* FaceSwap 3 (#1515)
* Update extract pipeline
* Update requirements + setup for nvidia
* Remove allow-growth option
* tf.keras to keras updates
* lib.model.losses - Port + fix all loss functions for Keras3
* lib.model - port initializers, layers. normalization to Keras3
* lib.model.autoclip to Keras 3
* Update mixed precision layer storage
* model file to .keras format
* Restructure nn_blocks to initialize layers in __init__
* Tensorboard
- Trainer: Add Torch compatible Tensorboard callbacks
- GUI event reader remove TF dependency
* Loss logging
- Flush TB logs on save
- Replace TB live iterator for GUI
* Backup models on total loss drop rather than per side
* Update all models to Keras3 Compat
* Remove lib.model.session
* Update clip ViT to Keras 3
* plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend
* Port AdaBelief to Keras 3
* setup.py:
- Add --dev flag for dev tool install
* Fix Keras 3 syntax
* Fix LR Finder for Keras 3
* Fix mixed precision switching for Keras 3
* Add more optimizers + open up config setting
* train: Remove updating FS1 weights to FS2 models
* Alignments: Remove support for legacy .json files
* tools.model:
- Remove TF Saved Format saving
- Fix Backup/Restore + Nan-Scan
* Fix inference model creation for Keras 3
* Preview tool: Fix for Keras3
* setup.py: Configure keras backend
* train: Migration of FS2 models to FS3
* Training: Default coverage to 100%
* Remove DirectML backend
* Update setup for MacOS
* GUI: Force line reading to UTF-8
* Remove redundant Tensorflow references
* Remove redundant code
* Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D
* Add vertical offset option for training
* Github actions: Add more python versions
* Add python version to workflow names
* Github workflow: Exclude Python 3.12 for macOS
* Implement custom training loop
* Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511)
* setup.py: Add Cuda/ROCm version select options
* bump minimum python version to 3.11
* Switch from setup.cgf to pyproject.toml
* Documentation: Update all docs to use automodapi
* Allow sysinfo to run with missing packages + correctly install tk under Linux
* Bugfix: dot naming convention in clip models
* lib.config: Centralise globally rather than passing as object
- Add torch DataParallel for multi-gpu training
- GUI: Group switches together when generating cli args
- CLI: Remove deprecated multi-character argparse args
- Refactor:
- Centralise tensorboard reading/writing + unit tests
- Create trainer plugin interfaces + add original + distributed
* Update installers
* requirements.txt: - Pin opencv to 4.1.1 (fixes cv2-dnn error)
* lib.face_detect.DetectedFace: change LandmarksXY to landmarks_xy. Add left, right, top, bottom attributes
* lib.model.session: Session manager for loading models into different graphs (for Nvidia + CPU)
* plugins.extract._base: New parent class for all extract plugins
* plugins.extract.pipeline. Remove MultiProcessing. Dynamically limit batchsize for Nvidia cards. Remove loglevel input
* S3FD + FAN plugins. Standardise to Keras version for all backends
* Standardize all extract plugins to new threaded codebase
* Documentation. Start implementing Numpy style docstrings for Sphinx Documentation
* Remove s3fd_amd. Change convert OTF to expect DetectedFace object
* faces_detect - clean up and documentation
* Remove PoolProcess
* Migrate manual tool to new extract workflow
* Remove AMD specific extractor code from cli and plugins
* Sort tool to new extract workflow
* Remove multiprocessing from project
* Remove multiprocessing queues from QueueManager
* Remove multiprocessing support from logger
* Move face_filter to new extraction pipeline
* Alignments landmarksXY > landmarks_xy and legacy handling
* Intercept get_backend for sphinx doc build
# Add Sphinx documentation
* model_refactor (#571)
* original model to new structure
* IAE model to new structure
* OriginalHiRes to new structure
* Fix trainer for different resolutions
* Initial config implementation
* Configparse library added
* improved training data loader
* dfaker model working
* Add logging to training functions
* Non blocking input for cli training
* Add error handling to threads. Add non-mp queues to queue_handler
* Improved Model Building and NNMeta
* refactor lib/models
* training refactor. DFL H128 model Implementation
* Dfaker - use hashes
* Move timelapse. Remove perceptual loss arg
* Update INSTALL.md. Add logger formatting. Update Dfaker training
* DFL h128 partially ported
* Add mask to dfaker (#573)
* Remove old models. Add mask to dfaker
* dfl mask. Make masks selectable in config (#575)
* DFL H128 Mask. Mask type selectable in config.
* remove gan_v2_2
* Creating Input Size config for models
Creating Input Size config for models
Will be used downstream in converters.
Also name change of image_shape to input_shape to clarify ( for future models with potentially different output_shapes)
* Add mask loss options to config
* MTCNN options to config.ini. Remove GAN config. Update USAGE.md
* Add sliders for numerical values in GUI
* Add config plugins menu to gui. Validate config
* Only backup model if loss has dropped. Get training working again
* bugfixes
* Standardise loss printing
* GUI idle cpu fixes. Graph loss fix.
* mutli-gpu logging bugfix
* Merge branch 'staging' into train_refactor
* backup state file
* Crash protection: Only backup if both total losses have dropped
* Port OriginalHiRes_RC4 to train_refactor (OriginalHiRes)
* Load and save model structure with weights
* Slight code update
* Improve config loader. Add subpixel opt to all models. Config to state
* Show samples... wrong input
* Remove AE topology. Add input/output shapes to State
* Port original_villain (birb/VillainGuy) model to faceswap
* Add plugin info to GUI config pages
* Load input shape from state. IAE Config options.
* Fix transform_kwargs.
Coverage to ratio.
Bugfix mask detection
* Suppress keras userwarnings.
Automate zoom.
Coverage_ratio to model def.
* Consolidation of converters & refactor (#574)
* Consolidation of converters & refactor
Initial Upload of alpha
Items
- consolidate convert_mased & convert_adjust into one converter
-add average color adjust to convert_masked
-allow mask transition blur size to be a fixed integer of pixels and a fraction of the facial mask size
-allow erosion/dilation size to be a fixed integer of pixels and a fraction of the facial mask size
-eliminate redundant type conversions to avoid multiple round-off errors
-refactor loops for vectorization/speed
-reorganize for clarity & style changes
TODO
- bug/issues with warping the new face onto a transparent old image...use a cleanup mask for now
- issues with mask border giving black ring at zero erosion .. investigate
- remove GAN ??
- test enlargment factors of umeyama standard face .. match to coverage factor
- make enlargment factor a model parameter
- remove convert_adjusted and referencing code when finished
* Update Convert_Masked.py
default blur size of 2 to match original...
description of enlargement tests
breakout matrxi scaling into def
* Enlargment scale as a cli parameter
* Update cli.py
* dynamic interpolation algorithm
Compute x & y scale factors from the affine matrix on the fly by QR decomp.
Choose interpolation alogrithm for the affine warp based on an upsample or downsample for each image
* input size
input size from config
* fix issues with <1.0 erosion
* Update convert.py
* Update Convert_Adjust.py
more work on the way to merginf
* Clean up help note on sharpen
* cleanup seamless
* Delete Convert_Adjust.py
* Update umeyama.py
* Update training_data.py
* swapping
* segmentation stub
* changes to convert.str
* Update masked.py
* Backwards compatibility fix for models
Get converter running
* Convert:
Move masks to class.
bugfix blur_size
some linting
* mask fix
* convert fixes
- missing facehull_rect re-added
- coverage to %
- corrected coverage logic
- cleanup of gui option ordering
* Update cli.py
* default for blur
* Update masked.py
* added preliminary low_mem version of OriginalHighRes model plugin
* Code cleanup, minor fixes
* Update masked.py
* Update masked.py
* Add dfl mask to convert
* histogram fix & seamless location
* update
* revert
* bugfix: Load actual configuration in gui
* Standardize nn_blocks
* Update cli.py
* Minor code amends
* Fix Original HiRes model
* Add masks to preview output for mask trainers
refactor trainer.__base.py
* Masked trainers converter support
* convert bugfix
* Bugfix: Converter for masked (dfl/dfaker) trainers
* Additional Losses (#592)
* initial upload
* Delete blur.py
* default initializer = He instead of Glorot (#588)
* Allow kernel_initializer to be overridable
* Add ICNR Initializer option for upscale on all models.
* Hopefully fixes RSoDs with original-highres model plugin
* remove debug line
* Original-HighRes model plugin Red Screen of Death fix, take #2
* Move global options to _base. Rename Villain model
* clipnorm and res block biases
* scale the end of res block
* res block
* dfaker pre-activation res
* OHRES pre-activation
* villain pre-activation
* tabs/space in nn_blocks
* fix for histogram with mask all set to zero
* fix to prevent two networks with same name
* GUI: Wider tooltips. Improve TQDM capture
* Fix regex bug
* Convert padding=48 to ratio of image size
* Add size option to alignments tool extract
* Pass through training image size to convert from model
* Convert: Pull training coverage from model
* convert: coverage, blur and erode to percent
* simplify matrix scaling
* ordering of sliders in train
* Add matrix scaling to utils. Use interpolation in lib.aligner transform
* masked.py Import get_matrix_scaling from utils
* fix circular import
* Update masked.py
* quick fix for matrix scaling
* testing thus for now
* tqdm regex capture bugfix
* Minor ammends
* blur size cleanup
* Remove coverage option from convert (Now cascades from model)
* Implement convert for all model types
* Add mask option and coverage option to all existing models
* bugfix for model loading on convert
* debug print removal
* Bugfix for masks in dfl_h128 and iae
* Update preview display. Add preview scaling to cli
* mask notes
* Delete training_data_v2.py
errant file
* training data variables
* Fix timelapse function
* Add new config items to state file for legacy purposes
* Slight GUI tweak
* Raise exception if problem with loaded model
* Add Tensorboard support (Logs stored in model directory)
* ICNR fix
* loss bugfix
* convert bugfix
* Move ini files to config folder. Make TensorBoard optional
* Fix training data for unbalanced inputs/outputs
* Fix config "none" test
* Keep helptext in .ini files when saving config from GUI
* Remove frame_dims from alignments
* Add no-flip and warp-to-landmarks cli options
* Revert OHR to RC4_fix version
* Fix lowmem mode on OHR model
* padding to variable
* Save models in parallel threads
* Speed-up of res_block stability
* Automated Reflection Padding
* Reflect Padding as a training option
Includes auto-calculation of proper padding shapes, input_shapes, output_shapes
Flag included in config now
* rest of reflect padding
* Move TB logging to cli. Session info to state file
* Add session iterations to state file
* Add recent files to menu. GUI code tidy up
* [GUI] Fix recent file list update issue
* Add correct loss names to TensorBoard logs
* Update live graph to use TensorBoard and remove animation
* Fix analysis tab. GUI optimizations
* Analysis Graph popup to Tensorboard Logs
* [GUI] Bug fix for graphing for models with hypens in name
* [GUI] Correctly split loss to tabs during training
* [GUI] Add loss type selection to analysis graph
* Fix store command name in recent files. Switch to correct tab on open
* [GUI] Disable training graph when 'no-logs' is selected
* Fix graphing race condition
* rename original_hires model to unbalanced
torzdf