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Old 24th October 2019, 04:21   #21  |  Link
Cary Knoop
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Quote:
Originally Posted by poisondeathray View Post
lower sec / frame means it 's faster
Oops, my mistake, I thought I read frames per second.
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Old 25th October 2019, 00:18   #22  |  Link
Cary Knoop
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It would probably be useful to add installation instructions for portable Vapoursynth installs as well.
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Old 27th October 2019, 23:13   #23  |  Link
AlphaAtlas
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Quote:
Originally Posted by Cary Knoop View Post
It would probably be useful to add installation instructions for portable Vapoursynth installs as well.
It works the same in the VS Fatpack for me. Just open a console window wherever python.exe is, and then install all the dependencies with pip the same way.

Unfortunately CUDA isn't portable, so that part needs a system install.

Last edited by AlphaAtlas; 27th October 2019 at 23:16.
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Old 28th October 2019, 01:50   #24  |  Link
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Quote:
Originally Posted by AlphaAtlas View Post
It works the same in the VS Fatpack for me. Just open a console window wherever python.exe is, and then install all the dependencies with pip the same way.
It's looking for the Vapoursynth installation path.
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Old 11th December 2019, 20:08   #25  |  Link
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Quote:
Originally Posted by Cary Knoop View Post
It's looking for the Vapoursynth installation path.
VSGAN doesn't care where or how you have VapourSynth installed. All it cares is you have installed it via pip and that you have all dependencies especially PyTorch installed.
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Old 11th December 2019, 20:31   #26  |  Link
Cary Knoop
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Quote:
Originally Posted by PRAGMA View Post
VSGAN doesn't care where or how you have VapourSynth installed. All it cares is you have installed it via pip and that you have all dependencies especially PyTorch installed.
That was my whole point if you have an install (not through pip) it fails.
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Old 11th December 2019, 21:47   #27  |  Link
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Quote:
Originally Posted by Cary Knoop View Post
That was my whole point if you have an install (not through pip) it fails.
AFAIK it's not really practical to install without pip, if that's what you mean.

To be clear, what I do with the vapoursynth fatpack is call "C:/pathtofatpack/VapourSynth64/python.exe -m pip install vsgan". You have to specify the portable python path, otherwise you'll call pip in a python setup that doesn't have access to VS.

On Linux, you can also set the PYTHONPATH environment variable in the terminal if you have multiple Python installs.

Last edited by brucethemoose; 11th December 2019 at 21:52.
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Old 11th December 2019, 23:02   #28  |  Link
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Quote:
Originally Posted by brucethemoose View Post
To be clear, what I do with the vapoursynth fatpack is call "C:/pathtofatpack/VapourSynth64/python.exe -m pip install vsgan". You have to specify the portable python path, otherwise you'll call pip in a python setup that doesn't have access to VS.
That would work.

I merely wanted to point out that not everybody will know this.
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Old 5th July 2020, 09:53   #29  |  Link
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Here's what I did:
  1. download lates VapourSynth64Portable
    https://github.com/theChaosCoder/vap...TPACK/releases
  2. upgrade pip
    Code:
    python.exe -m pip install --upgrade pip
  3. installed torch (inside the Vapoursynth64 folder)
    Code:
    python.exe -m pip install torch===1.4.0 torchvision===0.5.0 -f https://download.pytorch.org/whl/torch_stable.html
  4. installed vsgan:
    Code:
    python -m pip install vsgan

Then I used a script that worked before:
Code:
# Imports
import vapoursynth as vs
core = vs.get_core()
# Loading Plugins
from vsgan import VSGAN
vsgan_device = VSGAN("cuda")
# Loading C:/Users/Selur/Desktop/5000frames.mp4 using LWLibavSource
clip = core.lsmas.LWLibavSource(source="F:/TestClips&Co/files/5000frames.mp4", format="YUV420P10", cache=0, prefer_hw=0)
# making sure input color matrix is set as 470bg
clip = core.resize.Point(clip, matrix_in_s="470bg",range_s="limited")
# making sure frame rate is set to 25
clip = core.std.AssumeFPS(clip, fpsnum=25, fpsden=1)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)

vsgan_device.load_model(
    model="I:/Hybrid/64bit/vsfilters/ResizeFilter/VSGRAN/models/4x_xbrz_90k.pth",
    scale=2
)
clip = vsgan_device.run(clip=clip, chunk=True)

clip.set_output()
(see: https://github.com/rlaPHOENiX/VSGAN/issues/1)
which now gives me:
Code:
Python exception: Error(s) in loading state_dict for RRDBNet:
Missing key(s) in state_dict: "conv_first.weight", "conv_first.bias", "RRDB_trunk.0.RDB1.conv1.weight", "RRDB_trunk.0.RDB1.conv1.bias", "RRDB_trunk.0.RDB1.conv2.weight", "RRDB_trunk.0.RDB1.conv2.bias", "RRDB_trunk.0.RDB1.conv3.weight", "RRDB_trunk.0.RDB1.conv3.bias", "RRDB_trunk.0.RDB1.conv4.weight", "RRDB_trunk.0.RDB1.conv4.bias", "RRDB_trunk.0.RDB1.conv5.weight", "RRDB_trunk.0.RDB1.conv5.bias", "RRDB_trunk.0.RDB2.conv1.weight", "RRDB_trunk.0.RDB2.conv1.bias", "RRDB_trunk.0.RDB2.conv2.weight", "RRDB_trunk.0.RDB2.conv2.bias", "RRDB_trunk.0.RDB2.conv3.weight", "RRDB_trunk.0.RDB2.conv3.bias", "RRDB_trunk.0.RDB2.conv4.weight", "RRDB_trunk.0.RDB2.conv4.bias", "RRDB_trunk.0.RDB2.conv5.weight", "RRDB_trunk.0.RDB2.conv5.bias", "RRDB_trunk.0.RDB3.conv1.weight", "RRDB_trunk.0.RDB3.conv1.bias", "RRDB_trunk.0.RDB3.conv2.weight", "RRDB_trunk.0.RDB3.conv2.bias", "RRDB_trunk.0.RDB3.conv3.weight", "RRDB_trunk.0.RDB3.conv3.bias", "RRDB_trunk.0.RDB3.conv4.weight", "RRDB_trunk.0.RDB3.conv4.bias", "RRDB_trunk.0.RDB3.conv5.weight", "RRDB_trunk.0.RDB3.conv5.bias", "RRDB_trunk.1.RDB1.conv1.weight", "RRDB_trunk.1.RDB1.conv1.bias", "RRDB_trunk.1.RDB1.conv2.weight", "RRDB_trunk.1.RDB1.conv2.bias", "RRDB_trunk.1.RDB1.conv3.weight", "RRDB_trunk.1.RDB1.conv3.bias", "RRDB_trunk.1.RDB1.conv4.weight", "RRDB_trunk.1.RDB1.conv4.bias", "RRDB_trunk.1.RDB1.conv5.weight", "RRDB_trunk.1.RDB1.conv5.bias", "RRDB_trunk.1.RDB2.conv1.weight", "RRDB_trunk.1.RDB2.conv1.bias", "RRDB_trunk.1.RDB2.conv2.weight", "RRDB_trunk.1.RDB2.conv2.bias", "RRDB_trunk.1.RDB2.conv3.weight", "RRDB_trunk.1.RDB2.conv3.bias", "RRDB_trunk.1.RDB2.conv4.weight", "RRDB_trunk.1.RDB2.conv4.bias", "RRDB_trunk.1.RDB2.conv5.weight", "RRDB_trunk.1.RDB2.conv5.bias", "RRDB_trunk.1.RDB3.conv1.weight", "RRDB_trunk.1.RDB3.conv1.bias", "RRDB_trunk.1.RDB3.conv2.weight", "RRDB_trunk.1.RDB3.conv2.bias", "RRDB_trunk.1.RDB3.conv3.weight", "RRDB_trunk.1.RDB3.conv3.bias", "RRDB_trunk.1.RDB3.conv4.weight", "RRDB_trunk.1.RDB3.conv4.bias", "RRDB_trunk.1.RDB3.conv5.weight", "RRDB_trunk.1.RDB3.conv5.bias", "RRDB_trunk.2.RDB1.conv1.weight", "RRDB_trunk.2.RDB1.conv1.bias", "RRDB_trunk.2.RDB1.conv2.weight", "RRDB_trunk.2.RDB1.conv2.bias", "RRDB_trunk.2.RDB1.conv3.weight", "RRDB_trunk.2.RDB1.conv3.bias", "RRDB_trunk.2.RDB1.conv4.weight", "RRDB_trunk.2.RDB1.conv4.bias", "RRDB_trunk.2.RDB1.conv5.weight", "RRDB_trunk.2.RDB1.conv5.bias", "RRDB_trunk.2.RDB2.conv1.weight", "RRDB_trunk.2.RDB2.conv1.bias", "RRDB_trunk.2.RDB2.conv2.weight", "RRDB_trunk.2.RDB2.conv2.bias", "RRDB_trunk.2.RDB2.conv3.weight", "RRDB_trunk.2.RDB2.conv3.bias", "RRDB_trunk.2.RDB2.conv4.weight", "RRDB_trunk.2.RDB2.conv4.bias", "RRDB_trunk.2.RDB2.conv5.weight", "RRDB_trunk.2.RDB2.conv5.bias", "RRDB_trunk.2.RDB3.conv1.weight", "RRDB_trunk.2.RDB3.conv1.bias", "RRDB_trunk.2.RDB3.conv2.weight", "RRDB_trunk.2.RDB3.conv2.bias", "RRDB_trunk.2.RDB3.conv3.weight", "RRDB_trunk.2.RDB3.conv3.bias", "RRDB_trunk.2.RDB3.conv4.weight", "RRDB_trunk.2.RDB3.conv4.bias", "RRDB_trunk.2.RDB3.conv5.weight", "RRDB_trunk.2.RDB3.conv5.bias", "RRDB_trunk.3.RDB1.conv1.weight", "RRDB_trunk.3.RDB1.conv1.bias", "RRDB_trunk.3.RDB1.conv2.weight", "RRDB_trunk.3.RDB1.conv2.bias", "RRDB_trunk.3.RDB1.conv3.weight", "RRDB_trunk.3.RDB1.conv3.bias", "RRDB_trunk.3.RDB1.conv4.weight", "RRDB_trunk.3.RDB1.conv4.bias", "RRDB_trunk.3.RDB1.conv5.weight", "RRDB_trunk.3.RDB1.conv5.bias", "RRDB_trunk.3.RDB2.conv1.weight", "RRDB_trunk.3.RDB2.conv1.bias", "RRDB_trunk.3.RDB2.conv2.weight", "RRDB_trunk.3.RDB2.conv2.bias", "RRDB_trunk.3.RDB2.conv3.weight", "RRDB_trunk.3.RDB2.conv3.bias", "RRDB_trunk.3.RDB2.conv4.weight", "RRDB_trunk.3.RDB2.conv4.bias", "RRDB_trunk.3.RDB2.conv5.weight", "RRDB_trunk.3.RDB2.conv5.bias", "RRDB_trunk.3.RDB3.conv1.weight", "RRDB_trunk.3.RDB3.conv1.bias", "RRDB_trunk.3.RDB3.conv2.weight", "RRDB_trunk.3.RDB3.conv2.bias", "RRDB_trunk.3.RDB3.conv3.weight", "RRDB_trunk.3.RDB3.conv3.bias", "RRDB_trunk.3.RDB3.conv4.weight", "RRDB_trunk.3.RDB3.conv4.bias", "RRDB_trunk.3.RDB3.conv5.weight", "RRDB_trunk.3.RDB3.conv5.bias", "RRDB_trunk.4.RDB1.conv1.weight", "RRDB_trunk.4.RDB1.conv1.bias", "RRDB_trunk.4.RDB1.conv2.weight", "RRDB_trunk.4.RDB1.conv2.bias", "RRDB_trunk.4.RDB1.conv3.weight", "RRDB_trunk.4.RDB1.conv3.bias", "RRDB_trunk.4.RDB1.conv4.weight", "RRDB_trunk.4.RDB1.conv4.bias", "RRDB_trunk.4.RDB1.conv5.weight", "RRDB_trunk.4.RDB1.conv5.bias", "RRDB_trunk.4.RDB2.conv1.weight", "RRDB_trunk.4.RDB2.conv1.bias", "RRDB_trunk.4.RDB2.conv2.weight", "RRDB_trunk.4.RDB2.conv2.bias", "RRDB_trunk.4.RDB2.conv3.weight", "RRDB_trunk.4.RDB2.conv3.bias", "RRDB_trunk.4.RDB2.conv4.weight", "RRDB_trunk.4.RDB2.conv4.bias", "RRDB_trunk.4.RDB2.conv5.weight", "RRDB_trunk.4.RDB2.conv5.bias", "RRDB_trunk.4.RDB3.conv1.weight", "RRDB_trunk.4.RDB3.conv1.bias", "RRDB_trunk.4.RDB3.conv2.weight", "RRDB_trunk.4.RDB3.conv2.bias", "RRDB_trunk.4.RDB3.conv3.weight", "RRDB_trunk.4.RDB3.conv3.bias", "RRDB_trunk.4.RDB3.conv4.weight", "RRDB_trunk.4.RDB3.conv4.bias", "RRDB_trunk.4.RDB3.conv5.weight", "RRDB_trunk.4.RDB3.conv5.bias", "RRDB_trunk.5.RDB1.conv1.weight", "RRDB_trunk.5.RDB1.conv1.bias", "RRDB_trunk.5.RDB1.conv2.weight", "RRDB_trunk.5.RDB1.conv2.bias", "RRDB_trunk.5.RDB1.conv3.weight", "RRDB_trunk.5.RDB1.conv3.bias", "RRDB_trunk.5.RDB1.conv4.weight", "RRDB_trunk.5.RDB1.conv4.bias", "RRDB_trunk.5.RDB1.conv5.weight", "RRDB_trunk.5.RDB1.conv5.bias", "RRDB_trunk.5.RDB2.conv1.weight", "RRDB_trunk.5.RDB2.conv1.bias", "RRDB_trunk.5.RDB2.conv2.weight", "RRDB_trunk.5.RDB2.conv2.bias", "RRDB_trunk.5.RDB2.conv3.weight", "RRDB_trunk.5.RDB2.conv3.bias", "RRDB_trunk.5.RDB2.conv4.weight", "RRDB_trunk.5.RDB2.conv4.bias", "RRDB_trunk.5.RDB2.conv5.weight", "RRDB_trunk.5.RDB2.conv5.bias", "RRDB_trunk.5.RDB3.conv1.weight",
....
Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 1956, in vapoursynth.vpy_evaluateScript
File "src\cython\vapoursynth.pyx", line 1957, in vapoursynth.vpy_evaluateScript
File "C:/Users/Selur/Desktop/vsgantest.vpy", line 16, in 
vsgan_device.load_model(
File "C:\Users\Selur\Desktop\VapourSynth64Portable\VapourSynth64\Lib\site-packages\vsgan\__init__.py", line 34, in load_model
self.rrdb_net_model.load_state_dict(torch.load(self.model_file), strict=True)
File "C:\Users\Selur\Desktop\VapourSynth64Portable\VapourSynth64\Lib\site-packages\torch\nn\modules\module.py", line 829, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for RRDBNet:
Missing key(s) in state_dict: "conv_first.weight", "conv_first.bias", "RRDB_trunk.0.RDB1.conv1.weight", "RRDB_trunk.0.RDB1.conv1.bias", "RRDB_trunk.0.RDB1.conv2.weight", "RRDB_trunk.0.RDB1.conv2.bias", "RRDB_trunk.0.RDB1.conv3.weight", "RRDB_trunk.0.RDB1.conv3.bias", "RRDB_trunk.0.RDB1.conv4.weight", "RRDB_trunk.0.RDB1.conv4.bias", "RRDB_trunk.0.RDB1.conv5.weight", "RRDB_trunk.0.RDB1.conv5.bias", "RRDB_trunk.0.RDB2.conv1.weight", "RRDB_trunk.0.RDB2.conv1.bias", "RRDB_trunk.0.RDB2.conv2.weight", "RRDB_trunk.0.RDB2.conv2.bias", "RRDB_trunk.0.RDB2.conv3.weight", "RRDB_trunk.0.RDB2.conv3.bias", "RRDB_trunk.0.RDB2.conv4.weight", "RRDB_trunk.0.RDB2.conv4.bias", "RRDB_trunk.0.RDB2.conv5.weight", "RRDB_trunk.0.RDB2.conv5.bias", "RRDB_trunk.0.RDB3.conv1.weight", "RRDB_trunk.0.RDB3.conv1.bias", "RRDB_trunk.0.RDB3.conv2.weight", "RRDB_trunk.0.RDB3.conv2.bias", "RRDB_trunk.0.RDB3.conv3.weight", "RRDB_trunk.0.RDB3.conv3.bias", "RRDB_trunk.0.RDB3.conv4.weight", "RRDB_trunk.0.RDB3.conv4.bias", "RRDB_trunk.0.RDB3.conv5.weight", "RRDB_trunk.0.RDB3.conv5.bias", "RRDB_trunk.1.RDB1.conv1.weight", "RRDB_trunk.1.RDB1.conv1.bias", "RRDB_trunk.1.RDB1.conv2.weight", "RRDB_trunk.1.RDB1.conv2.bias", "RRDB_trunk.1.RDB1.conv3.weight", "RRDB_trunk.1.RDB1.conv3.bias", "RRDB_trunk.1.RDB1.conv4.weight", "RRDB_trunk.1.RDB1.conv4.bias", "RRDB_trunk.1.RDB1.conv5.weight", "RRDB_trunk.1.RDB1.conv5.bias", "RRDB_trunk.1.RDB2.conv1.weight", "RRDB_trunk.1.RDB2.conv1.bias", "RRDB_trunk.1.RDB2.conv2.weight", "RRDB_trunk.1.RDB2.conv2.bias", "RRDB_trunk.1.RDB2.conv3.weight", "RRDB_trunk.1.RDB2.conv3.bias", "RRDB_trunk.1.RDB2.conv4.weight", "RRDB_trunk.1.RDB2.conv4.bias", "RRDB_trunk.1.RDB2.conv5.weight", "RRDB_trunk.1.RDB2.conv5.bias", "RRDB_trunk.1.RDB3.conv1.weight", "RRDB_trunk.1.RDB3.conv1.bias", "RRDB_trunk.1.RDB3.conv2.weight", "RRDB_trunk.1.RDB3.conv2.bias", "RRDB_trunk.1.RDB3.conv3.weight", "RRDB_trunk.1.RDB3.conv3.bias", "RRDB_trunk.1.RDB3.conv4.weight", "RRDB_trunk.1.RDB3.conv4.bias", "RRDB_trunk.1.RDB3.conv5.weight", "RRDB_trunk.1.RDB3.conv5.bias", "RRDB_trunk.2.RDB1.conv1.weight", "RRDB_trunk.2.RDB1.conv1.bias", "RRDB_trunk.2.RDB1.conv2.weight", "RRDB_trunk.2.RDB1.conv2.bias", "RRDB_trunk.2.RDB1.conv3.weight", "RRDB_trunk.2.RDB1.conv3.bias", "RRDB_trunk.2.RDB1.conv4.weight", "RRDB_trunk.2.RDB1.conv4.bias", "RRDB_trunk.2.RDB1.conv5.weight", "RRDB_trunk.2.RDB1.conv5.bias", "RRDB_trunk.2.RDB2.conv1.weight", "RRDB_trunk.2.RDB2.conv1.bias", "RRDB_trunk.2.RDB2.conv2.weight", "RRDB_trunk.2.RDB2.conv2.bias", "RRDB_trunk.2.RDB2.conv3.weight", "RRDB_trunk.2.RDB2.conv3.bias", "RRDB_trunk.2.RDB2.conv4.weight", "RRDB_trunk.2.RDB2.conv4.bia
...
Okay, I thought since its 4 months since I last looked at this may be something changed that I'm not aware of so I looked at https://drive.google.com/drive/u/0/f...dMAIccSSlqLecY and tried the 'RRDB_ESRGAN_x4.pth'-model
using:
Code:
# Imports
import vapoursynth as vs
core = vs.get_core()
# Loading Plugins
from vsgan import VSGAN
vsgan_device = VSGAN("cuda")
# Loading C:/Users/Selur/Desktop/5000frames.mp4 using LWLibavSource
clip = core.lsmas.LWLibavSource(source="F:/TestClips&Co/files/5000frames.mp4", format="YUV420P10", cache=0, prefer_hw=0)
# making sure input color matrix is set as 470bg
clip = core.resize.Point(clip, matrix_in_s="470bg",range_s="limited")
# making sure frame rate is set to 25
clip = core.std.AssumeFPS(clip, fpsnum=25, fpsden=1)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)

vsgan_device.load_model(
    model="I:/Hybrid/64bit/vsfilters/ResizeFilter/VSGRAN/models/RRDB_ESRGAN_x4.pth",
    scale=2
)
clip = vsgan_device.run(clip=clip, chunk=True)

clip.set_output()
but that only gives me:
Code:
Error on frame 0 request:
error in LoadLibraryA
using RRDB_ESRGAN_x4_old_arch instead of RRDB_ESRGAN_x4 gives me the same errors as with 4x_xbrz_90k.

-> Does anyone know how to fix this?

Cu Selur
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Old 5th July 2020, 11:39   #30  |  Link
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You could also try https://github.com/AlphaAtlas/Vapour...olution-Helper
I noticed that there are some modules that don't play nicely with the python embedded version (https://github.com/Irrational-Encoding-Wizardry/yuuno is one of such module). I think you can take a regular python intstallation and use it more or less as a "portable" version (install in a vm and then just copy the install folder to you hdd)

Maybe then it will work.
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Old 5th July 2020, 12:43   #31  |  Link
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The strange thing is that the above did work a while back.
My main problem is I don't know why it isn't working now.
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Old 5th July 2020, 14:43   #32  |  Link
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You're using a 4x trained model , but specified scale=2 . Did you try scale=4 ?
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Old 11th July 2020, 18:09   #33  |  Link
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Nope, since it worked before with 2, but I just did and still got the same error.
Code:
Error on frame 0 request:
error in LoadLibraryA
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Old 11th July 2020, 19:25   #34  |  Link
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Have you tried newer/older torch torchvision version?
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Old 11th July 2020, 20:23   #35  |  Link
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Nope, didn't have the time to play with it that much. Only looked at it since it looked interesting and I thought about adding support for it in Hybrid.
Also since that torch version worked before, I haven't really spend much thought about trying other versions.
Thought others might have played with it and directly knew what was going wrong.
(Don't know when I will look at this more actively since I'm quite busy with normal live atm.)

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Old 10th August 2020, 05:11   #36  |  Link
_Al_
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Installing pytorch using cpu only, which went well:
Code:
pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
then for final install:
Code:
pip install vsgan
I get error not having Microsoft Visual C++ v 14.0. But I have in PC 2008, 2013 and Microsoft Visual C++ 2017 which is 14.15.26706
Anyone what could go wrong?
Code:
C:\Users\xxx\AppData\Local\Programs\Python\Python37\Scripts>pip3 install vsgan
Collecting vsgan
  Downloading https://files.pythonhosted.org/packages/1d/eb/369bc17433a3e8ab8bf7ab0a74ab7052f28b35ecb2de07683163d385eb2f/vsgan-1.0.8-py3-none-any.whl
Collecting vapoursynth (from vsgan)
  Downloading https://files.pythonhosted.org/packages/41/a6/75c8e6c37e26641f73cd967f4c365655b206f279ddd52461f4a1b9bd1621/VapourSynth-51.zip (426kB)
    100% |████████████████████████████████| 430kB 5.4MB/s
Requirement already satisfied: torch in c:\users\xxx\appdata\local\programs\python\python37\lib\site-packages (from vsgan) (1.6.0+cpu)
Requirement already satisfied: numpy in c:\users\xxx\appdata\local\programs\python\python37\lib\site-packages (from vsgan) (1.16.2)
Requirement already satisfied: future in c:\users\xxx\appdata\local\programs\python\python37\lib\site-packages (from torch->vsgan) (0.18.2)
Installing collected packages: vapoursynth, vsgan
  Running setup.py install for vapoursynth ... error
    Complete output from command c:\users\xxx\appdata\local\programs\python\python37\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\
\xxx\\AppData\\Local\\Temp\\pip-install-s6avgaje\\vapoursynth\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n'
);f.close();exec(compile(code, __file__, 'exec'))" install --record C:\Users\xxx\AppData\Local\Temp\pip-record-ntdlen2q\install-record.txt --single-ver
sion-externally-managed --compile:
    Found VapourSynth.dll at: C:\Program Files (x86)\VapourSynth\core64\vapoursynth.dll
    running install
    running build
    running build_ext
    skipping 'src\cython\vapoursynth.c' Cython extension (up-to-date)
    building 'vapoursynth' extension
    error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": https://visualstudio.microsoft.com/downloads/

    ----------------------------------------
Command "c:\users\xxx\appdata\local\programs\python\python37\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\xxxl\\AppData\\Local\\Tem
p\\pip-install-s6avgaje\\vapoursynth\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile
(code, __file__, 'exec'))" install --record C:\Users\xx\AppData\Local\Temp\pip-record-ntdlen2q\install-record.txt --single-version-externally-managed
--compile" failed with error code 1 in C:\Users\xxx\AppData\Local\Temp\pip-install-s6avgaje\vapoursynth\
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Old 10th August 2020, 07:46   #37  |  Link
Cary Knoop
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Location: Newark CA, USA
Posts: 397
Try to uninstall VC 2017 and then install VC 2015 Build Tools and then install VC 2017 again.
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