vapoursynth-mlrt-trt-rtx 15.16.1

TensorRT-based for RTX GPU inference plugin for VapourSynth

VapourSynth-MLRT-TRT-RTX

This package contains the TensorRT-based for RTX GPU inference backend implementation of the vs-mlrt plugin.

Installation

pip install vapoursynth-mlrt-trt-rtx

Building from source

Requirements

• C++ Compiler: C++20 compatible (e.g. MSVC 2019+, GCC, Clang)
• Dependencies:
  • CUDAToolkit
  • TensorRT-RTX SDK
• Environment Variables:
  • TENSORRT_RTX_HOME: Path to the TensorRT-RTX installation directory (must contain include, lib, and bin).

Compilation

Set the TENSORRT_RTX_HOME environment variable before running the build:

$env:TENSORRT_RTX_HOME="C:\Path\To\TensorRT-RTX"

uv build --package vapoursynth-mlrt-trt-rtx

On Linux:

export TENSORRT_RTX_HOME="/path/to/TensorRT-RTX"

uv build --package vapoursynth-mlrt-trt-rtx

---

Detailed parameter information from the parent project follows.

---

VapourSynth TensorRT & TensorRT-RTX

The vs-tensorrt plugin provides optimized CUDA runtime for some popular AI filters.

Usage

Prototype: core.{trt, trt_rtx}.Model(clip[] clips, string engine_path[, int[] overlap, int[] tilesize, int device_id=0, bint use_cuda_graph=False, int num_streams=1, int verbosity=2, string flexible_output_prop=""])

Arguments:

• clip[] clips: the input clips, only 32-bit floating point RGB or GRAY clips are supported. For model specific input requirements, please consult our wiki.

• string engine_path: the path to the prebuilt engine (see below)

• int[] overlap: some networks (e.g. CNN) support arbitrary input shape where other networks might only support fixed input shape and the input clip must be processed in tiles. The overlap argument specifies the overlapping (horizontal and vertical, or both, in pixels) between adjacent tiles to minimize boundary issues. Please refer to network specific docs on the recommended overlapping size.

• int[] tilesize: Even for CNN where arbitrary input sizes could be supported, sometimes the network does not work well for the entire range of input dimensions, and you have to limit the size of each tile. This parameter specify the tile size (horizontal and vertical, or both, including the overlapping). Please refer to network specific docs on the recommended tile size.

• int device_id: Specifies the GPU device id to use, default 0. Requires Nvidia GPUs with second-generation Kepler architecture onwards.

• bint use_cuda_graph: whether to use CUDA Graphs to improve performance and reduce CPU overhead.

• int num_streams: number of concurrent CUDA streams to use. Default 1. Increase if GPU not saturated.

• verbosity: The verbosity level of TensorRT runtime. The message writes to stderr. 0: Internal error. 1: Application error. 2: Warning. 3: Informational messages with instructional information. 4: Verbose messages with debugging information.

• string flexible_output_prop: used to support onnx models with arbitrary number of output planes.

  from typing import TypedDict
  
  class Output(TypedDict):
      clip: vs.VideoNode
      num_planes: int
  
  prop = "planes" # arbitrary non-empty string
  output = core.trt.Model(src, engine_path, flexible_output_prop=prop) # type: Output
  
  clip = output["clip"]
  num_planes = output["num_planes"]
  
  output_planes = [
      clip.std.PropToClip(prop=f"{prop}{i}")
      for i in range(num_planes)
  ] # type: list[vs.VideoNode]
  

When overlap and tilesize are not specified, the filter will internally try to resize the network to fit the input clips. This might not always work (for example, the network might require the width to be divisible by 8), and the filter will error out in this case.

The general rule is to either:

1. left out overlap, tilesize at all and just process the input frame in one tile, or
2. set all three so that the frame is processed in tilesize[0] x tilesize[1] tiles, and adjacent tiles will have an overlap of overlap[0] x overlap[1] pixels on each direction. The overlapped region will be throw out so that only internal output pixels are used.

Instructions for TensorRT

Build engine with dynamic shape support

• Requires models with built-in dynamic shape support, e.g. waifu2x_v3.7z and dpir_v3.7z.

1. Build engine

   trtexec --onnx=drunet_gray.onnx --minShapes=input:1x2x8x8 --optShapes=input:1x2x64x64 --maxShapes=input:1x2x1080x1920 --saveEngine=dpir_gray_1080p_dynamic.engine
   

   The engine will be optimized for 64x64 input and can be applied to eligible inputs with shape from 8x8 to 1920x1080 by specifying parameter tilesize in the trt plugin.

   Also check trtexec useful arguments

Run model

In vpy script:

# DPIR
src = core.std.BlankClip(src, width=640, height=360, format=vs.GRAYS)
sigma = 10.0
flt = core.trt.Model([src, core.std.BlankClip(src, color=sigma/255.0)], engine_path="dpir_gray_1080p_dynamic.engine", tilesize=[640, 360])

trtexec useful arguments

• --workspace=N: Set workspace size in megabytes (default = 16)

• --fp16: Enable fp16 precision, in addition to fp32 (default = disabled)

• --noTF32: Disable tf32 precision (default is to enable tf32, in addition to fp32, Ampere only)

• --device=N: Select cuda device N (default = 0)

• --timingCacheFile=<file>: Save/load the serialized global timing cache

• --verbose: Use verbose logging (default = false)

• --profilingVerbosity=mode: Specify profiling verbosity.

  mode ::= layer_names_only|detailed|none
  

  (default = layer_names_only)

• --tacticSources=tactics: Specify the tactics to be used by adding (+) or removing (-) tactics from the default

  tactic sources (default = all available tactics).

  Note: Currently only cuDNN, cuBLAS and cuBLAS-LT are listed as optional tactics.

  Tactic Sources:

  tactics ::= [","tactic]
  tactic  ::= (+|-)lib
  lib     ::= "CUBLAS"|"CUBLAS_LT"|"CUDNN"
  

  For example, to disable cudnn and enable cublas: --tacticSources=-CUDNN,+CUBLAS

• --useCudaGraph: Use CUDA graph to capture engine execution and then launch inference (default = disabled). This flag may be ignored if the graph capture fails.

• --noDataTransfers: Disable DMA transfers to and from device (default = enabled).

• --saveEngine=<file>: Save the serialized engine

• --loadEngine=<file>: Load a serialized engine

Instructions for TensorRT-RTX

Replace the trtexec executable by the tensorrt_rtx executable. Some options may not be supported, e.g. --fp16.