@ The type of computations that the process does are well suited for graphics card
Desktop cards later than the 7xx series are most likely supported.
- **A lot of patience**

## Supported operating systems:
## Supported operating systems
- **Windows 10**
Windows 7 and 8 might work. Your milage may vary
- **Linux**
Expand All @@ -31,7 +66,7 @@ Alternatively there is a docker image that is based on Debian.

The developers are also not responsible for any damage you might cause to your own computer.

# Installation Instructions
# General Install Guide
## Installing dependencies
- Python >= 3.2
- apt/yum install python3 (Linux)
Expand All @@ -50,16 +85,14 @@ Simply download the code from http://github.com/deepfakes/faceswap - For develop

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.

## Setting up for our project

### Setup
## Setup
Enter the folder that faceswap has been downloaded to and run:
```bash
python setup.py
```
If setup fails for any reason you can still manually install the packages listed within requirements.txt

### About some of the options:
### About some of the options
- 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.
Expand All @@ -78,21 +111,18 @@ 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
2. Install Nvidia-Docker & Restart Docker Service
https://github.com/NVIDIA/nvidia-docker
4. Build Docker Image For Faceswap
3. Build Docker Image For Faceswap
docker build -t deepfakes-gpu -f Dockerfile.gpu .
5. Mount faceswap volume and Run it
4. Mount faceswap volume and Run it
# without gui. tools.py gui not working.
docker run -p 8888:8888 \
nvidia-docker run --rm -it -p 8888:8888 \
--hostname faceswap-gpu --name faceswap-gpu \
-v /opt/faceswap:/srv \
faceswap-gpu
deepfakes-gpu
# with gui. tools.py gui working.
## enable local access to X11 server
Expand All @@ -109,9 +139,9 @@ INFO 1. Install Docker
-e VIDEO_GID=`getent group video | cut -d: -f3` \
-e GID=`id -g` \
-e UID=`id -u` \
faceswap-gpu
deepfakes-gpu
6. Open a new terminal to interact with the project
5. Open a new terminal to interact with the project
docker exec faceswap-gpu python /srv/tools.py gui
```

Expand Down Expand Up @@ -161,3 +191,145 @@ Proceed to [../blob/master/USAGE.md](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](https://github.com/deepfakes/faceswap-playground) repository instead of the main repo.

# Windows Install Guide
Setting up Faceswap can seem a little intimidating to new users, but it isn't that complicated, although a little time consuming. It is recommended to use Linux where possible as Windows will hog about 20% of your GPU Memory, making Faceswap run a little slower, however using Windows is perfectly fine and 100% supported.

## Prerequisites
### Microsoft Visual Studio 2015
**Important** Make sure to downoad the 2015 version of Microsoft Visual Studio

Download and install Microsoft Visual Studio 2015 from: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409

On the install screen:
- Select "Custom" then click "Next"\
![MSVS Custom](https://i.imgur.com/Bx8fjzT.png)
- Uncheck all previously checked options
- Expand "Programming Languages" and select "Visual C++"\
![MSVS C++](https://i.imgur.com/c8k1IYD.png)
- Select "Next" and "Install"


### Cuda
**GPU Only** If you do not have an Nvidia GPU you can skip this step.

At the time of writing Tensorflow (version 1.12) only supports Cuda up to version 9.0, but check https://www.tensorflow.org/install/gpu for the latest supported version. It is crucial that you download the correct version of Cuda.

Download and install the correct version of the Cuda Toolkit from: https://developer.nvidia.com/cuda-toolkit-archive

NB: Make a note of the install folder as you'll need to access it in the next step.

### cuDNN
**GPU Only** If you do not have an Nvidia GPU you can skip this step.

As with Cuda you will need to install the correct version of cuDNN that the latest Tensorflow supports. At the time of writing this is Tensorflow v1.12 which supports cuDNN version 7.2, but check https://www.tensorflow.org/install/gpu for the latest supported version.

Download cuDNN from https://developer.nvidia.com/cudnn. You will need to create an account with Nvidia.

At the bottom of the list of latest cuDNN release will be a link to "Archived cuDNN Releases". Select this and choose the latest version of cuDNN that supports the version of Cuda you installed and has a minor version greater than or equal to the latest version that Tensorflow supports. (Eg Tensorflow 1.12 supports Cuda 9.0 and cuDNN 7.2. There is not an archived version of cuDNN 7.2 for Cuda 9.0, so select cuDNN version 7.3)
- Open the zip file
- Extract all of the files and folders into your Cuda folder (It is likely to be located in `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA`):\
![cudnn to cuda](https://i.imgur.com/X098w0N.png)

### CMake
Install the latest stable release of CMake from https://cmake.org/download/. (Scroll down the page for Latest Releases and select the relevant Binary distribution installer for your OS).

When installing CMake make sure to enable the option to CMake to the system path:
![cmake path](https://i.imgur.com/XTtacdY.png)


### Anaconda
Download and install the latest Python 3 Anacconda from: https://www.anaconda.com/download/. Unless you know what you are doing, you can leave all the options at default.

### Git
Download and install Git for Windows: https://git-scm.com/download/win. Unless you know what you are doing, you can leave all the options at default.

## Setup
Reboot your PC, so that everything you have just installed gets registered.

### Anaconda
#### Set up a virtual environment
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Select "Create" at the bottom
- In the pop up:
- Give it the name: faceswap
- **IMPORTANT**: Select python version 3.5
- Hit "Create" (NB: This may take a while as it will need to download Python 3.5)
![Anaconda virtual env setup](https://i.imgur.com/Tl5tyVq.png)

#### Entering your virtual environment
To enter the virtual environment:
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Hit the ">" arrow next to your faceswap environment and select "Open Terminal"
![Anaconda enter virtual env](https://i.imgur.com/rKSq2Pd.png)

### Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Get the Faceswap repo by typing: `git clone https://github.com/deepfakes/faceswap.git`
- Enter the faceswap folder: `cd faceswap`

#### Easy install
- Enter `python setup.py` and follow the prompts.

If you have issues/errors follow the Manual install steps below.

#### Manual install
If dlib failed to install you can follow the steps to [manually install dlib](#dlib).\
Once dlib is installed follow these steps:

- Install tkinter (required for the GUI) by typing: `conda install tk`
- Install requirements: `pip install -r requirements.txt`
- Install Tensorflow (either GPU or CPU version depending on your setup):
- GPU Version: `pip install tensorflow-gpu`
- Non GPU Version: `pip install tensorflow`

## Running Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following to see the list of commands: `python faceswap.py -h` or enter `python faceswap.py gui` to launch the GUI

## Create a desktop shortcut
A desktop shortcut can be added to easily launch staight into the faceswap GUI:

- Open Notepad
- Paste the following:
```
%USERPROFILE%\Anaconda3\envs\faceswap\python.exe %USERPROFILE%/faceswap/faceswap.py gui
```
- Save the file to your desktop as "faceswap.bat"

## Updating faceswap
It's good to keep faceswap up to date as new features are added and bugs are fixed. To do so:
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following `git pull --all`
- Once the latest version has downloaded, make sure your requirements are up to date: `pip install --upgrade -r requirements.txt`

## Dlib
You should only need to follow these steps if you want the latest Dlib code or the process was unable to install Dlib for you.

For reasons outside of our control, this is the trickiest part of the process, and most of the prerequisites you installed are to support just Dlib. It is recommended to build Dlib from source for 3 main reasons:
1) To get the latest version
2) Enable GPU Support in Dlib
3) To prevent yourself running into a whole host of issues later in the process.

If you are not bothered about having GPU support or the latest version, scroll to the end of this section for a simple one-liner to install the CPU version of Dlib.
### Build Latest Dlib with GPU Support
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- In the terminal type: `git clone https://github.com/davisking/dlib.git`
- Enter the dlib folder: `cd dlib`
- Add Visual Studio to your path by typing: `SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin`
- Enter: `python setup.py -G "Visual Studio 14 2015" install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA --clean`

This will build and install dlib for you. It is worth backing up the generated .egg file somewhere so that you can re-install it if you ever need to rather than having to re-compile:
- From within the dlib folder copy the file named `dlib-xx.yy.zz-py3.5-win-amd64.egg` to somewhere safe
- If you ever need to install it again, then from within your virtual environment enter: `python -m easy_install <path to saved .egg>`

Once Dlib is built, you can remove Visual Studio and CMake from your PC.

### Easy install of Dlib without GPU Support
NB: Don't do this if you have already compiled Dlib with GPU support.
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- In the terminal type: `conda install -c conda-forge dlib`
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