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Make sure you check out INSTALL.md before getting started.
When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. It ran, it worked, and as is so often the way with new technology emerging on the internet, it was immediately used to create inappropriate content. Despite the inappropriate uses the software was given originally, it was the first AI code that anyone could download, run and learn by experimentation without having a Ph.D. in math, computer theory, psychology, and more. Before “deepfakes” these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.
“Deepfakes” changed all that and anyone could participate in AI development. To us, developers, the release of this code opened up a fantastic learning opportunity. It allowed us to build on ideas developed by others, collaborate with a variety of skilled coders, experiment with AI whilst learning new skills and ultimately contribute towards an emerging technology which will only see more mainstream use as it progresses.
Are there some out there doing horrible things with similar software? Yes. And because of this, the developers have been following strict ethical standards. Many of us don’t even use it to create videos, we just tinker with the code to see what it does. Sadly, the media concentrates only on the unethical uses of this software. That is, unfortunately, the nature of how it was first exposed to the public, but it is not representative of why it was created, how we use it now, or what we see in its future. Like any technology, it can be used for good or it can be abused. It is our intention to develop FaceSwap in a way that its potential for abuse is minimized whilst maximizing its potential as a tool for learning, experimenting and, yes, for legitimate faceswapping.
We are not trying to denigrate celebrities or to demean anyone. We are programmers, we are engineers, we are Hollywood VFX artists, we are activists, we are hobbyists, we are human beings. To this end, we feel that it’s time to come out with a standard statement of what this software is and isn’t as far as us developers are concerned.
We are very troubled by the fact that FaceSwap can be used for unethical and disreputable things. However, we support the development of tools and techniques that can be used ethically as well as provide education and experience in AI for anyone who wants to learn it hands-on. We will take a zero tolerance approach to anyone using this software for any unethical purposes and will actively discourage any such uses.
FaceSwap is a Python program that will run on multiple Operating Systems including Windows, Linux, and MacOS.
See INSTALL.md for full installation instructions. You will need a modern GPU with CUDA support for best performance. AMD GPUs are partially supported.
The project has multiple entry points. You will have to:
Check out USAGE.md for more detailed instructions.
From your setup folder, run
python faceswap.py extract. This will take photos from
src folder and extract faces into
From your setup folder, run
python faceswap.py train. This will take photos from two folders containing pictures of both faces and train a model that will be saved inside the
From your setup folder, run
python faceswap.py convert. This will take photos from
original folder and apply new faces into
Alternatively, you can run the GUI by running
python faceswap.py gui
--helpoptions with arguments that they will accept. You’re smart, you can figure out how this works, right?!
NB: there is a conversion tool for video. This can be accessed by running
python tools.py effmpeg -h. Alternatively, you can use ffmpeg to convert video into photos, process images, and convert images back to the video.
Reusing existing models will train much faster than starting from nothing. If there is not enough training data, start with someone who looks similar, then switch the data.
Your best bet is to join the FaceSwap Discord server where there are plenty of users willing to help. Please note that, like this repo, this is a SFW Server!
Alternatively, you can post questions in the FaceSwap Forum. Please do not post general support questions in this repo as they are liable to be deleted without response.
The developers work tirelessly to improve and develop FaceSwap. Many hours have been put in to provide the software as it is today, but this is an extremely time-consuming process with no financial reward. If you enjoy using the software, please consider donating to the devs, so they can spend more time implementing improvements.
There is very little FaceSwap code that hasn’t been touched by torzdf. He is responsible for implementing the GUI, FAN aligner, MTCNN detector and porting the Villain, DFL-H128 and DFaker models to FaceSwap, as well as significantly improving many areas of the code.
Creator of the Unbalanced and OHR models, as well as expanding various capabilities within the training process. Andenixa is currently working on new models and will take requests for donations.
Responsible for consolidating the converters, adding a lot of code to fix model stability issues, and helping significantly towards making the training process more modular, kvrooman continues to be a very active contributor.
Sorry, no time for that.
It is a community repository for active users.
The joshua-wu repo seems not active. Simple bugs like missing http:// in front of urls have not been solved since days.
This is a friendly typosquat, and it is fully dedicated to the project. If /u/deepfakes wants to take over this repo/user and drive the project, he is welcomed to do so (Raise an issue, and he will be contacted on Reddit). Please do not send /u/deepfakes messages for help with the code you find here.
tl;dr: training data + trial and error