added SAEHD model ( High Definition Styled AutoEncoder )
This is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020.
Differences from SAE:
+ new encoder produces more stable face and less scale jitter
before: https://i.imgur.com/4jUcol8.gifv
after: https://i.imgur.com/lyiax49.gifv - scale of the face is less changed within frame size
+ new decoder produces subpixel clear result
+ pixel loss and dssim loss are merged together to achieve both training speed and pixel trueness
+ by default networks will be initialized with CA weights, but only after first successful iteration
therefore you can test network size and batch size before weights initialization process
+ new neural network optimizer consumes less VRAM than before
+ added option <Enable 'true face' training>
The result face will be more like src and will get extra sharpness.
example: https://i.imgur.com/ME3A7dI.gifv
Enable it for last 15-30k iterations before conversion.
+ encoder and decoder dims are merged to one parameter encoder/decoder dims
+ added mid-full face, which covers 30% more area than half face.
removed TrueFace model.
added SAEv2 model. Differences from SAE:
+ default e_ch_dims is now 21
+ new encoder produces more stable face and less scale jitter
before: https://i.imgur.com/4jUcol8.gifv
after: https://i.imgur.com/lyiax49.gifv - scale of the face is less changed within frame size
+ decoder now has only 1 residual block instead of 2, result is same quality with less decoder size
+ added mid-full face, which covers 30% more area than half face.
+ added option " Enable 'true face' training "
Enable it only after 50k iters, when the face is sharp enough.
the result face will be more like src.
The most src-like face with 'true-face-training' you can achieve with DF architecture.
