iperov-DeepFaceLab/DockerCPU.md

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

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

In our Case,Cage's Face is SRC Face,and Trump's Face is DST Face.and finally we get the Result below.

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