Einige Bugs/Fehler - Printable Version

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: WARNING: [Torch-TensorRT] - WARNING The logger passed into createInferRuntime differs from one already registered for an existing builder, runtime, or refitter. So the current new logger is ignored, and TensorRT will use the existing one which is returned by nvinfer1::getLogger() instead.

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: --- vsViewer error - END ---

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: [Torch-TensorRT] - WARNING The logger passed into createInferRuntime differs from one already registered for an existing builder, runtime, or refitter. So the current new logger is ignored, and TensorRT will use the existing one which is returned by nvinfer1::getLogger() instead.

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: --- vsViewer output - START ---

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: --- vsViewer output - END ---

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: --- vsViewer error - START ---

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: ERROR: [Torch-TensorRT] - IRuntime::deserializeCudaEngine: Error Code 1: Serialization (Serialization assertion stdVersionRead == kSERIALIZATION_VERSION failed.Version tag does not match. Note: Current Version: 240, Serialized Engine Version: 239 In nvinfer1::rt::StdArchiveReader::stdArchiveReaderInitCommon at runtime/deserialization/stdArchiveReader.cpp:49)

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: --- vsViewer error - END ---

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: [Torch-TensorRT] - IRuntime::deserializeCudaEngine: Error Code 1: Serialization (Serialization assertion stdVersionRead == kSERIALIZATION_VERSION failed.Version tag does not match. Note: Current Version: 240, Serialized Engine Version: 239 In nvinfer1::rt::StdArchiveReader::stdArchiveReaderInitCommon at runtime/deserialization/stdArchiveReader.cpp:49)

2025.09.01 - 10:40:40_Windows 10 Version 22H2 (64bit)_2025.08.30.1 - level 9: Failed to evaluate the script:

Python exception: [Error thrown at core/runtime/TRTEngine.cpp:119] Expected (cuda_engine.get() != nullptr) to be true but got false

Unable to deserialize the TensorRT engine





Traceback (most recent call last):

  File "src/cython/vapoursynth.pyx", line 3378, in vapoursynth._vpy_evaluate

  File "src/cython/vapoursynth.pyx", line 3379, in vapoursynth._vpy_evaluate

  File "C:\Users\Peter\AppData\Local\Temp\tempPreviewVapoursynthFile10_39_06_118.vpy", line 80, in <module>

    clip = RealESRGAN(clip=clip, model=4, device_index=0, trt=True, trt_cache_dir=r"C:\Users\Peter\AppData\Local\Temp") # 7680x3264

           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "contextlib.py", line 81, in inner

  File "C:\Program Files\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context

    return func(*args, **kwargs)

           ^^^^^^^^^^^^^^^^^^^^^

  File "C:\Program Files\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsrealesrgan\__init__.py", line 347, in realesrgan

    module = [torch.jit.load(trt_engine_path).eval() for _ in range(num_streams)]

              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "C:\Program Files\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\jit\_serialization.py", line 168, in load

    cpp_module = torch._C.import_ir_module(

                 ^^^^^^^^^^^^^^^^^^^^^^^^^^

RuntimeError: [Error thrown at core/runtime/TRTEngine.cpp:119] Expected (cuda_engine.get() != nullptr) to be true but got false

Unable to deserialize the TensorRT engine

# Imports

import vapoursynth as vs

# getting Vapoursynth core

import logging

import site

import sys

import os

core = vs.core

# Import scripts folder

scriptPath = 'C:/Program Files/Hybrid/64bit/vsscripts'

sys.path.insert(0, os.path.abspath(scriptPath))

os.environ["CUDA_MODULE_LOADING"] = "LAZY"

# Force logging to std:err

logging.StreamHandler(sys.stderr)

# loading plugins

core.std.LoadPlugin(path="C:/Program Files/Hybrid/64bit/vsfilters/Support/vszip.dll")

core.std.LoadPlugin(path="C:/Program Files/Hybrid/64bit/vs-mlrt/vstrt.dll")

core.std.LoadPlugin(path="C:/Program Files/Hybrid/64bit/vsfilters/Support/fmtconv.dll")

core.std.LoadPlugin(path="C:/Program Files/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/LSMASHSource.dll")

# Import scripts

from importlib.machinery import SourceFileLoader

vsmlrt = SourceFileLoader('vsmlrt', 'C:/Program Files/Hybrid/64bit/vs-mlrt/vsmlrt.py').load_module()

from vsmlrt import Backend

import validate

# Source: 'C:\Users\Peter\Downloads\Upscaling\Lord.of.Mysteries.S01E01.German.ML.EAC3.1080p.WebDL.x264-Pudding-sama.mkv'

# Current color space: YUV420P8, bit depth: 8, resolution: 1920x816, frame rate: 25fps, scanorder: progressive, yuv luminance scale: limited, matrix: 709, format: AVC

# Loading C:\Users\Peter\Downloads\Upscaling\Lord.of.Mysteries.S01E01.German.ML.EAC3.1080p.WebDL.x264-Pudding-sama.mkv using LWLibavSource

clip = core.lsmas.LWLibavSource(source="C:/Users/Peter/Downloads/Upscaling/Lord.of.Mysteries.S01E01.German.ML.EAC3.1080p.WebDL.x264-Pudding-sama.mkv", format="YUV420P8", stream_index=0, cache=0, prefer_hw=0)

frame = clip.get_frame(0)

# setting color matrix to 709.

clip = core.std.SetFrameProps(clip, _Matrix=vs.MATRIX_BT709)

# setting color transfer (vs.TRANSFER_BT709), if it is not set.

if validate.transferIsInvalid(clip):

  clip = core.std.SetFrameProps(clip=clip, _Transfer=vs.TRANSFER_BT709)

# setting color primaries info (to vs.PRIMARIES_BT709), if it is not set.

if validate.primariesIsInvalid(clip):

  clip = core.std.SetFrameProps(clip=clip, _Primaries=vs.PRIMARIES_BT709)

# setting color range to TV (limited) range.

clip = core.std.SetFrameProps(clip=clip, _ColorRange=vs.RANGE_LIMITED)

# making sure frame rate is set to 25fps

clip = core.std.AssumeFPS(clip=clip, fpsnum=25, fpsden=1)

# making sure the detected scan type is set (detected: progressive)

clip = core.std.SetFrameProps(clip=clip, _FieldBased=vs.FIELD_PROGRESSIVE) # progressive

# changing range from limited to full range for vsVSMLRTFilter

clip = core.resize.Bicubic(clip, format=vs.YUV420P8, matrix_s="709", range_in_s="limited", range_s="full")

# setting color range to PC (full) range.

clip = core.std.SetFrameProps(clip=clip, _ColorRange=vs.RANGE_FULL)

# adjusting color space from YUV420P8 to RGBS for vsVSMLRTFilter

clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="709", range_in_s="full", range_s="full")

# filtering using VSMLRT

clip = vsmlrt.inference([clip],network_path="C:/Program Files/Hybrid/64bit/onnx_models/2x_LD-Anime_Compact_330k_net_g.onnx", backend=Backend.TRT(fp16=False,device_id=0,bf16=True,num_streams=1,verbose=True,use_cuda_graph=False,workspace=1073741824,builder_optimization_level=3,engine_folder="C:/Users/Peter/AppData/Local/Temp")) # 3840x1632

# undo resizing 3840x1632 to 1920x816

clip = core.resize.Spline64(clip=clip, width=1920, height=816) # resolution 1920x816)

# changing range from full to limited range for vsVSMLRTFilter

clip = core.resize.Bicubic(clip, format=vs.RGBS,range_in_s="full", range_s="limited")

# making sure 0-1 limits are respected

clip = core.vszip.Limiter(clip=clip, min=[0,0,0], max=[1,1,1])

from vsrealesrgan import realesrgan as RealESRGAN

# adjusting color space from RGBS to RGBH for vsRealESRGAN

clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, dither_type="error_diffusion")

# resizing using RealESRGAN

clip = RealESRGAN(clip=clip, model=4, device_index=0, trt=True, trt_cache_dir=r"C:\Users\Peter\AppData\Local\Temp") # 7680x3264

# resizing 7680x3264 to 3840x1632

clip = core.resize.Bicubic(clip=clip, format=vs.RGBS)

clip = core.fmtc.resample(clip=clip, w=3840, h=1632, kernel="spline", taps=6, interlaced=False, interlacedd=False)

# adjusting output color from: RGBS to YUV420P10 for NVEncModel

clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="709", range_in_s="full", range_s="limited", dither_type="error_diffusion") # additional resize to allow target color sampling

# set output frame rate to 25fps (progressive)

clip = core.std.AssumeFPS(clip=clip, fpsnum=25, fpsden=1)

# output

clip.set_output()