DeepFaceLab is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.
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Goal: RTX 2080 TI
new architecture, easy to experiment with models
face data embedded to png files
automatic GPU manager, chooses best gpu(s) and supports --multi-gpu (only for identical cards). Warning: dont use cards in SLI mode.
cpu mode. 8th gen Intel core CPU able to train H64 model in 2 days.
new preview window
extractor in parallel
converter in parallel
added --debug option for all stages
standalone zero dependencies ready to work prebuilt binary for all windows versions, see below
Warning: Facesets of FaceSwap or FakeApp are not compatible with this repo. You should to run extract again.
- H64 (2GB+) - half face with 64 resolution. It is as original FakeApp or FaceSwap, but with new TensorFlow 1.8 DSSIM Loss func and separated mask decoder + better ConverterMasked. for 2GB and 3GB VRAM model works in reduced mode.
H64 Robert Downey Jr.:
- H128 (3GB+) - as H64, but in 128 resolution. Better face details. for 3GB and 4GB VRAM model works in reduced mode.
H128 asian face on blurry target:
- DF (5GB+) - @dfaker model. As H128, but fullface model. Strongly recommended not to mix various light conditions in src faces.
- LIAEF128 (5GB+) - Less agressive Improved Autoencoder Fullface 128 model. Result of combining DF, IAE, + experiments. Model tries to morph src face to dst, while keeping facial features of src face, but less agressive morphing. Model has problems with closed eyes recognizing.
LIAEF128 Cage video:
- SAE (2GB+) - Styled AutoEncoder - new superior model based on style loss. Morphing/stylizing done directly by neural network. Face obstructions also reconstructed without any masks. Converter mode 'overlay' should be used. Model has several options on start for fine tuning to fit your GPU.
SAE model Cage-Trump video: https://www.youtube.com/watch?v=2R_aqHBClUQ
SAE model Putin-Navalny video: https://www.youtube.com/watch?v=Jj7b3mqx-Mw
Tips and tricks:
unfortunately deepfaking is time/eletricpower consuming topic and has a lot of nuances.
Every model is good for specific scenes and faces.
H64 - good for straight faces as a demo and for low vram.
H128 - good for straight faces, gives highest resolution and details possible in 2019. Absolute best for asian faces, because they are flat, similar and evenly lighted with clear skin.
DF - good for side faces, but results in a lower resolution and details. Covers more area of cheeks. Keeps face unmorphed. Good for similar face shapes.
LIAE - can partially fix dissimilar face shapes, but results in a less recognizable face.
SAE - no matter how similar faces, src face will be morphed onto dst face, which can make face absolutely unrecognizable. Model can collapse on some scenes. Easy to overlay final face because dst background is also predicted.
Quality of src faceset significantly affects the final face.
Narrow src face is better fakeable than wide. This is why Cage is so popular in deepfakes.
if src faceset has number of faces more than dst faceset, model can be not converged. In this case try 'Feed faces to network sorted by yaw' option.
if src face wider than dst, model can be not converged. In this case try to decrease 'Src face scale modifier' to -5.
architecture 'df' make predicted face looking more like src, but if model not converges try default 'liae'.
most scenes converge fine with batch size = 8. In this case better to increase 'encoder/decoder dims per channel' to get more sharp result.
blur places most blurred faces at end of folder
hist groups images by similar content
hist-dissim places most similar to each other images to end.
hist-blur sort by blur in groups of similar content
black Places images which contains black area at end of folder. Useful to get rid of src faces which cutted by screen.
final sorts by yaw, blur, and hist, and leaves best 1500-1700 images.
Best practice for gather src faceset from tens of thousands images:
black-> then delete faces cutted by black area at end of folder
blur-> then delete blurred faces at end of folder
hist-> then delete groups of similar unwanted faces and leave only target face
final-> then delete faces occluded by obstructions
Best practice for dst faces:
- delete first unsorted aligned groups of images what you can to delete. Dont touch target face mixed with others.
hist-> then delete groups of similar and leave only target face
Ready to work facesets:
Nicolas Cage 4 facesets (1 mix + 3 different), Steve Jobs, Putin
download from here: https://mega.nz/#F!y1ERHDaL!PPwg01PQZk0FhWLVo5_MaQ
dlib==19.10.0 from pip compiled without CUDA. Therefore you have to compile DLIB manually, orelse use MT extractor only.
Command line example for windows:
python setup.py install -G "Visual Studio 14 2015" --yes DLIB_USE_CUDA
CPU only mode
CPU mode enabled by arg --cpu-only for all stages. Follow requirements-cpu.txt to install req packages. Do not use DLIB extractor in CPU mode, its too slow. Only H64 model reasonable to train on home CPU.
Mac/linux/docker script support.
This repo supports only windows build of scripts. If you want to support mac/linux/docker - create fork, it will be referenced here.
Prebuilt windows app:
Windows 7,8,8.1,10 zero dependency (just install/update your GeForce Drivers) prebuilt DeepFaceLab (include GPU and CPU versions) can be downloaded from
- torrent https://rutracker.org/forum/viewtopic.php?p=75318742 (magnet link inside).
(Chinese) QQ group 951138799 for ML/AI experts