improved model generalization, overall accuracy and sharpness
by using new 'Learning rate dropout' technique from paper https://arxiv.org/abs/1912.00144
An example of a loss histogram where this function is enabled after the red arrow:
https://i.imgur.com/3olskOd.jpg
This is sort method by absolute per pixel difference between all faces.
options:
Sort by similar? ( y/n ?:help skip:y ) :
if you choose 'n', then most dissimilar faces will be placed first.
now you have 3 ways:
1) define light directions manually (not for google colab)
watch demo https://youtu.be/79xz7yEO5Jw
2) relight faceset with one random direction
3) relight faceset with predefined 8 directions
Synthesize new faces from existing ones by relighting them using DeepPortraitRelighter network.
With the relighted faces neural network will better reproduce face shadows.
Therefore you can synthsize shadowed faces from fully lit faceset.
https://i.imgur.com/wxcmQoi.jpg
as a result, better fakes on dark faces:
https://i.imgur.com/5xXIbz5.jpg
in OpenCL build Relighter runs on CPU,
install pytorch directly via pip install, look at requirements
This is the fastest model for low-end cards.
Model has zero options and trains a 96pix fullface.
It is good for quick deepfake demo.
Example of the preview trained in 15 minutes on RTX2080Ti:
https://i.imgur.com/oRMvZFP.jpg
fixed crashes
removed useless 'ebs' color transfer
changed keys for color degrade
added image degrade via denoise - same as denoise extracted data_dst.bat ,
but you can control this option directly in the interactive converter
added image degrade via bicubic downscale and upscale
SAEHD: default ae_dims for df now 256.
removed option 'apply random ct'
added option
Color transfer mode apply to src faceset. ( none/rct/lct/mkl/idt, ?:help skip: none )
Change color distribution of src samples close to dst samples. Try all modes to find the best.
before was lct mode, but sometime it does not work properly for some facesets.
