- Gui - Stats - Return empty dict on state file look up error
- Gui - Last Session - Don't load saved project information when loading project from last session
- Train - Set default coverage to 87.5%
- Add support for different mask centering
- Update legacy alignments to store mask centering
- Bugfix: lib.image ImageIO. Ensure unique queues are created (fixes mask tool when Face is input and an output folder is provided)
- plugins.train.trainer._base - Don't output error message for sides which have valid masks
- lib.gui.menu - Fix project file saving extension on Linux
- lib.image Don't raise error if legacy non-png is found when reading header data
- plugins.train.trainer._base - Correctly pass legacy alignments through to DetectedFace
Documentation
- Update Usage.md, align.rst and image.rst
lib.image.py
- read_image - Remove hash return, add metadata return
- Remove read_image_hash functions
- Add read_image_meta functios
- Replace encode_image_with_hash with encode_image (to store metadata)
- Add png meta reading and writing functions
- Update Image Loaders/Savers to handle metadata rather than hashes
lib.training_data
- Naming updates to remove references to hashes
lib.align.Alignments
- Add versioning notes
- Increment alignments version to 2.1
- Deprecate hashing lookup functions
- Replace filter_hashes with filter_faces
lib.align.detected_face
- DetectedFace
- Remove hash property
- Add png header data serializing/deserializing functions
- Mask
- Add png header data serializing/deserializing functions
- add update_legacy_png_header function to update png meta data
lib.cli.args - Deprecate alignments files for training
- plugins.train.trainer
- Update alignments/mask code to read png header data
- scripts.convert
- Aligned images folder - read data from png headers
- scripts.extract
- Write png header information and no longer store hash of face
- tools.alignments
- remove leftover-faces, merge and update-hashes jobs
- Update jobs to use png meta data rather than hashes
- tools.manual
- Update extract code to output png meta data and don't store hashes
- Perform check on launch that tool is not pointing at a faces folder
tools.mask
- Update to use png meta data
tools.sort
- Update to use png meta data
* Minimum requirements to tf2.3.
- Handle upgrades for Windows users with tf2.2 installed by Pip
- Handle windows upgrade from pip tf2.2
- Explicitly install Cuda for Conda installs
* Update tensorflow errors api reference
* Suppress AutoGraph warning messages
* Update GUI Stats to work with tf2.3
* Fix live graph for tf2.3
* DSSIMObjective - autoGraph bugfix
* Update Travis test
- Manual Tool:
- Hide annotations for faces not meeting criteria
- Update landmarks on face add/del
- Clearer landmark annotations
- Handle non-numerics in frame number box
- Training
- Fix mis-aligned preview images
- Allows mixing legacy + new alignments for A and B
- Catch non-training images in training folder
- Catch inconsistently sized training images
- Standardize coverage ratio calculation
- lib.image - Add option to get image shape along with hash
Dfaker model:
- Add 256px mode
* Extract
- Implement aligner re-feeding
- Add extract type to pipeline.ExtractMedia
- Add pose annotation to debug
* Convert
- implement centering
- remove usage of feed and reference face properties
- Remove distributed option from convert
- Force update of alignments file on legacy receive
* Train
- Resize preview image to model output size
- Force legacy centering if centering does not exist in model's state file
- Enable training on legacy face sets
* Alignments Tool
- Update draw to include head/pose
- Remove DFL drop + linting
- Remove remove-frames job
- remove align-eyes option
- Update legacy masks to new extract type
- Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
- Re-generate thumbnails on legacy upgrade
* Mask Tool
- Update for new extract + bugfix full frame
* Manual Tool
- Update to new extraction method
- Disable legacy alignments,
- extract box bugfix
- extract faces - size to 512 and center on head
* Preview Tool
- Display based on model centering
* Sort Tool
- Use alignments for sort by face
* lib.aligner
- Add Pose Class
- Add AlignedFace Class
- center _MEAN_FACE on x
- Add meta information with versioning to alignments file
- lib.aligner.get_align_matrix to use landmarks not face
- Refactor aligned faces in lib.faces_detect
* lib.logger
- larger file log padding
* lib.config
- Fix global changeable_items
* lib.face_filter
- Use new extracted face images
* lib.image
- bump thumbnail default size to 96px
* Faster stat loading + caching
* Compress data in cache
* Optimize some calculations
* Vectorize smoothing
* stats.Calculations optimized
* Load latest training data from live iterator
* Add options to training graph
* Priority Training for Mouth and Eyes - Tensorflow
* Use chosen loss function for area multipliers
* loss multipliers for AMD
* Fix mask multipliers for plaid and roll PenalizedMaskLoss into LossWrapper
* losses_tf: roll PenalizedMaskLoss into LossWrapper
* Core Updates
- Remove lib.utils.keras_backend_quiet and replace with get_backend() where relevant
- Document lib.gpu_stats and lib.sys_info
- Remove call to GPUStats.is_plaidml from convert and replace with get_backend()
- lib.gui.menu - typofix
* Update Dependencies
Bump Tensorflow Version Check
* Port extraction to tf2
* Add custom import finder for loading Keras or tf.keras depending on backend
* Add `tensorflow` to KerasFinder search path
* Basic TF2 training running
* model.initializers - docstring fix
* Fix and pass tests for tf2
* Replace Keras backend tests with faceswap backend tests
* Initial optimizers update
* Monkey patch tf.keras optimizer
* Remove custom Adam Optimizers and Memory Saving Gradients
* Remove multi-gpu option. Add Distribution to cli
* plugins.train.model._base: Add Mirror, Central and Default distribution strategies
* Update tensorboard kwargs for tf2
* Penalized Loss - Fix for TF2 and AMD
* Fix syntax for tf2.1
* requirements typo fix
* Explicit None for clipnorm if using a distribution strategy
* Fix penalized loss for distribution strategies
* Update Dlight
* typo fix
* Pin to TF2.2
* setup.py - Install tensorflow from pip if not available in Conda
* Add reduction options and set default for mirrored distribution strategy
* Explicitly use default strategy rather than nullcontext
* lib.model.backup_restore documentation
* Remove mirrored strategy reduction method and default based on OS
* Initial restructure - training
* Remove PingPong
Start model.base refactor
* Model saving and resuming enabled
* More tidying up of model.base
* Enable backup and snapshotting
* Re-enable state file
Remove loss names from state file
Fix print loss function
Set snapshot iterations correctly
* Revert original model to Keras Model structure rather than custom layer
Output full model and sub model summary
Change NNBlocks to callables rather than custom keras layers
* Apply custom Conv2D layer
* Finalize NNBlock restructure
Update Dfaker blocks
* Fix reloading model under a different distribution strategy
* Pass command line arguments through to trainer
* Remove training_opts from model and reference params directly
* Tidy up model __init__
* Re-enable tensorboard logging
Suppress "Model Not Compiled" warning
* Fix timelapse
* lib.model.nnblocks - Bugfix residual block
Port dfaker
bugfix original
* dfl-h128 ported
* DFL SAE ported
* IAE Ported
* dlight ported
* port lightweight
* realface ported
* unbalanced ported
* villain ported
* lib.cli.args - Update Batchsize + move allow_growth to config
* Remove output shape definition
Get image sizes per side rather than globally
* Strip mask input from encoder
* Fix learn mask and output learned mask to preview
* Trigger Allow Growth prior to setting strategy
* Fix GUI Graphing
* GUI - Display batchsize correctly + fix training graphs
* Fix penalized loss
* Enable mixed precision training
* Update analysis displayed batch to match input
* Penalized Loss - Multi-GPU Fix
* Fix all losses for TF2
* Fix Reflect Padding
* Allow different input size for each side of the model
* Fix conv-aware initialization on reload
* Switch allow_growth order
* Move mixed_precision to cli
* Remove distrubution strategies
* Compile penalized loss sub-function into LossContainer
* Bump default save interval to 250
Generate preview on first iteration but don't save
Fix iterations to start at 1 instead of 0
Remove training deprecation warnings
Bump some scripts.train loglevels
* Add ability to refresh preview on demand on pop-up window
* Enable refresh of training preview from GUI
* Fix Convert
Debug logging in Initializers
* Fix Preview Tool
* Update Legacy TF1 weights to TF2
Catch stats error on loading stats with missing logs
* lib.gui.popup_configure - Make more responsive + document
* Multiple Outputs supported in trainer
Original Model - Mask output bugfix
* Make universal inference model for convert
Remove scaling from penalized mask loss (now handled at input to y_true)
* Fix inference model to work properly with all models
* Fix multi-scale output for convert
* Fix clipnorm issue with distribution strategies
Edit error message on OOM
* Update plaidml losses
* Add missing file
* Disable gmsd loss for plaidnl
* PlaidML - Basic training working
* clipnorm rewriting for mixed-precision
* Inference model creation bugfixes
* Remove debug code
* Bugfix: Default clipnorm to 1.0
* Remove all mask inputs from training code
* Remove mask inputs from convert
* GUI - Analysis Tab - Docstrings
* Fix rate in totals row
* lib.gui - Only update display pages if they have focus
* Save the model on first iteration
* plaidml - Fix SSIM loss with penalized loss
* tools.alignments - Remove manual and fix jobs
* GUI - Remove case formatting on help text
* gui MultiSelect custom widget - Set default values on init
* vgg_face2 - Move to plugins.extract.recognition and use plugins._base base class
cli - Add global GPU Exclude Option
tools.sort - Use global GPU Exlude option for backend
lib.model.session - Exclude all GPUs when running in CPU mode
lib.cli.launcher - Set backend to CPU mode when all GPUs excluded
* Cascade excluded devices to GPU Stats
* Explicit GPU selection for Train and Convert
* Reduce Tensorflow Min GPU Multiprocessor Count to 4
* remove compat.v1 code from extract
* Force TF to skip mixed precision compatibility check if GPUs have been filtered
* Add notes to config for non-working AMD losses
* Rasie error if forcing extract to CPU mode
* Fix loading of legace dfl-sae weights + dfl-sae typo fix
* Remove unused requirements
Update sphinx requirements
Fix broken rst file locations
* docs: lib.gui.display
* clipnorm amd condition check
* documentation - gui.display_analysis
* Documentation - gui.popup_configure
* Documentation - lib.logger
* Documentation - lib.model.initializers
* Documentation - lib.model.layers
* Documentation - lib.model.losses
* Documentation - lib.model.nn_blocks
* Documetation - lib.model.normalization
* Documentation - lib.model.session
* Documentation - lib.plaidml_stats
* Documentation: lib.training_data
* Documentation: lib.utils
* Documentation: plugins.train.model._base
* GUI Stats: prevent stats from using GPU
* Documentation - Original Model
* Documentation: plugins.model.trainer._base
* linting
* unit tests: initializers + losses
* unit tests: nn_blocks
* bugfix - Exclude gpu devices in train, not include
* Enable Exclude-Gpus in Extract
* Enable exclude gpus in tools
* Disallow multiple plugin types in a single model folder
* Automatically add exclude_gpus argument in for cpu backends
* Cpu backend fixes
* Relax optimizer test threshold
* Default Train settings - Set mask to Extended
* Update Extractor cli help text
Update to Python 3.8
* Fix FAN to run on CPU
* lib.plaidml_tools - typofix
* Linux installer - check for curl
* linux installer - typo fix
* Smart Masks - Training
- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code
* Update mask helptext in cli.py
* Fix Warp to Landmarks
Remove SHA1 hashing from training data
* Update mask training config
* Capture missing masks at training init
* lib.image.read_image_batch - Return filenames with batch for ordering
* scripts.train - Documentation
* plugins.train.trainer - documentation
* Ensure backward compatibility.
Fix convert for new predicted masks
* Update removed masks to components for legacy models.
Limit queue sizes to reduce RAM usage
Rename lib.image.BackgroundIO to ImageIO
Create separate ImagesLoader and ImagesSaver classes
Load/Save images from centralized lib.image.ImageIO
scripts.extract documentation
- Remove storage of original frame_dims from alignments file
- Require frame dims to be passed in to faces_detect.Mask when requesting full frame mask
- Create copy of read only mask when adding blurring/threshold
- Remove none mask plugin
- Make pipeline more flexible
- Add support for pre-aligned faces to masker plugin
- Migrate blur and threshold settings to mask output
- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format
- Remove legacy update hashes
- Remove legacy job from alignment-tools
- Remove legacy landmark rotation
- Add rotate face method to plugins/extract/detect
- Update travis test for new alignments extension
- Alignments format to .fsa
- Remove serializer option from alignments-tool
- Auto update legacy format alignment files to new format
- Save mask to alignments file as dict
- Remove blur_kernel param from plugins
- Correctly read out the mask buffer on decompress
- Fix full frame mask output
- Remove BORDER_TRANSPARENT in warp_affine (it is bugged. Don't use it)
- Store the affine matrix for the saved mask size
- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version
- PEP8 Fixes
- Remove config for non NN Masks
- Tidy up defaults helptext
- cli.py fix typos
- Remove unused imports and functions _base.py
- Standardize input_size param
- Enable and update documentation
- Change references from `aligner` to `masker`
- Change input_size, output_size and coverage_ratio from kwargs to params
- Move load_aligned to batch input iterator
- Remove unnecessary self.input param
- Add softmax layer append function to KSession
- Remove references to KSession protected objects
- Standardize plugin output into finalize method
- Make masks full frame and add to lib.faces_detect
- Add masks to alignments.json (temporary zipped base64 solution)
* Standardize serialization
- Linting
- Standardize serializer use throughout code
- Extend serializer to load and save files
- Always load and save in utf-8
- Create documentation
* Move image utils to lib.image
* Add .pylintrc file
* Remove some cv2 pylint ignores
* TrainingData: Load images from disk in batches
* TrainingData: get_landmarks to batch
* TrainingData: transform and flip to batches
* TrainingData: Optimize color augmentation
* TrainingData: Optimize target and random_warp
* TrainingData - Convert _get_closest_match for batching
* TrainingData: Warp To Landmarks optimized
* Save models to threadpoolexecutor
* Move stack_images, Rename ImageManipulation. ImageAugmentation Docstrings
* Masks: Set dtype and threshold for lib.masks based on input face
* Docstrings and Documentation
* 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
- utils.py: Set FS backend if .faceswap config file doesn't exist
- setup.py: set .faceswap config on setup
- Dynamically load options for AMD/CPU/NVIDIA backends
Fix Keras Tensorboard callback for TF1.14
Default install on CPU to TF 1.14.0
Default Conda install to TF 1.14.0
Move pynvx for macOS to conditional requirements.txt
- train/_config.py: PEP8 Fixes. Slight description change on coverage
- models/_base.py:
- Remove unused variables from Loss()
- Delete legacy config items from state file
- Save state file on Legacy update
- PEP8
- Remove _defaults.py for models with no config options
* documentation, pep8, style, clarity updates
* Update cli.py
* Update _config.py
remove extra mask and coverage
mask type as dropdown
* Update training_data.py
move coverage / LR to global
cut down on loss description
style change
losses working in PR
* simpler logging
* legacy update
* Update manual_balance.py
* Update manual_balance_defaults.py
- Renamed the parameters to `contrast` and `brightness`
- Values range is altered to run from -100.0 to +100.0
