NVIDIA GTX 780Ti slow extracting suspected GPU not in use
Posted: Fri Jan 03, 2020 4:21 pm
Hi guys,
As my topic created few hours ago (viewtopic.php?f=5&t=307) got help from [mention]torzdf[/mention] thanks for that quick reply and his absolutely right answer.
But to follow [mention]bryanlyon[/mention] advice, I re-install the whole Faceswap and chose NVIDIA GPU at installing step (which I strongly sure that the very first Faceswap installed time I did check that box). Unfortunately, everything is not improving, still slow speed and I even can't find CUDA option in task manager GPU performance drop-down menu
So, here is my "Faceswap -> Help -> Output system information" as bryanlyon teach me taking for.
Code: Select all
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: ba41a9c bugfix: dlight model change "mask_type" to "learn_mask" in decoders
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: GeForce GTX 780 Ti
gpu_devices_active: GPU_0
gpu_driver: 441.12
gpu_vram: GPU_0: 3072MB
os_machine: AMD64
os_platform: Windows-10-10.0.17763-SP0
os_release: 10
py_command: C:\Users\vietn\faceswap/faceswap.py gui
py_conda_version: conda 4.8.0
py_implementation: CPython
py_version: 3.7.5
py_virtual_env: True
sys_cores: 8
sys_processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
sys_ram: Total: 8183MB, Available: 2562MB, Used: 5620MB, Free: 2562MB
=============== Pip Packages ===============
absl-py==0.8.1
astor==0.8.0
certifi==2019.11.28
cloudpickle==1.2.2
cycler==0.10.0
cytoolz==0.10.1
dask==2.9.0
decorator==4.4.1
fastcluster==1.1.25
ffmpy==0.2.2
gast==0.2.2
google-pasta==0.1.8
grpcio==1.16.1
h5py==2.9.0
imageio==2.6.1
imageio-ffmpeg==0.3.0
joblib==0.14.1
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
Markdown==3.1.1
matplotlib==3.1.1
mkl-fft==1.0.15
mkl-random==1.1.0
mkl-service==2.3.0
networkx==2.4
numpy==1.17.4
nvidia-ml-py3==7.352.1
olefile==0.46
opencv-python==4.1.2.30
opt-einsum==3.1.0
pathlib==1.0.1
Pillow==6.2.1
protobuf==3.11.2
psutil==5.6.7
pyparsing==2.4.5
pyreadline==2.1
python-dateutil==2.8.1
pytz==2019.3
PyWavelets==1.1.1
pywin32==227
PyYAML==5.2
scikit-image==0.15.0
scikit-learn==0.22
scipy==1.3.2
six==1.13.0
tensorboard==2.0.0
tensorflow==1.15.0
tensorflow-estimator==1.15.1
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.3
tqdm==4.40.2
Werkzeug==0.16.0
wincertstore==0.2
wrapt==1.11.2
============== Conda Packages ==============
# packages in environment at C:\Users\vietn\MiniConda3\envs\faceswap:
#
# Name Version Build Channel
_tflow_select 2.3.0 mkl
absl-py 0.8.1 py37_0
astor 0.8.0 py37_0
blas 1.0 mkl
ca-certificates 2019.11.27 0
certifi 2019.11.28 py37_0
cloudpickle 1.2.2 py_0
cycler 0.10.0 py37_0
cytoolz 0.10.1 py37he774522_0
dask-core 2.9.0 py_0
decorator 4.4.1 py_0
fastcluster 1.1.25 py37he350917_1000 conda-forge
ffmpeg 4.2 h6538335_0 conda-forge
ffmpy 0.2.2 pypi_0 pypi
freetype 2.9.1 ha9979f8_1
gast 0.2.2 py37_0
git 2.23.0 h6bb4b03_0
google-pasta 0.1.8 py_0
grpcio 1.16.1 py37h351948d_1
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
imageio 2.6.1 py37_0
imageio-ffmpeg 0.3.0 py_0 conda-forge
intel-openmp 2019.4 245
joblib 0.14.1 py_0
jpeg 9b hb83a4c4_2
keras 2.2.4 0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py37_0
keras-preprocessing 1.1.0 py_1
kiwisolver 1.1.0 py37ha925a31_0
libmklml 2019.0.5 0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.11.2 h7bd577a_0
libtiff 4.1.0 h56a325e_0
markdown 3.1.1 py37_0
matplotlib 3.1.1 py37hc8f65d3_0
mkl 2019.4 245
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.15 py37h14836fe_0
mkl_random 1.1.0 py37h675688f_0
networkx 2.4 py_0
numpy 1.17.4 py37h4320e6b_0
numpy-base 1.17.4 py37hc3f5095_0
nvidia-ml-py3 7.352.1 pypi_0 pypi
olefile 0.46 py37_0
opencv-python 4.1.2.30 pypi_0 pypi
openssl 1.1.1d he774522_3
opt_einsum 3.1.0 py_0
pathlib 1.0.1 py37_1
pillow 6.2.1 py37hdc69c19_0
pip 19.3.1 py37_0
protobuf 3.11.2 py37h33f27b4_0
psutil 5.6.7 py37he774522_0
pyparsing 2.4.5 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
python 3.7.5 h8c8aaf0_0
python-dateutil 2.8.1 py_0
pytz 2019.3 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_0
pyyaml 5.2 py37he774522_0
qt 5.9.7 vc14h73c81de_0
scikit-image 0.15.0 py37ha925a31_0
scikit-learn 0.22 py37h6288b17_0
scipy 1.3.2 py37h29ff71c_0
setuptools 42.0.2 py37_0
sip 4.19.8 py37h6538335_0
six 1.13.0 py37_0
sqlite 3.30.1 he774522_0
tensorboard 2.0.0 pyhb38c66f_1
tensorflow 1.15.0 mkl_py37h3789bd0_0
tensorflow-base 1.15.0 mkl_py37h190a33d_0
tensorflow-estimator 1.15.1 pyh2649769_0
termcolor 1.1.0 py37_1
tk 8.6.8 hfa6e2cd_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 6.0.3 py37he774522_0
tqdm 4.40.2 py_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
werkzeug 0.16.0 py_0
wheel 0.33.6 py37_0
wincertstore 0.2 py37_0
wrapt 1.11.2 py37he774522_0
xz 5.2.4 h2fa13f4_4
yaml 0.1.7 hc54c509_2
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_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
radius: 3.0
passes: 4
erosion: 0.0
[scaling.sharpen]
method: unsharp_mask
amount: 150
radius: 0.3
threshold: 5.0
[writer.ffmpeg]
container: mp4
codec: libx264
crf: 23
preset: medium
tune: none
profile: auto
level: auto
[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
threshold_1: 0.6
threshold_2: 0.7
threshold_3: 0.7
scalefactor: 0.709
batch-size: 8
[detect.s3fd]
confidence: 70
batch-size: 4
[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: True
--------- train.ini ---------
[global]
coverage: 68.75
mask_type: none
mask_blur_kernel: 3
mask_threshold: 4
learn_mask: False
icnr_init: False
conv_aware_init: False
subpixel_upscaling: False
reflect_padding: False
penalized_mask_loss: True
loss_function: mae
learning_rate: 5e-05
[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.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
Any idea?
I wondering what magic you have.