No problem.
We just switched from preview back to session graph on an active session and reproduced a crash. It seemed to log a report so here it is:
Code: Select all
05/07/2022 22:22:09 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['03579.png', '06427.png', '01761.png', '06144.png', '01268.png']
05/07/2022 22:22:11 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['09530.png', '04870.png', '03438.png', '07545.png', '01785.png']
05/07/2022 22:22:14 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['02406.png', '03829.png', '09482.png', '05399.png', '01876.png']
05/07/2022 22:22:16 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['02428.png', '10602.png', '00239.png', '08793.png', '08451.png']
05/07/2022 22:22:19 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['00478.png', '08664.png', '04416.png', '09345.png', '00448.png']
05/07/2022 22:22:22 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['09560.png', '03496.png', '09380.png', '05842.png', '03877.png']
05/07/2022 22:22:24 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['05337.png', '08500.png', '04145.png', '05222.png', '03419.png']
05/07/2022 22:22:27 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['09503.png', '08846.png', '06926.png', '03326.png', '05017.png']
05/07/2022 22:22:30 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['00942.png', '03173.png', '09885.png', '10417.png', '10565.png']
05/07/2022 22:22:32 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['02884.png', '03842.png', '09246.png', '04563.png', '04737.png']
05/07/2022 22:22:35 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['07989.png', '03885.png', '10616.png', '07268.png', '00270.png']
05/07/2022 22:22:38 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['07308.png', '05281.png', '08401.png', '09281.png', '08685.png']
05/07/2022 22:22:40 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['10378.png', '05292.png', '07052.png', '00539.png', '07737.png']
05/07/2022 22:22:43 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['07573.png', '08968.png', '00856.png', '00640.png', '01667.png']
05/07/2022 22:22:46 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['08268.png', '00400.png', '08811.png', '01895.png', '00550.png']
05/07/2022 22:22:48 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['02878.png', '09182.png', '08688.png', '01811.png', '10277.png']
05/07/2022 22:22:51 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['06369.png', '04020.png', '10585.png', '02178.png', '09142.png']
05/07/2022 22:22:54 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['04492.png', '01282.png', '06344.png', '03188.png', '02644.png']
05/07/2022 22:22:57 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['05381.png', '04707.png', '10261.png', '04729.png', '09365.png']
05/07/2022 22:22:59 MainProcess _run_1 generator cache_metadata DEBUG All metadata already cached for: ['03918.png', '05473.png', '09662.png', '05705.png', '02001.png']
05/07/2022 22:23:01 MainProcess _run_0 generator cache_metadata VERBOSE Cache filled: 'C:\Convert AI\LVR2\Training Set'
05/07/2022 22:29:14 MainProcess _training_0 _base generate_preview DEBUG Generating preview
05/07/2022 22:29:14 MainProcess _training_0 _base compile_sample DEBUG Compiling samples: (side: 'a', samples: 14)
05/07/2022 22:29:14 MainProcess _training_0 _base compile_sample DEBUG Compiling samples: (side: 'b', samples: 14)
05/07/2022 22:29:14 MainProcess _training_0 _base show_sample DEBUG Showing sample
05/07/2022 22:29:14 MainProcess _training_0 _base _get_predictions DEBUG Getting Predictions
05/07/2022 22:29:16 MainProcess _training_0 _base _get_predictions DEBUG Returning predictions: {'a_a': (14, 384, 384, 3), 'b_b': (14, 384, 384, 3), 'a_b': (14, 384, 384, 3), 'b_a': (14, 384, 384, 3)}
05/07/2022 22:29:16 MainProcess _training_0 _base _to_full_frame DEBUG side: 'a', number of sample arrays: 3, prediction.shapes: [(14, 384, 384, 3), (14, 384, 384, 3)])
05/07/2022 22:29:16 MainProcess _training_0 _base _process_full DEBUG full_size: 384, prediction_size: 384, color: (0, 0, 255)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 384, 384, 3), target_size: 438, scale: 1.140625)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 438, 438, 3))
05/07/2022 22:29:16 MainProcess _training_0 _base _process_full DEBUG Overlayed background. Shape: (14, 438, 438, 3)
05/07/2022 22:29:16 MainProcess _training_0 _base _compile_masked DEBUG masked shapes: [(14, 384, 384, 3), (14, 384, 384, 3), (14, 384, 384, 3)]
05/07/2022 22:29:16 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:16 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:16 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 328, 328, 3))
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 328, 328, 3))
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'a', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'a' shape: (14, 328, 328, 3))
05/07/2022 22:29:16 MainProcess _training_0 _base _get_headers DEBUG side: 'a', width: 328
05/07/2022 22:29:16 MainProcess _training_0 _base _get_headers DEBUG height: 72, total_width: 984
05/07/2022 22:29:16 MainProcess _training_0 _base _get_headers DEBUG texts: ['Original (A)', 'Original > Original', 'Original > Swap'], text_sizes: [(183, 23), (296, 23), (259, 23)], text_x: [72, 344, 690], text_y: 47
05/07/2022 22:29:16 MainProcess _training_0 _base _get_headers DEBUG header_box.shape: (72, 984, 3)
05/07/2022 22:29:16 MainProcess _training_0 _base _to_full_frame DEBUG side: 'b', number of sample arrays: 3, prediction.shapes: [(14, 384, 384, 3), (14, 384, 384, 3)])
05/07/2022 22:29:16 MainProcess _training_0 _base _process_full DEBUG full_size: 384, prediction_size: 384, color: (0, 0, 255)
05/07/2022 22:29:16 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 384, 384, 3), target_size: 438, scale: 1.140625)
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 438, 438, 3))
05/07/2022 22:29:17 MainProcess _training_0 _base _process_full DEBUG Overlayed background. Shape: (14, 438, 438, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _compile_masked DEBUG masked shapes: [(14, 384, 384, 3), (14, 384, 384, 3), (14, 384, 384, 3)]
05/07/2022 22:29:17 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _overlay_foreground DEBUG Overlayed foreground. Shape: (14, 438, 438, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 328, 328, 3))
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 328, 328, 3))
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resizing sample: (side: 'b', sample.shape: (14, 438, 438, 3), target_size: 328, scale: 0.7488584474885844)
05/07/2022 22:29:17 MainProcess _training_0 _base _resize_sample DEBUG Resized sample: (side: 'b' shape: (14, 328, 328, 3))
05/07/2022 22:29:17 MainProcess _training_0 _base _get_headers DEBUG side: 'b', width: 328
05/07/2022 22:29:17 MainProcess _training_0 _base _get_headers DEBUG height: 72, total_width: 984
05/07/2022 22:29:17 MainProcess _training_0 _base _get_headers DEBUG texts: ['Swap (B)', 'Swap > Swap', 'Swap > Original'], text_sizes: [(150, 23), (222, 23), (259, 23)], text_x: [89, 381, 690], text_y: 47
05/07/2022 22:29:17 MainProcess _training_0 _base _get_headers DEBUG header_box.shape: (72, 984, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _duplicate_headers DEBUG side: a header.shape: (72, 984, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _duplicate_headers DEBUG side: b header.shape: (72, 984, 3)
05/07/2022 22:29:17 MainProcess _training_0 _base _stack_images DEBUG Stack images
05/07/2022 22:29:17 MainProcess _training_0 _base get_transpose_axes DEBUG Even number of images to stack
05/07/2022 22:29:17 MainProcess _training_0 _base _stack_images DEBUG Stacked images
05/07/2022 22:29:17 MainProcess _training_0 _base show_sample DEBUG Compiled sample
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Updating preview: (name: Training - 'S': Save Now. 'R': Refresh Preview. 'M': Toggle Mask. 'ENTER': Save and Quit)
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Generating preview for GUI
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Generated preview for GUI: '.gui_training_preview.jpg'
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Generating preview for display: 'Training - 'S': Save Now. 'R': Refresh Preview. 'M': Toggle Mask. 'ENTER': Save and Quit'
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Generated preview for display: 'Training - 'S': Save Now. 'R': Refresh Preview. 'M': Toggle Mask. 'ENTER': Save and Quit'
05/07/2022 22:29:18 MainProcess _training_0 train _show DEBUG Updated preview: (name: Training - 'S': Save Now. 'R': Refresh Preview. 'M': Toggle Mask. 'ENTER': Save and Quit)
05/07/2022 22:29:18 MainProcess _training_0 train _run_training_cycle DEBUG Save Iteration: (iteration: 4500
05/07/2022 22:29:18 MainProcess _training_0 _base _save DEBUG Backing up and saving models
05/07/2022 22:29:18 MainProcess _training_0 _base _get_save_averages DEBUG Getting save averages
05/07/2022 22:29:18 MainProcess _training_0 _base _get_save_averages DEBUG Average losses since last save: [0.054676631107926366, 0.05488332705199719]
05/07/2022 22:29:18 MainProcess _training_0 _base _should_backup DEBUG Updated lowest historical save iteration averages from: {'a': 0.05644378334283829, 'b': 0.05529949029535055} to: {'a': 0.054676631107926366, 'b': 0.05488332705199719}
05/07/2022 22:29:18 MainProcess _training_0 _base _should_backup DEBUG Should backup: True
05/07/2022 22:29:18 MainProcess _training_0 backup_restore backup_model VERBOSE Backing up: 'C:\Convert AI\LVR2\Model\phaze_a.h5' to 'C:\Convert AI\LVR2\Model\phaze_a.h5.bk'
05/07/2022 22:29:18 MainProcess _training_0 backup_restore backup_model VERBOSE Backing up: 'C:\Convert AI\LVR2\Model\phaze_a_state.json' to 'C:\Convert AI\LVR2\Model\phaze_a_state.json.bk'
05/07/2022 22:29:22 MainProcess _training_0 _base save DEBUG Saving State
05/07/2022 22:29:22 MainProcess _training_0 serializer save DEBUG filename: C:\Convert AI\LVR2\Model\phaze_a_state.json, data type: <class 'dict'>
05/07/2022 22:29:22 MainProcess _training_0 serializer _check_extension DEBUG Original filename: 'C:\Convert AI\LVR2\Model\phaze_a_state.json', final filename: 'C:\Convert AI\LVR2\Model\phaze_a_state.json'
05/07/2022 22:29:22 MainProcess _training_0 serializer marshal DEBUG data type: <class 'dict'>
05/07/2022 22:29:22 MainProcess _training_0 serializer marshal DEBUG returned data type: <class 'bytes'>
05/07/2022 22:29:22 MainProcess _training_0 _base save DEBUG Saved State
05/07/2022 22:29:22 MainProcess _training_0 _base _save INFO [Saved models] - Average loss since last save: face_a: 0.05468, face_b: 0.05488
05/07/2022 22:34:03 MainProcess _training_0 multithreading run DEBUG Error in thread (_training_0): [Errno 22] Invalid argument
05/07/2022 22:34:05 MainProcess MainThread train _monitor DEBUG Thread error detected
05/07/2022 22:34:05 MainProcess MainThread train _monitor DEBUG Closed Monitor
05/07/2022 22:34:05 MainProcess MainThread train _end_thread DEBUG Ending Training thread
05/07/2022 22:34:05 MainProcess MainThread train _end_thread CRITICAL Error caught! Exiting...
05/07/2022 22:34:05 MainProcess MainThread multithreading join DEBUG Joining Threads: '_training'
05/07/2022 22:34:05 MainProcess MainThread multithreading join DEBUG Joining Thread: '_training_0'
05/07/2022 22:34:05 MainProcess MainThread multithreading join ERROR Caught exception in thread: '_training_0'
Traceback (most recent call last):
File "C:\Convert\lib\cli\launcher.py", line 182, in execute_script
process.process()
File "C:\Convert\scripts\train.py", line 190, in process
self._end_thread(thread, err)
File "C:\Convert\scripts\train.py", line 230, in _end_thread
thread.join()
File "C:\Convert\lib\multithreading.py", line 121, in join
raise thread.err[1].with_traceback(thread.err[2])
File "C:\Convert\lib\multithreading.py", line 37, in run
self._target(*self._args, **self._kwargs)
File "C:\Convert\scripts\train.py", line 252, in _training
raise err
File "C:\Convert\scripts\train.py", line 242, in _training
self._run_training_cycle(model, trainer)
File "C:\Convert\scripts\train.py", line 327, in _run_training_cycle
trainer.train_one_step(viewer, timelapse)
File "C:\Convert\plugins\train\trainer\_base.py", line 225, in train_one_step
self._print_loss(loss)
File "C:\Convert\plugins\train\trainer\_base.py", line 314, in _print_loss
print(f"\r{output}", end="")
OSError: [Errno 22] Invalid argument
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: a046248 BugFix - lib.keypress. 60f95bb fix: PhazeA - Use correct name for EffNetV2 freezing
gpu_cuda: No global version found. Check Conda packages for Conda Cuda
gpu_cudnn: No global version found. Check Conda packages for Conda cuDNN
gpu_devices: GPU_0: NVIDIA GeForce RTX 3090, GPU_1: NVIDIA GeForce RTX 2080 Ti
gpu_devices_active: GPU_0
gpu_driver: 512.15
gpu_vram: GPU_0: 24576MB, GPU_1: 11264MB
os_machine: AMD64
os_platform: Windows-10-10.0.22000-SP0
os_release: 10
py_command: C:\Convert\faceswap.py train -A C:/Convert AI/LVR2/Training Set -B C:/Convert AI/L Work Folder/Brand New Set 512 -m C:/Convert AI/LVR2/Model -t phaze-a -bs 5 -it 1000000 -s 500 -ss 25000 -ps 75 -p -wl -X 1 -L INFO -gui
py_conda_version: conda 4.12.0
py_implementation: CPython
py_version: 3.8.13
py_virtual_env: True
sys_cores: 48
sys_processor: Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
sys_ram: Total: 130718MB, Available: 117856MB, Used: 12861MB, Free: 117856MB
=============== Pip Packages ===============
============== Conda Packages ==============
# packages in environment at C:\Users\ \MiniConda3\envs\faceswap:
#
# Name Version Build Channel
absl-py 1.0.0 pypi_0 pypi
astunparse 1.6.3 pypi_0 pypi
blas 1.0 mkl
ca-certificates 2021.10.8 h5b45459_0 conda-forge
cachetools 5.0.0 pypi_0 pypi
certifi 2021.10.8 py38haa244fe_2 conda-forge
charset-normalizer 2.0.12 pypi_0 pypi
colorama 0.4.4 pyhd3eb1b0_0
cudatoolkit 11.2.2 h933977f_10 conda-forge
cudnn 8.1.0.77 h3e0f4f4_0 conda-forge
cycler 0.11.0 pyhd3eb1b0_0
fastcluster 1.2.6 py38hcc40339_1 conda-forge
ffmpeg 4.3.1 ha925a31_0 conda-forge
ffmpy 0.2.3 pypi_0 pypi
flatbuffers 2.0 pypi_0 pypi
freetype 2.10.4 hd328e21_0
gast 0.5.3 pypi_0 pypi
git 2.34.1 haa95532_0
google-auth 2.6.6 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.46.0 pypi_0 pypi
h5py 3.6.0 pypi_0 pypi
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha925a31_3
idna 3.3 pypi_0 pypi
imageio 2.9.0 pyhd3eb1b0_0
imageio-ffmpeg 0.4.7 pyhd8ed1ab_0 conda-forge
importlib-metadata 4.11.3 pypi_0 pypi
intel-openmp 2021.4.0 haa95532_3556
joblib 1.1.0 pyhd3eb1b0_0
jpeg 9e h2bbff1b_0
keras 2.8.0 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.3.2 py38hd77b12b_0
libclang 14.0.1 pypi_0 pypi
libpng 1.6.37 h2a8f88b_0
libtiff 4.2.0 hd0e1b90_0
libwebp 1.2.2 h2bbff1b_0
lz4-c 1.9.3 h2bbff1b_1
markdown 3.3.7 pypi_0 pypi
matplotlib 3.2.2 0
matplotlib-base 3.2.2 py38h64f37c6_0
mkl 2021.4.0 haa95532_640
mkl-service 2.4.0 py38h2bbff1b_0
mkl_fft 1.3.1 py38h277e83a_0
mkl_random 1.2.2 py38hf11a4ad_0
numpy 1.21.5 py38h7a0a035_2
numpy-base 1.21.5 py38hca35cd5_2
nvidia-ml-py 11.510.69 pypi_0 pypi
oauthlib 3.2.0 pypi_0 pypi
opencv-python 4.5.5.64 pypi_0 pypi
openssl 1.1.1o h8ffe710_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
pillow 9.0.1 py38hdc2b20a_0
pip 21.2.2 py38haa95532_0
protobuf 3.20.1 pypi_0 pypi
psutil 5.8.0 py38h2bbff1b_1
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pyparsing 3.0.4 pyhd3eb1b0_0
pyqt 5.9.2 py38hd77b12b_6
python 3.8.13 h6244533_0
python-dateutil 2.8.2 pyhd3eb1b0_0
python_abi 3.8 2_cp38 conda-forge
pywin32 302 py38h2bbff1b_2
qt 5.9.7 vc14h73c81de_0
requests 2.27.1 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rsa 4.8 pypi_0 pypi
scikit-learn 1.0.2 py38hf11a4ad_1
scipy 1.7.3 py38h0a974cb_0
setuptools 61.2.0 py38haa95532_0
sip 4.19.13 py38hd77b12b_0
six 1.16.0 pyhd3eb1b0_1
sqlite 3.38.3 h2bbff1b_0
tensorboard 2.8.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow-gpu 2.8.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.25.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
tf-estimator-nightly 2.8.0.dev2021122109 pypi_0 pypi
threadpoolctl 2.2.0 pyh0d69192_0
tk 8.6.11 h2bbff1b_0
tornado 6.1 py38h2bbff1b_0
tqdm 4.64.0 py38haa95532_0
typing-extensions 4.2.0 pypi_0 pypi
urllib3 1.26.9 pypi_0 pypi
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
werkzeug 2.1.2 pypi_0 pypi
wheel 0.37.1 pyhd3eb1b0_0
wincertstore 0.2 py38haa95532_2
wrapt 1.14.1 pypi_0 pypi
xz 5.2.5 h8cc25b3_1
zipp 3.8.0 pypi_0 pypi
zlib 1.2.12 h8cc25b3_2
zstd 1.4.9 h19a0ad4_0
================= Configs ==================
--------- .faceswap ---------
backend: nvidia
--------- convert.ini ---------
[color.color_transfer]
clip: True
preserve_paper: True
[color.manual_balance]
colorspace: HSV
balance_1: 0.0
balance_2: 0.0
balance_3: 0.0
contrast: 0.0
brightness: 0.0
[color.match_hist]
threshold: 99.0
[mask.box_blend]
type: gaussian
distance: 11.0
radius: 5.0
passes: 1
[mask.mask_blend]
type: normalized
kernel_size: 3
passes: 4
threshold: 4
erosion: 0.0
[scaling.sharpen]
method: none
amount: 150
radius: 0.3
threshold: 5.0
[writer.ffmpeg]
container: mp4
codec: libx264
crf: 23
preset: medium
tune: none
profile: auto
level: auto
skip_mux: False
[writer.gif]
fps: 25
loop: 0
palettesize: 256
subrectangles: False
[writer.opencv]
format: png
draw_transparent: False
jpg_quality: 75
png_compress_level: 3
[writer.pillow]
format: png
draw_transparent: False
optimize: False
gif_interlace: True
jpg_quality: 75
png_compress_level: 3
tif_compression: tiff_deflate
--------- extract.ini ---------
[global]
allow_growth: False
[align.fan]
batch-size: 12
[detect.cv2_dnn]
confidence: 50
[detect.mtcnn]
minsize: 20
scalefactor: 0.709
batch-size: 8
threshold_1: 0.6
threshold_2: 0.7
threshold_3: 0.7
[detect.s3fd]
confidence: 50
batch-size: 4
[mask.bisenet_fp]
batch-size: 8
weights: faceswap
include_ears: False
include_hair: False
include_glasses: True
[mask.unet_dfl]
batch-size: 8
[mask.vgg_clear]
batch-size: 6
[mask.vgg_obstructed]
batch-size: 2
--------- gui.ini ---------
[global]
fullscreen: False
tab: extract
options_panel_width: 30
console_panel_height: 20
icon_size: 14
font: default
font_size: 9
autosave_last_session: prompt
timeout: 120
auto_load_model_stats: False
--------- train.ini ---------
[global]
centering: face
coverage: 87.5
icnr_init: False
conv_aware_init: True
optimizer: adam
learning_rate: 4e-05
epsilon_exponent: -5
reflect_padding: False
allow_growth: False
mixed_precision: True
nan_protection: True
convert_batchsize: 16
[global.loss]
loss_function: ssim
mask_loss_function: mse
l2_reg_term: 100
eye_multiplier: 3
mouth_multiplier: 2
penalized_mask_loss: True
mask_type: bisenet-fp_face
mask_blur_kernel: 3
mask_threshold: 4
learn_mask: False
[model.dfaker]
output_size: 128
[model.dfl_h128]
lowmem: False
[model.dfl_sae]
input_size: 128
clipnorm: True
architecture: df
autoencoder_dims: 0
encoder_dims: 42
decoder_dims: 21
multiscale_decoder: False
[model.dlight]
features: best
details: good
output_size: 256
[model.original]
lowmem: False
[model.phaze_a]
output_size: 384
shared_fc: None
enable_gblock: True
split_fc: True
split_gblock: False
split_decoders: False
enc_architecture: efficientnet_v2_l
enc_scaling: 80
enc_load_weights: True
bottleneck_type: dense
bottleneck_norm: None
bottleneck_size: 512
bottleneck_in_encoder: True
fc_depth: 1
fc_min_filters: 1280
fc_max_filters: 1280
fc_dimensions: 8
fc_filter_slope: -0.5
fc_dropout: 0.0
fc_upsampler: upsample2d
fc_upsamples: 1
fc_upsample_filters: 1280
fc_gblock_depth: 3
fc_gblock_min_nodes: 512
fc_gblock_max_nodes: 512
fc_gblock_filter_slope: -0.5
fc_gblock_dropout: 0.0
dec_upscale_method: resize_images
dec_norm: None
dec_min_filters: 160
dec_max_filters: 640
dec_filter_slope: -0.33
dec_res_blocks: 1
dec_output_kernel: 3
dec_gaussian: True
dec_skip_last_residual: False
freeze_layers: keras_encoder
load_layers: encoder
fs_original_depth: 4
fs_original_min_filters: 128
fs_original_max_filters: 1024
mobilenet_width: 1.0
mobilenet_depth: 1
mobilenet_dropout: 0.001
mobilenet_minimalistic: False
[model.realface]
input_size: 64
output_size: 128
dense_nodes: 1536
complexity_encoder: 128
complexity_decoder: 512
[model.unbalanced]
input_size: 128
lowmem: False
clipnorm: True
nodes: 1024
complexity_encoder: 128
complexity_decoder_a: 384
complexity_decoder_b: 512
[model.villain]
lowmem: False
[trainer.original]
preview_images: 14
zoom_amount: 5
rotation_range: 10
shift_range: 5
flip_chance: 50
color_lightness: 30
color_ab: 8
color_clahe_chance: 50
color_clahe_max_size: 4