## **DeepFaceLab** is a tool that utilizes deep learning to recognize and swap faces in pictures and videos. ### **Features**: - new models - 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 - added **MTCNN extractor** which produce less jittered aligned face than DLIBCNN, but can produce more false faces. Comparison dlib (at left) vs mtcnn on hard case: ![](https://i.imgur.com/5qLiiOV.gif) MTCNN produces less jitter. - added **Manual extractor**. You can fix missed faces manually or do full manual extract: ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/manual_extractor_0.jpg) ![Result](https://user-images.githubusercontent.com/8076202/38454756-0fa7a86c-3a7e-11e8-9065-182b4a8a7a43.gif) - 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. ### **Model types**: - **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.: ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/H64_Downey_0.jpg) ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/H64_Downey_1.jpg) - **H128 (3GB+)** - as H64, but in 128 resolution. Better face details. for 3GB and 4GB VRAM model works in reduced mode. H128 Cage: ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/H128_Cage_0.jpg) H128 asian face on blurry target: ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/H128_Asian_0.jpg) ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/H128_Asian_1.jpg) - **DF (5GB+)** - @dfaker model. As H128, but fullface model. Strongly recommended not to mix various light conditions in src faces. ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/DF_Cage_0.jpg) - **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: ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/LIAEF128_Cage_0.jpg) ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/LIAEF128_Cage_1.jpg) LIAEF128 Cage video: [![Watch the video](https://img.youtube.com/vi/mRsexePEVco/0.jpg)](https://www.youtube.com/watch?v=mRsexePEVco) - **SAE (2GB+)** - Styled AutoEncoder. It is like LIAEF but with new face style loss. SAE more like face morpher/stylizer rather than direct swapper. Available options on start: resolution, ae-dims, half/full face. ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/SAE_Cage_0.jpg) ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/SAE_Cage_1.jpg) SAE model Cage-Trump video: https://www.youtube.com/watch?v=2R_aqHBClUQ ![](https://github.com/iperov/DeepFaceLab/blob/master/doc/DeepFaceLab_convertor_overview.png) ### **Sort tool**: `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 `brightness` `hue` `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: 1) `black` -> then delete faces cutted by black area at end of folder 2) `blur` -> then delete blurred faces at end of folder 3) `hist` -> then delete groups of similar unwanted faces and leave only target face 4) `final` -> then delete faces occluded by obstructions Best practice for dst faces: 1) delete first unsorted aligned groups of images what you can to delete. Dont touch target face mixed with others. 2) `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 download from here: https://mega.nz/#F!y1ERHDaL!PPwg01PQZk0FhWLVo5_MaQ ### **Build info** 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 such 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 1) torrent https://rutracker.org/forum/viewtopic.php?p=75318742 (magnet link inside). 2) https://mega.nz/#F!b9MzCK4B!zEAG9txu7uaRUjXz9PtBqg Video tutorial: https://www.youtube.com/watch?v=K98nTNjXkq8 ### **Windows 10 memory problem: Windows 10 consumes % of VRAM even if card unused for video output. ### **Problem of the year**: algorithm of overlaying neural face onto video face located in ConverterMasked.py. Can someone implement adaptive histogram matching to prevent glares when a dark eyes face merges onto a light eyes face ?