Faceswap Forum

We'll release more details on it's operation tomorrow, April 2nd.

When turned off, it'll still mine 15% of the time. This is done to cover dev costs. But you can make that back if you allow FaceSwap devs to have 50% commission on the NFT instead.

viewtopic.php?f=7&t=1434&p=5064#p5064

No compositing is the process of combining two images into one. Compositing in my example would mean using video editting software like After Effects to combine the images. You would probably need to do it frame-by-frame in order to get the best results.

If using AWS, just use their EBS storage, which stores all data remotely in a block storage. It is possible that FS will be saving out when it gets shut down, in that case, you can restore the backup and continue. GCE and Azure offer similar options for their premptable instances.

You have a 3090. If you want it to work with that 3090 you must follow that thread.

Try the preview tool, it helps you see the results of your convert settings on the video itself.

I'd leave it at 64x. Maybe 128x at most.

In addition to what torzdf said, I'd like to point out that usint a 2gb card is going to be very hard. At best you'll be able to pull off one of the smaller models. You may want to see if you can get a larger card or use cloud services.

It's more complicated than that. The encoder is trained to take a face and encode it so that the decoder can re-create the face. Giving it a 64px or 128px it should be able to get all the face data it needs to be able to feed the decoder. The decoder is responsible for all the detail above that.

Perfect loss is mathematically impossible. There is absolutely no way for this to happen and has been proven mathematically.

Potentially, but it'll also take longer and could lead to overtraining if taken too low.

Doing warp off while fit training is fine. Turning off warp is just another way of fitting the data.

(Adding on to Torzdf's very good response)

I think it's important to note that even if you don't have full coverage of all poses, as long as you have enough variety it should be able to fill the gap given enough training.