The smaller the value, the more src-like facial expressions will appear.
The larger the value, the less space there is to train a large dst faceset in the neural network.
Typical fine value is 0.33
AMP model: added ‘pretrain’ mode as in SAEHD
Windows build: Default pretrain dataset is updated with applied Generic XSeg mask
Helps to fix eye problems during training like "alien eyes" and wrong eyes direction.
Also makes the detail of the teeth higher.
New default values with new model:
Archi : ‘liae-ud’
AdaBelief : enabled
Changed help for “Learning rate dropout” option:
When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations.
Enabled it before “disable random warp” and before GAN. n disabled. y enabled
cpu enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.
Changed help for GAN option:
Train the network in Generative Adversarial manner.
Forces the neural network to learn small details of the face.
Enable it only when the face is trained enough and don't disable.
Typical value is 0.1
improved GAN. Now it produces better skin detail, less patterned aggressive artifacts, works faster.
https://i.imgur.com/Nbh3mw1.png
Maximum resolution is increased to 640.
‘hd’ archi is removed. ‘hd’ was experimental archi created to remove subpixel shake, but ‘lr_dropout’ and ‘disable random warping’ do that better.
‘uhd’ is renamed to ‘-u’
dfuhd and liaeuhd will be automatically renamed to df-u and liae-u in existing models.
Added new experimental archi (key -d) which doubles the resolution using the same computation cost.
It is mean same configs will be x2 faster, or for example you can set 448 resolution and it will train as 224.
Strongly recommended not to train from scratch and use pretrained models.
New archi naming:
'df' keeps more identity-preserved face.
'liae' can fix overly different face shapes.
'-u' increased likeness of the face.
'-d' (experimental) doubling the resolution using the same computation cost
Examples: df, liae, df-d, df-ud, liae-ud, ...
Improved GAN training (GAN_power option). It was used for dst model, but actually we don’t need it for dst.
Instead, a second src GAN model with x2 smaller patch size was added, so the overall quality for hi-res models should be higher.
Added option ‘Uniform yaw distribution of samples (y/n)’:
Helps to fix blurry side faces due to small amount of them in the faceset.
Quick96:
Now based on df-ud archi and 20% faster.
XSeg trainer:
Improved sample generator.
Now it randomly adds the background from other samples.
Result is reduced chance of random mask noise on the area outside the face.
Now you can specify ‘batch_size’ in range 2-16.
Reduced size of samples with applied XSeg mask. Thus size of packed samples with applied xseg mask is also reduced.
Fixed "Write preview history". Now it writes all subpreviews in separated folders
https://i.imgur.com/IszifCJ.jpg
also the last preview saved as _last.jpg before the first file
https://i.imgur.com/Ls1AOK4.jpg
thus you can easily check the changes with the first file in photo viewer
‘cpu’ mean enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.
SAEHD: resolution >= 256 now has second dssim loss function
