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 !
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
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.
* Restore mask functionality
Once mask is saved (using 'c'), mask tool can apply same modifications to the next alignment (by pressing 'r'). Thus some routine work is decreased.
* Mask edit added function to re-apply changes
fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.
Converter: added blur on the same keys as sharpness
Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.
Session is now saved to the model folder.
blur and erode ranges are increased to -400+400
hist-match-bw is now replaced with seamless2 mode.
Added 'ebs' color transfer mode (works only on Windows).
FANSEG model (used in FAN-x mask modes) is retrained with new model configuration
and now produces better precision and less jitter
if input frames are changed (amount or filenames)
then interactive converter automatically starts a new session.
if model is more trained then all frames will be recomputed again with their saved configs.
ConvertAvatar: fix input image after fix landmarks face align,
VideoEd: video_from_sequence now uses pipe input to input any filenames instead of %.5d. formatted
