iperov
153 lines
5.8 KiB
Markdown
153 lines
5.8 KiB
Markdown
## **DeepFaceLab** is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.
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### **Features**:
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- new models
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- new architecture, easy to experiment with models
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- face data embedded to png files
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- automatic GPU manager, chooses best gpu(s) and supports --multi-gpu (only for identical cards). Warning: dont use cards in SLI mode.
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- cpu mode. 8th gen Intel core CPU able to train H64 model in 2 days.
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- new preview window
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- extractor in parallel
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- converter in parallel
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- added **--debug** option for all stages
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- 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:
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MTCNN produces less jitter.
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- added **Manual extractor**. You can fix missed faces manually or do full manual extract:
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- standalone zero dependencies ready to work prebuilt binary for all windows versions, see below
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### Warning: **Facesets** of FaceSwap or FakeApp are **not compatible** with this repo. You should to run extract again.
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### **Model types**:
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- **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.
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H64 Robert Downey Jr.:
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- **H128 (3GB+)** - as H64, but in 128 resolution. Better face details. for 3GB and 4GB VRAM model works in reduced mode.
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H128 Cage:
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H128 asian face on blurry target:
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- **DF (5GB+)** - @dfaker model. As H128, but fullface model. Strongly recommended not to mix various light conditions in src faces.
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- **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.
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LIAEF128 Cage:
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LIAEF128 Cage video:
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[](https://www.youtube.com/watch?v=mRsexePEVco)
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- **SAE (2GB+)** - Styled AutoEncoder. It is like LIAEF but with new face style loss like in UFM.
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SAE model Cage-Trump video: https://www.youtube.com/watch?v=2R_aqHBClUQ
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### **Sort tool**:
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`blur` places most blurred faces at end of folder
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`hist` groups images by similar content
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`hist-dissim` places most similar to each other images to end.
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`hist-blur` sort by blur in groups of similar content
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`brightness`
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`hue`
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`black` Places images which contains black area at end of folder. Useful to get rid of src faces which cutted by screen.
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Best practice for gather src faceset:
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1) delete first unsorted aligned groups of images what you can to delete. Dont touch target face mixed with others.
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2) `black` -> delete faces cutted by black area at end of folder
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3) `blur` -> delete 30-50% at end of folder
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4) `hist` -> delete groups of similar and leave only target face
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5) `hist-dissim` -> leave only first **1500 faces**
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6) `face-yaw` -> just for finalize faceset
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Best practice for dst faces:
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1) delete first unsorted aligned groups of images what you can to delete. Dont touch target face mixed with others.
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2) `hist` -> delete groups of similar and leave only target face
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### **Ready to work facesets**:
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- Nicolas Cage 4 facesets (1 mix + 3 different)
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- Steve Jobs
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download from here: https://mega.nz/#F!y1ERHDaL!PPwg01PQZk0FhWLVo5_MaQ
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### **Build info**
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dlib==19.10.0 from pip compiled without CUDA. Therefore you have to compile DLIB manually, orelse use MT extractor only.
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Command line example for windows: `python setup.py install -G "Visual Studio 14 2015" --yes DLIB_USE_CUDA`
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### **CPU only mode**
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CPU mode enabled by arg --cpu-only for all stages. Follow requirements-cpu.txt to install req packages.
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Do not use DLIB extractor in CPU mode, its too slow.
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Only H64 model reasonable to train on home CPU.
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### Mac/linux/docker script support.
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This repo supports only windows build of scripts. If you want to support mac/linux/docker - create such fork, it will be referenced here.
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### Prebuilt windows app:
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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
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1) torrent https://rutracker.org/forum/viewtopic.php?p=75318742 (magnet link inside).
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2) https://mega.nz/#F!b9MzCK4B!zEAG9txu7uaRUjXz9PtBqg
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Video tutorial: https://www.youtube.com/watch?v=K98nTNjXkq8
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### **Windows 10 memory problem:
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Windows 10 consumes % of VRAM even if card unused for video output.
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### **Problem of the year**:
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algorithm of overlaying neural face onto video face located in ConverterMasked.py.
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Can someone implement adaptive histogram matching to prevent glares when a dark eyes face merges onto a light eyes face ?
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