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27th October 2019, 23:13 | #23 | Link | |
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Quote:
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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11th December 2019, 21:47 | #27 | Link | |
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Quote:
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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11th December 2019, 23:02 | #28 | Link | |
Cary Knoop
Join Date: Feb 2017
Location: Newark CA, USA
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Quote:
I merely wanted to point out that not everybody will know this. |
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5th July 2020, 09:53 | #29 | Link |
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Here's what I did:
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() 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 ... 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() Code:
Error on frame 0 request: error in LoadLibraryA -> Does anyone know how to fix this? Cu Selur |
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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AVSRepoGUI // VSRepoGUI - Package Manager for AviSynth // VapourSynth VapourSynth Portable FATPACK || VapourSynth Database |
11th July 2020, 19:25 | #34 | Link |
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Have you tried newer/older torch torchvision version?
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AVSRepoGUI // VSRepoGUI - Package Manager for AviSynth // VapourSynth VapourSynth Portable FATPACK || VapourSynth Database |
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.) Cu Selur |
10th August 2020, 05:11 | #36 | Link |
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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 Code:
pip install vsgan 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\ |
11th December 2020, 10:07 | #38 | Link |
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btw. has anyone compiled and tried https://github.com/Sg4Dylan/vapoursy...nn-ncnn-vulkan ?
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15th December 2020, 14:05 | #39 | Link |
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No, I havenīt,
maybe this shader implementation can be used? https://github.com/igv/FSRCNN-TensorFlow/releases |
15th May 2021, 12:45 | #40 | Link |
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Since I was testing some stuff here's how on can create portable VSGAN (https://github.com/rlaPHOENiX/VSGAN) version:
In case you want to try out VSGAN:
start the preview. Cu Selur |
Tags |
esrgan, gan, upscale, vapoursynth |
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