- implement configurable re-align function in extract
- update locales + documentation
- re-factor align._base and split to separate modules
- move normalization method to plugin parent
- bugfix: FAN use zeros for pre-processing crop
- lint AlignedFilter
- Allow selecting folder as well as multiple images
- Lower default threshold and update helptext
- bugfix: detector error when using all aligned faces
- Standardize output folder name
- Remove old face filter
- plugins.extract.pipeline: Expose plugins directly
- Change `is_aligned` from plugin level to ExtractMedia level
- Allow extract pipeline to take faceswap aligned images
- Add ability for recognition plugins to accept aligned faces as input
- Add face filter to recognition plugin
- Move extractor pipeline IO ops to own class
- lib.detected_face
- Subclass Masks for Landmark based masks
- Add training mask propery + methods to DetectedFace
- lib.training_training
- subclass TrainingDataGenerator for training and preview data
- Split cache into own module
- Reduce thread count to 1 to prevent image corruption + data re-use
- Process on largest model input/output size rather than stored image size
- Size and crop masks during caching stage
- Implement ring buffer for data flow
- Fix preview reload bug
- augmentation
- typing
- switch color aug order
- better initialization
- Fix warp + landmark warp to correctly apply at different image scales
- Slightly improved warp caching
- Don't store whether image is_preview. Handle all data as training images implicitly
- plugins.trainer: Typing and fixes to work with trainingdata refactor
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
* 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
* 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
* Clarification of Phases
When multi-processing, the status bar description lists the phase as only Detect. This is misleading and there likely should be a reference to the multiple simutaneous phases being run.
* Simplify Communication
* 1st Round update for Python 3.7, TF1.15, Keras2.3
Move Tensorflow logging verbosity prior to first tensorflow import
Keras Optimizers and nn_block update
lib.logger - Change tf deprecation messages from WARNING to DEBUG
Raise Tensorflow Max version check to 1.15
Update requirements and conda check for python 3.7+
Update install scripts, travis and documentation to Python 3.7
* Revert Keras to 2.2.4
* 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
- 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
* 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
