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Sorry, no more linux/mac/docker support. If someone want to support it - create linux/mac fork, I will reference them in readme.

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iperov 2019-01-01 19:48:03 +04:00
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132
DockerCPU.md
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# For Mac Users
If you just have a **MacBook**.DeepFaceLab **GPU** mode does not works. However,it can also works with **CPU** mode.Follow the Steps below will help you build the **DRE** (DeepFaceLab Runtime Environment) Easier.
### 1. Open a new terminal and Clone DeepFaceLab with git
```
$ git git@github.com:iperov/DeepFaceLab.git
```
### 2. Change the directory to DeepFaceLab
```
$ cd DeepFaceLab
```
### 3. Install Docker
[Docker Desktop for Mac](https://hub.docker.com/editions/community/docker-ce-desktop-mac)
### 4. Build Docker Image For DeepFaceLab
```
$ docker build -t deepfacelab-cpu -f Dockerfile.cpu .
```
### 5. Mount DeepFaceLab volume and Run it
```
$ docker run -p 8888:8888 --hostname deepfacelab-cpu --name deepfacelab-cpu -v $PWD:/notebooks deepfacelab-cpu
```
PS: Because your current directory is `DeepFaceLab`,so `-v $PWD:/notebooks` means Mount `DeepFaceLab` volume to `notebooks` in **Docker**
And then you will see the log below:
```
The Jupyter Notebook is running at:
http://(deepfacelab-cpu or 127.0.0.1):8888/?token=your token
```
### 6. Open a new terminal to run DeepFaceLab in /notebooks
```
$ docker exec -it deepfacelab-cpu bash
$ ls -A
```
### 7. Use jupyter in deepfacelab-cpu bash
```
$ jupyter notebook list
```
or just open it on your browser `http://127.0.0.1:8888/?token=your_token`
PS: You can run python with jupyter.However,we just run our code in bash.It's simpler and clearer.Now the **DRE** (DeepFaceLab Runtime Environment) almost builded.
### 8. Stop or Kill Docker Container
```
$ docker stop deepfacelab-cpu
$ docker kill deepfacelab-cpu
```
### 9. Start Docker Container
```
# start docker container
$ docker start deepfacelab-cpu
# open bash to run deepfacelab
$ docker exec -it deepfacelab-cpu bash
```
PS: `STEP 8` or `STEP 9` just show you the way to stop and start **DRE**.
### 10. enjoy it
```
# make sure you current directory is `/notebooks`
$ pwd
# make sure all `DeepFaceLab` code is in current path `/notebooks`
$ ls -a
# read and write permission
$ chmod +x cpu.sh
# run `DeepFaceLab`
$ ./cpu.sh
```
### Details with `DeepFaceLab`
#### 1. Concepts
![SRC](doc/DF_Cage_0.jpg)
In our Case,**Cage**'s Face is **SRC Face**,and **Trump**'s Face is **DST Face**.and finally we get the **Result** below.
![Result](doc/merged-face.jpg)
So,before you run `./cpu.sh`.You should be aware of this.
#### 2. Use MTCNN(mt) to extract faces
Do not use DLIB extractor in CPU mode
#### 3. Best practice for SORT
1) delete first unsorted aligned groups of images what you can to delete.
2) use `hist`
#### 4. Use `H64 model` to train and convert
Only H64 model reasonable to train on home CPU.You can choice other model like **H128 (3GB+)** | **DF (5GB+)** and so on ,it depends entirely on your CPU performance.
#### 5. execute the script below one by one
```
root@deepfacelab-cpu:/notebooks# ./cpu.sh
1) clear workspace 7) data_dst sort by hist
2) extract PNG from video data_src 8) train
3) data_src extract faces 9) convert
4) data_src sort 10) converted to mp4
5) extract PNG from video data_dst 11) quit
6) data_dst extract faces
Please enter your choice:
```
#### 6. Put all videos in `workspace` directory
```
.
├── data_dst
├── data_src
├── dst.mp4
├── model
└── src.mp4
3 directories, 2 files
```

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Dockerfile.cpu
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FROM tensorflow/tensorflow:latest-py3
RUN apt-get update -qq -y \
&& apt-get install -y libsm6 libxrender1 libxext-dev python3-tk\
&& apt-get install -y ffmpeg \
&& apt-get install -y wget \
&& apt-get install -y vim \
&& apt-get install -y git \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
COPY requirements-cpu-docker.txt /opt/
RUN pip3 install cmake
RUN pip3 --no-cache-dir install -r /opt/requirements-cpu-docker.txt && rm /opt/requirements-cpu-docker.txt
WORKDIR "/notebooks"
CMD ["/run_jupyter.sh", "--allow-root"]

38
LINUX.md
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## **GNU/Linux installation instructions**
**!!! FFmpeg and NVIDIA Driver shall be already installed !!!**
First of all, i strongly recommend to install Anaconda, that it was convenient to work with DeepFaceLab.
Official instruction: https://docs.anaconda.com/anaconda/install/linux
After, you can create environment with packages, needed by DeepFaceLab:
```
conda create -y -n deepfacelab python==3.6.6 cudatoolkit==9.0 cudnn
```
Then activate environment:
```
source activate deepfacelab
```
And install the remained packages:
```
python -m pip install \
pathlib==1.0.1 \
scandir==1.6 \
h5py==2.7.1 \
Keras==2.1.6 \
opencv-python==3.4.0.12 \
tensorflow-gpu==1.8.0 \
scikit-image \
dlib==19.10.0 \
tqdm \
git+https://www.github.com/keras-team/keras-contrib.git
```
Now clone the repository and run... Good luck ;-)
```
git clone https://github.com/iperov/DeepFaceLab
cd DeepFaceLab && chmod +x main.sh && ./main.sh
```
**NOTE !!! Before launching DeepFaceLab, you should convince in that you already executed "source activate deepfacelab" !!!**
P.S. English is not my native language, so please be kind to my mistakes.

3
README.md
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@ -176,9 +176,6 @@ Video tutorial: https://www.youtube.com/watch?v=K98nTNjXkq8
Windows 10 consumes % of VRAM even if card unused for video output. Windows 10 consumes % of VRAM even if card unused for video output.
### For Mac Users
Check out [DockerCPU.md](DockerCPU.md) for more detailed instructions.
### **Problem of the year**: ### **Problem of the year**:
algorithm of overlaying neural face onto video face located in ConverterMasked.py. algorithm of overlaying neural face onto video face located in ConverterMasked.py.

71
cpu.sh
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#!/bin/bash
INTERNAL_DIR=`pwd`
WORKSPACE=$INTERNAL_DIR/workspace
PYTHON=`which python`
PS3="Please enter your choice: "
options=("clear workspace" "extract PNG from video data_src" "data_src extract faces" "data_src sort" "extract PNG from video data_dst" "data_dst extract faces" "data_dst sort by hist" "train" "convert" "converted to mp4" "quit")
select opt in "${options[@]}"
do
case $opt in
"clear workspace" )
echo -n "Clean up workspace? [Y/n] "; read workspace_ans
if [ "$workspace_ans" == "Y" ] || [ "$workspace_ans" == "y" ]; then
rm -rf $WORKSPACE
mkdir -p $WORKSPACE/data_src/aligned
mkdir -p $WORKSPACE/data_dst/aligned
mkdir -p $WORKSPACE/model
echo "Workspace has been successfully cleaned!"
fi
;;
"extract PNG from video data_src" )
echo -n "File name: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_src/%04d.png -loglevel error
;;
"data_src extract faces" )
echo -n "Detector? [mt | manual] "; read detector
$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_src --output-dir $WORKSPACE/data_src/aligned --detector $detector --debug --cpu-only
;;
"data_src sort" )
echo -n "Sort by? [blur | brightness | face-yaw | hue | hist | hist-blur | hist-dissim] "; read sort_method
$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_src/aligned --by $sort_method
;;
"extract PNG from video data_dst" )
echo -n "File name: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_dst/%04d.png -loglevel error
;;
"data_dst extract faces" )
echo -n "Detector? [mt | manual] "; read detector
$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/aligned --detector $detector --debug --cpu-only
;;
"data_dst sort by hist" )
$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_dst/aligned --by hist
;;
"train" )
echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR (4GB+) ] "; read model
echo -n "Show Preview? [Y/n] "; read preview
if [ "$preview" == "Y" ] || [ "$preview" == "y" ]; then preview="--preview"; else preview=""; fi
$PYTHON $INTERNAL_DIR/main.py train --training-data-src-dir $WORKSPACE/data_src/aligned --training-data-dst-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model --cpu-only $preview
;;
"convert" )
echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR(4GB+) ] "; read model
$PYTHON $INTERNAL_DIR/main.py convert --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/merged --aligned-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model --ask-for-params --cpu-only
;;
"converted to mp4" )
echo -n "File name of destination video: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -y -i $WORKSPACE/$filename -r $fps -i "$WORKSPACE/data_dst/merged/%04d.png" -map 0:a? -map 1:v -r $fps -c:v libx264 -b:v 8M -pix_fmt yuv420p -c:a aac -strict -2 -b:a 192k -ar 48000 "$WORKSPACE/result.mp4" -loglevel error
;;
"quit" )
break
;;
*)
echo "Invalid choice!"
;;
esac
done

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main.sh
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#!/bin/bash
INTERNAL_DIR=`pwd`
WORKSPACE=$INTERNAL_DIR/workspace
PYTHON=`which python`
PS3="Please enter your choice: "
options=("clear workspace" "extract PNG from video data_src" "data_src extract faces" "data_src sort" "extract PNG from video data_dst" "data_dst extract faces" "data_dst sort by hist" "train" "convert" "converted to mp4" "quit")
select opt in "${options[@]}"
do
case $opt in
"clear workspace" )
echo -n "Clean up workspace? [Y/n] "; read workspace_ans
if [ "$workspace_ans" == "Y" ] || [ "$workspace_ans" == "y" ]; then
rm -rf $WORKSPACE
mkdir -p $WORKSPACE/data_src/aligned
mkdir -p $WORKSPACE/data_dst/aligned
mkdir -p $WORKSPACE/model
echo "Workspace has been successfully cleaned!"
fi
;;
"extract PNG from video data_src" )
echo -n "File name: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_src/%04d.png -loglevel error
;;
"data_src extract faces" )
echo -n "Detector? [dlib | mt | manual] "; read detector
echo -n "Multi-GPU? [Y/n] "; read gpu_ans
if [ "$gpu_ans" == "Y" ] || [ "$gpu_ans" == "y" ]; then gpu_ans="--multi-gpu"; else gpu_ans=""; fi
$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_src --output-dir $WORKSPACE/data_src/aligned --detector $detector $gpu_ans --debug
;;
"data_src sort" )
echo -n "Sort by? [blur | brightness | face-yaw | hue | hist | hist-blur | hist-dissim] "; read sort_method
$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_src/aligned --by $sort_method
;;
"extract PNG from video data_dst" )
echo -n "File name: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_dst/%04d.png -loglevel error
;;
"data_dst extract faces" )
echo -n "Detector? [dlib | mt | manual] "; read detector
echo -n "Multi-GPU? [Y/n] "; read gpu_ans
if [ "$gpu_ans" == "Y" ] || [ "$gpu_ans" == "y" ]; then gpu_ans="--multi-gpu"; else gpu_ans=""; fi
$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/aligned --detector $detector $gpu_ans --debug
;;
"data_dst sort by hist" )
$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_dst/aligned --by hist
;;
"train" )
echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR (4GB+) ] "; read model
echo -n "Multi-GPU? [Y/n] "; read gpu_ans
if [ "$gpu_ans" == "Y" ] || [ "$gpu_ans" == "y" ]; then gpu_ans="--multi-gpu"; else gpu_ans=""; fi
$PYTHON $INTERNAL_DIR/main.py train --training-data-src-dir $WORKSPACE/data_src/aligned --training-data-dst-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model $gpu_ans
;;
"convert" )
echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR(4GB+) ] "; read model
$PYTHON $INTERNAL_DIR/main.py convert --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/merged --aligned-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model --ask-for-params
;;
"converted to mp4" )
echo -n "File name of destination video: "; read filename
echo -n "FPS: "; read fps
if [ -z "$fps" ]; then fps="25"; fi
ffmpeg -y -i $WORKSPACE/$filename -r $fps -i "$WORKSPACE/data_dst/merged/%04d.png" -map 0:a? -map 1:v -r $fps -c:v libx264 -b:v 8M -pix_fmt yuv420p -c:a aac -b:a 192k -ar 48000 "$WORKSPACE/result.mp4" -loglevel error
;;
"quit" )
break
;;
*)
echo "Invalid choice!"
;;
esac
done

9
requirements-cpu-docker.txt
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pathlib==1.0.1
scandir==1.6
h5py==2.7.1
Keras==2.2.4
opencv-python==3.4.0.12
scikit-image
dlib==19.10.0
tqdm
git+https://www.github.com/keras-team/keras-contrib.git