If you want, you can manually remove unnecessary angles from src faceset after sort by yaw.
Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster.
added
4.2.other) data_src/dst util faceset pack.bat
Packs /aligned/ samples into one /aligned/samples.pak file.
After that, all faces will be deleted.
4.2.other) data_src/dst util faceset unpack.bat
unpacks faces from /aligned/samples.pak to /aligned/ dir.
After that, samples.pak will be deleted.
Packed faceset load and work faster.
4.2.other) data_src util faceset metadata save.bat
saves metadata of data_src\aligned\ faces into data_src\aligned\meta.dat
4.2.other) data_src util faceset metadata restore.bat
restore metadata from 'meta.dat' to images
if image size different from original, then it will be automatically resized
You can greatly enhance face details of src faceset by using Topaz Gigapixel software.
example https://i.imgur.com/Gwee99L.jpg
Example of workflow:
1) run 'data_src util faceset metadata save.bat'
2) launch Topaz Gigapixel
3) open 'data_src\aligned\' and select all images
4) set output folder to 'data_src\aligned_topaz' (create folder in save dialog)
5) set settings as on screenshot https://i.imgur.com/kAVWMQG.jpg
you can choose 2x, 4x, or 6x upscale rate
6) start process images and wait full process
7) rename folders:
data_src\aligned -> data_src\aligned_original
data_src\aligned_topaz -> data_src\aligned
8) copy 'data_src\aligned_original\meta.dat' to 'data_src\aligned\'
9) run 'data_src util faceset metadata restore.bat'
images will be downscaled back to original size (256x256) preserving details
metadata will be restored
10) now your new enhanced faceset is ready to use !
More stable and precise version of the face transformation matrix.
Now full_faces are aligned with the upper and lateral boundaries of the frame,
result: fix of cutted mouth, increase area of the cheeks of side faces
before/after https://i.imgur.com/t9IyGZv.jpg
therefore, additional training is required for existing models.
Optionally, you can re-extract dst faces of your project, if they have problems with cutted mouth or cheeks.
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.
