Removed the wait at first launch for most graphics cards.
Increased speed of training by 10-20%, but you have to retrain all models from scratch.
SAEHD:
added option 'use float16'
Experimental option. Reduces the model size by half.
Increases the speed of training.
Decreases the accuracy of the model.
The model may collapse or not train.
Model may not learn the mask in large resolutions.
true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
Getting rid of the weakest link - AMD cards support.
All neural network codebase transferred to pure low-level TensorFlow backend, therefore
removed AMD/Intel cards support, now DFL works only on NVIDIA cards or CPU.
old DFL marked as 1.0 still available for download, but it will no longer be supported.
global code refactoring, fixes and optimizations
Extractor:
now you can choose on which GPUs (or CPU) to process
improved stability for < 4GB GPUs
increased speed of multi gpu initializing
now works in one pass (except manual mode)
so you won't lose the processed data if something goes wrong before the old 3rd pass
Faceset enhancer:
now you can choose on which GPUs (or CPU) to process
Trainer:
now you can choose on which GPUs (or CPU) to train the model.
Multi-gpu training is now supported.
Select identical cards, otherwise fast GPU will wait slow GPU every iteration.
now remembers the previous option input as default with the current workspace/model/ folder.
the number of sample generators now matches the available number of processors
saved models now have names instead of GPU indexes.
Therefore you can switch GPUs for every saved model.
Trainer offers to choose latest saved model by default.
You can rename or delete any model using the dialog.
models now save the optimizer weights in the model folder to continue training properly
removed all models except SAEHD, Quick96
trained model files from DFL 1.0 cannot be reused
AVATAR model is also removed.
How to create AVATAR like in this video? https://www.youtube.com/watch?v=4GdWD0yxvqw
1) capture yourself with your own speech repeating same head direction as celeb in target video
2) train regular deepfake model with celeb faces from target video as src, and your face as dst
3) merge celeb face onto your face with raw-rgb mode
4) compose masked mouth with target video in AfterEffects
SAEHD:
now has 3 options: Encoder dimensions, Decoder dimensions, Decoder mask dimensions
now has 4 arhis: dfhd (default), liaehd, df, liae
df and liae are from SAE model, but use features from SAEHD model (such as combined loss and disable random warp)
dfhd/liaehd - changed encoder/decoder architectures
decoder model is combined with mask decoder model
mask training is combined with face training,
result is reduced time per iteration and decreased vram usage by optimizer
"Initialize CA weights" now works faster and integrated to "Initialize models" progress bar
removed optimizer_mode option
added option 'Place models and optimizer on GPU?'
When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process.
You can place they on CPU to free up extra VRAM, thus you can set larger model parameters.
This option is unavailable in MultiGPU mode.
pretraining now does not use rgb channel shuffling
pretraining now can be continued
when pre-training is disabled:
1) iters and loss history are reset to 1
2) in df/dfhd archis, only the inter part of the encoder is reset (before encoder+inter)
thus the fake will train faster with a pretrained df model
Merger ( renamed from Converter ):
now you can choose on which GPUs (or CPU) to process
new hot key combinations to navigate and override frame's configs
super resolution upscaler "RankSRGAN" is replaced by "FaceEnhancer"
FAN-x mask mode now works on GPU while merging (before on CPU),
therefore all models (Main face model + FAN-x + FaceEnhancer)
now work on GPU while merging, and work properly even on 2GB GPU.
Quick96:
now automatically uses pretrained model
Sorter:
removed all sort by *.bat files except one sort.bat
now you have to choose sort method in the dialog
Other:
all console dialogs are now more convenient
new default example video files data_src/data_dst for newbies ( Robert Downey Jr. on Elon Musk )
XnViewMP is updated to 0.94.1 version
ffmpeg is updated to 4.2.1 version
ffmpeg: video codec is changed to x265
_internal/vscode.bat starts VSCode IDE where you can view and edit DeepFaceLab source code.
removed russian/english manual. Read community manuals and tutorials here
https://mrdeepfakes.com/forums/forum-guides-and-tutorials
new github page design
added FacesetEnhancer
4.2.other) data_src util faceset enhance best GPU.bat
4.2.other) data_src util faceset enhance multi GPU.bat
FacesetEnhancer greatly increases details in your source face set,
same as Gigapixel enhancer, but in fully automatic mode.
In OpenCL build it works on CPU only.
Please consider a donation.
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
Colombo
