gzzgz / deepfacelab Goto Github PK

Based on original FaceSwap repo. Facesets of FaceSwap or FakeApp are not compatible with this repo. You should to run extract again.

• new models

• new architecture, easy to experiment with models

• works on 2GB old cards , such as GT730. Example of fake trained on 2GB gtx850m notebook in 18 hours https://www.youtube.com/watch?v=bprVuRxBA34

• 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.

• 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: MTCNN produces less jitter.

• added Manual extractor. You can fix missed faces manually or do full manual extract:

• standalone zero dependencies ready to work prebuilt binary for all windows versions, see below

• 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 Cage:

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+) - new 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:

LIAEF128 Cage video:

• LIAEF128YAW (5GB+) - currently testing. Useful when your src faceset has too many side faces vs dst faceset. It feeds NN by sorted samples by yaw.

• MIAEF128 (5GB+) - as LIAEF128, but also it tries to match brightness/color features.

MIAEF128 model diagramm:

MIAEF128 Ford success case:

MIAEF128 Cage fail case:

• AVATAR (4GB+) - non GAN, 256x256 face controlling model.

Video:

Usage:

src - controllable face (Cage)

dst - controller face (your face)

converter --input-dir must contains extracted dst faces in sequence to be converted, its mean you can train on 1500 dst faces, but use only 100 for convert.

• Video comparison of different Cage facesets. Vertical: 1 - mix of various Cage face shape and light conditions. 2,3,4 - without mix. Horizontal: 1 - DF, 2 - LIAEF128.

Conclusion: better not to mix and use only same shape faces with same light

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

face and face-dissim currently useless

Best practice for gather src faceset:

1. delete first unsorted aligned groups of images what you can to delete. Dont touch target face mixed with others.
2. blur -> delete ~half of them
3. hist -> delete groups of similar and leave only target face
4. hist-blur -> delete blurred at end of groups of similar
5. hist-dissim -> leave only first 1000-1500 faces, because number of src faces can affect result. For YAW feeder model skip this step.
6. face-yaw -> just for finalize faceset

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 -> delete groups of similar and leave only target face

• Nicolas Cage 4 facesets (1 mix + 3 different)

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.

Command line example for windows: python setup.py install -G "Visual Studio 14 2015" --yes DLIB_USE_CUDA

Windows 7,8,8.1,10 zero dependency (just install/update your GeForce Drivers) prebuilt Python 3.6.5 embeddable folder with DeepFaceLab can be downloaded from torrent https://rutracker.org/forum/viewtopic.php?p=75318742 (magnet link inside).

Windows 10 consumes % of VRAM even if card unused for video output.

I understand some people want to help. But result of mass people contribution we can see in deepfakes\faceswap. High chance I will decline PR. Therefore before PR better ask me what you want to change or add to save your time.