I have trained the model for 3000+ iterations. The loss of both are about 0.05. And the images are quite blurred. I want to know whats wrong. THX
The recovered images are quite blurred
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This forum is for discussing tips and understanding the process involved with Training a Faceswap model.
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Re: The recovered images are quite blurred
This is all covered in the training guide and faq, so you're best off starting there:
viewtopic.php?f=6&t=146
The number of iterations will depend on the model. For 2080Ti use Villain or Dlight
How long did it take you to get to 3k iterations?
My word is final
Re: The recovered images are quite blurred
Right now I'm running on 1650 one, and it took 12 hours more or less, using Dfaker model and extraction resolution is 128x128. Data size is about 5k for each. Now I'm going to try on 2080ti one, so I ask some suggestion to guarantee better performance.
Re: The recovered images are quite blurred
12 hours to reach 3000 iterations is waaaaay too slow and sounds like you are using CPU.
Please can you go Help > Output System Information and paste the output here,
Also, I suggest just extracting at default (256px)
My word is final
Re: The recovered images are quite blurred
It's the time that I run it on 1650, and 4GB ram is all full, I checked.
Code: Select all
encoding: cp936
git_branch: master
git_commits: d5f42b6 Bugfix: lib.gui.project - Reset invalid
Thank you for your reply, I was in the correct folder, although it refused to work. I did the old restart PC "fix" and it worked. Very odd.
choices to default if an invalid choice is discovered when loading a .fsw file. cbba53e Core Updates (#1015). 2b66013 Bugfix: logger - Still output crash report if system information fails to load. 3f0a016 bugfix: Extract - raise error if Keras fails to import. ff8d851 Cli Restructure + Multi-Mask Select on Extract (#1012)
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 1650
gpu_devices_active: GPU_0
gpu_driver: 441.08
gpu_vram: GPU_0: 4096MB
os_machine: AMD64
os_platform: Windows-10-10.0.17763-SP0
os_release: 10
py_command: faceswap.py gui
py_conda_version: conda 4.8.2
py_implementation: CPython
py_version: 3.7.7
py_virtual_env: True
sys_cores: 6
sys_processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
sys_ram: Total: 16314MB, Available: 6572MB, Used: 9741MB, Free: 6572MB
=============== Pip Packages ===============
absl-py==0.9.0
asn1crypto==1.3.0
astor==0.8.0
blinker==1.4
cachetools==3.1.1
certifi==2020.4.5.1
cffi==1.14.0
chardet==3.0.4
click==7.1.1
cloudpickle==1.3.0
cryptography==2.8
cycler==0.10.0
cytoolz==0.10.1
dask==2.14.0
decorator==4.4.2
fastcluster==1.1.26
ffmpy==0.2.2
gast==0.2.2
google-auth==1.13.1
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
grpcio==1.27.2
h5py==2.9.0
idna==2.9
imageio==2.6.1
imageio-ffmpeg==0.4.1
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==3.1.3
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
oauthlib==3.1.0
olefile==0.46
opencv-python==4.1.2.30
opt-einsum==3.1.0
pathlib==1.0.1
Pillow==6.2.1
protobuf==3.11.4
psutil==5.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.7
pycparser==2.20
PyJWT==1.7.1
pyOpenSSL==19.1.0
pyparsing==2.4.6
pyreadline==2.1
PySocks==1.7.1
python-dateutil==2.8.1
pytz==2019.3
PyWavelets==1.1.1
pywin32==227
PyYAML==5.3.1
requests==2.23.0
requests-oauthlib==1.3.0
rsa==4.0
scikit-image==0.16.2
scikit-learn==0.22.1
scipy==1.4.1
six==1.14.0
tensorboard==2.1.0
tensorflow==1.15.0
tensorflow-estimator==1.15.1
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.4
tqdm==4.45.0
urllib3==1.25.8
Werkzeug==0.16.1
win-inet-pton==1.1.0
wincertstore==0.2
wrapt==1.12.1
============== Conda Packages ==============
# packages in environment at C:\Users\EDZ\MiniConda3\envs\faceswap:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu
absl-py 0.9.0 py37_0
asn1crypto 1.3.0 py37_0
astor 0.8.0 py37_0
blas 1.0 mkl
blinker 1.4 py37_0
ca-certificates 2020.1.1 0
cachetools 3.1.1 py_0
certifi 2020.4.5.1 py37_0
cffi 1.14.0 py37h7a1dbc1_0
chardet 3.0.4 py37_1003
click 7.1.1 py_0
cloudpickle 1.3.0 py_0
cryptography 2.8 py37h7a1dbc1_0
cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
cycler 0.10.0 py37_0
cytoolz 0.10.1 py37he774522_0
dask-core 2.14.0 py_0
decorator 4.4.2 py_0
fastcluster 1.1.26 py37he350917_0 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-auth 1.13.1 py_0
google-auth-oauthlib 0.4.1 py_2
google-pasta 0.2.0 py_0
grpcio 1.27.2 py37h351948d_0
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
idna 2.9 py_1
imageio 2.6.1 py37_0
imageio-ffmpeg 0.4.1 py_0 conda-forge
intel-openmp 2020.0 166
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
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.11.4 h7bd577a_0
libtiff 4.1.0 h56a325e_0
markdown 3.1.1 py37_0
matplotlib 3.1.1 py37hc8f65d3_0
matplotlib-base 3.1.3 py37h64f37c6_0
mkl 2020.0 166
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
oauthlib 3.1.0 py_0
olefile 0.46 py37_0
opencv-python 4.1.2.30 pypi_0 pypi
openssl 1.1.1g he774522_0
opt_einsum 3.1.0 py_0
pathlib 1.0.1 py37_1
pillow 6.2.1 py37hdc69c19_0
pip 20.0.2 py37_1
protobuf 3.11.4 py37h33f27b4_0
psutil 5.7.0 py37he774522_0
pyasn1 0.4.8 py_0
pyasn1-modules 0.2.7 py_0
pycparser 2.20 py_0
pyjwt 1.7.1 py37_0
pyopenssl 19.1.0 py37_0
pyparsing 2.4.6 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pysocks 1.7.1 py37_0
python 3.7.7 h60c2a47_2
python-dateutil 2.8.1 py_0
python_abi 3.7 1_cp37m conda-forge
pytz 2019.3 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_1
pyyaml 5.3.1 py37he774522_0
qt 5.9.7 vc14h73c81de_0
requests 2.23.0 py37_0
requests-oauthlib 1.3.0 py_0
rsa 4.0 py_0
scikit-image 0.16.2 py37h47e9c7a_0
scikit-learn 0.22.1 py37h6288b17_0
scipy 1.4.1 py37h9439919_0
setuptools 46.1.3 py37_0
sip 4.19.8 py37h6538335_0
six 1.14.0 py37_0
sqlite 3.31.1 h2a8f88b_1
tensorboard 2.1.0 py3_0
tensorflow 1.15.0 gpu_py37hc3743a6_0
tensorflow-base 1.15.0 gpu_py37h1afeea4_0
tensorflow-estimator 1.15.1 pyh2649769_0
tensorflow-gpu 1.15.0 h0d30ee6_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 6.0.4 py37he774522_1
tqdm 4.45.0 py_0
urllib3 1.25.8 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
werkzeug 0.16.1 py_0
wheel 0.34.2 py37_0
win_inet_pton 1.1.0 py37_0
wincertstore 0.2 py37_0
wrapt 1.12.1 py37he774522_1
xz 5.2.5 h62dcd97_0
yaml 0.1.7 hc54c509_2
zlib 1.2.11 h62dcd97_4
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
kernel_size: 3
passes: 4
threshold: 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: True
[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: 62.5
mask_type: none
mask_blur_kernel: 0
mask_threshold: 4
learn_mask: False
icnr_init: False
conv_aware_init: False
subpixel_upscaling: False
reflect_padding: False
penalized_mask_loss: False
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
Re: The recovered images are quite blurred
I have trained for 20K iterations, and recovered images are quite better, the loss for each is about 0.039. I want to it would got better if I train longer, and when to stop ? Depends on loss?
Re: The recovered images are quite blurred
I typically run Realface or Villain and 300K iterations is generally minimum I do, even for a simple swap.
I've run one particularly difficult swap, due to lots of face movements, lighting changes and size changes, up to 1million !
If the face to swap is static. like a newsreader, you can get away with lower iterations. Loss on training is realatively meaningleess as it depends on the swap you want to do, a straight on face or action movie.
2 x 1070ti's, keeps tthe shack toasty when it's cold
An example, Facwswap Russell Crowe to Rylan Clark-Neal (A UK TV celeb) in the Gladiator. Two scenes with close up face and large change in lighting one side of face to other return only small improvements with more training. 450k, 600k and 1 million. Further away and 600k is acceptable. Click thumbnail for larger size.
Re: The recovered images are quite blurred
My recent experience:
Dlight: batches of 4 (sorry... little Gpu-RAM on my GTX1050) Almost 520K iterations and going. Almost 2K faces per set.
The eyes are just beginning to look realistic.... and looking in the right directions. Only very little photos still have a "blank" cornea or -quite odd- an inverted iris (Dark cornea and white Iris... just like the kids in the Village of the Damned)
Also the teeth are getting some nice details.
The input is the usual 256x256... the output 128x128.
I believe that patience is all, in this game (which is not bad... I work 9-11 hrs/day... and I launch the training on my pee-see in the morning before getting to work.... so... I got nothing to do but see the results in the evening.
P.S.
Any update on the new manual alignment tool? How is it going ?
Re: The recovered images are quite blurred
Become a patreon and find out. Buy these fellas some coffee.
I dunno what I'm doing
2X RTX 3090 : RTX 3080 : RTX: 2060 : 2x RTX 2080 Super : Ghetto 1060
Re: The recovered images are quite blurred
Although it may look good before a million
I've done it couple around 80k, with excellent facial data, to a 30sec clip and it looked pretty dang good.
I dunno what I'm doing
2X RTX 3090 : RTX 3080 : RTX: 2060 : 2x RTX 2080 Super : Ghetto 1060
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Re: The recovered images are quite blurred
Like ABigFlea said, this has been released to the Patrons in a beta test format. Torzdf will relelase it to everyone once he's happy with how it's working for them.