holywu / vs-scunet Goto Github PK

# Imports
import vapoursynth as vs
# getting Vapoursynth core
core = vs.core
import site
import os
import sys
# Import scripts folder
scriptPath = 'i:/Hybrid/64bit/vsscripts'
sys.path.insert(0, os.path.abspath(scriptPath))
import ctypes
# Adding torch dependencies to PATH
path = site.getsitepackages()[0]+'/torch_dependencies/bin/'
ctypes.windll.kernel32.SetDllDirectoryW(path)
path = path.replace('\\', '/')
os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
os.environ["CUDA_MODULE_LOADING"] = "LAZY"
# Loading Plugins
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
# source: 'G:\TestClips&Co\files\lossless\suzie_lossless..mp4'
# current color space: YUV444P8, bit depth: 8, resolution: 176x144, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: progressive
# Loading G:\TestClips&Co\files\lossless\suzie_lossless..mp4 using LibavSMASHSource
clip = core.lsmas.LibavSMASHSource(source="G:/TestClips&Co/files/lossless/suzie_lossless..mp4")
# Setting detected color matrix (470bg).
clip = core.std.SetFrameProps(clip, _Matrix=5)
# Setting color transfer info (470bg), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
# Setting color primaries info (), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
# making sure frame rate is set to 29.97
clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0) # progressive
from vsfemasr import femasr as FeMaSR
# adjusting color space from YUV444P8 to RGBH for VsFeMaSR
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")
# resizing using FeMaSR
clip = FeMaSR(clip=clip, model=1, device_index=0, trt=True, trt_cache_path=r"J:\tmp") # 704x576
# resizing 704x576 to 1280x1048
# adjusting resizing
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, range_s="limited")
clip = core.fmtc.resample(clip=clip, w=1280, h=1048, kernel="cubic", interlaced=False, interlacedd=False)
from vsscunet import scunet as SCUNet
# denoising using SCUNet
clip = SCUNet(clip=clip, model=4, cuda_graphs=True, nvfuser=True)
# adjusting output color from: RGBS to YUV420P10 for x265Model
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
# adjusted resizing to achieve PAR 1:1 for preview
# resizing for preview to square pixel to 1400x1048
clip = core.resize.Bicubic(clip=clip, width=1400, height=1048)
# set output frame rate to 29.97fps (progressive)
clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
# Output
clip.set_output()

Failed to evaluate the script:
Python exception: expected scalar type Half but found Float

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
File "J:\tmp\tempPreviewVapoursynthFile10_46_44_212.vpy", line 47, in 
clip = SCUNet(clip=clip, model=4)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\__init__.py", line 141, in scunet
module(static_input[i])
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 705, in forward
return compiled_f(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 656, in returned_function
compiled_fn = create_aot_dispatcher_function(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 512, in create_aot_dispatcher_function
return aot_dispatch_base(flat_fn, fake_flat_tensor_args, aot_config)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 277, in aot_dispatch_base
fw_module = make_fx(flat_fn, aot_config.decompositions)(*flat_args)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 652, in wrapped
t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 400, in dispatch_trace
graph = tracer.trace(root, concrete_args)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 739, in trace
(self.create_arg(fn(*args)),),
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 414, in wrapped
out = f(*tensors)
File "", line 1, in 
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 636, in flat_fn
tree_out = fn(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\functorch\_src\aot_autograd.py", line 695, in functional_call
return stateless.functional_call(mod, params_and_buffers, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\utils\stateless.py", line 156, in functional_call
out = module(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 717, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 366, in call_module
return forward(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 710, in forward
return _orig_module_call(mod, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\network_scunet.py", line 226, in forward
x2 = self.m_down1(x1)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 717, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 366, in call_module
return forward(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 710, in forward
return _orig_module_call(mod, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\container.py", line 204, in forward
input = module(input)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 717, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 366, in call_module
return forward(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 710, in forward
return _orig_module_call(mod, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\network_scunet.py", line 156, in forward
trans_x = self.trans_block(trans_x)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 717, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 366, in call_module
return forward(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 710, in forward
return _orig_module_call(mod, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\network_scunet.py", line 120, in forward
x = x + self.drop_path(self.msa(self.ln1(x)))
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 717, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 366, in call_module
return forward(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\_symbolic_trace.py", line 710, in forward
return _orig_module_call(mod, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsscunet\network_scunet.py", line 83, in forward
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\functional.py", line 378, in einsum
return _VF.einsum(equation, operands) # type: ignore[attr-defined]
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\utils\_python_dispatch.py", line 101, in __torch_dispatch__
return old.__torch_dispatch__(func, types, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 440, in __torch_dispatch__
return self.inner_torch_dispatch(func, types, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 465, in inner_torch_dispatch
out = proxy_call(self, func, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 218, in proxy_call
r = func.decompose(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\_ops.py", line 279, in decompose
return self._op_dk(dk, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\utils\_python_dispatch.py", line 101, in __torch_dispatch__
return old.__torch_dispatch__(func, types, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 440, in __torch_dispatch__
return self.inner_torch_dispatch(func, types, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 465, in inner_torch_dispatch
out = proxy_call(self, func, args, kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\experimental\proxy_tensor.py", line 304, in proxy_call
out = func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\_ops.py", line 257, in __call__
return self._op(*args, **kwargs or {})
RuntimeError: expected scalar type Half but found Float

# Imports
import vapoursynth as vs
# getting Vapoursynth core
core = vs.core
import site
import os
import sys
# Import scripts folder
scriptPath = 'i:/Hybrid/64bit/vsscripts'
sys.path.insert(0, os.path.abspath(scriptPath))
import ctypes
# Adding torch dependencies to PATH
path = site.getsitepackages()[0]+'/torch_dependencies/bin/'
ctypes.windll.kernel32.SetDllDirectoryW(path)
path = path.replace('\\', '/')
os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
os.environ["CUDA_MODULE_LOADING"] = "LAZY"
# Loading Plugins
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
# source: 'G:\TestClips&Co\files\lossless\suzie_lossless..mp4'
# current color space: YUV444P8, bit depth: 8, resolution: 176x144, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: progressive
# Loading G:\TestClips&Co\files\lossless\suzie_lossless..mp4 using LibavSMASHSource
clip = core.lsmas.LibavSMASHSource(source="G:/TestClips&Co/files/lossless/suzie_lossless..mp4")
# Setting detected color matrix (470bg).
clip = core.std.SetFrameProps(clip, _Matrix=5)
# Setting color transfer info (470bg), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
# Setting color primaries info (), when it is not set
clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
# making sure frame rate is set to 29.97
clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0) # progressive
from vsfemasr import femasr as FeMaSR
# adjusting color space from YUV444P8 to RGBS for VsFeMaSR
clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
# resizing using FeMaSR
clip = FeMaSR(clip=clip, model=1, device_index=0, trt=True, trt_cache_path=r"J:\tmp") # 704x576
# resizing 704x576 to 1280x1048
# adjusting resizing
clip = core.fmtc.resample(clip=clip, w=1280, h=1048, kernel="cubic", interlaced=False, interlacedd=False)
from vsscunet import scunet as SCUNet
# denoising using SCUNet
clip = SCUNet(clip=clip, model=4, cuda_graphs=True, nvfuser=True)
# adjusting output color from: RGBS to YUV420P10 for x265Model
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
# set output frame rate to 29.97fps (progressive)
clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
# Output
clip.set_output()