VapourSynth-MLRT-NCNN

This package contains the NCNN backend implementation of the vs-mlrt plugin.

Installation

pip install vapoursynth-mlrt-ncnn

Building from source

Requirements

  • C++ Compiler: C++20 compatible (e.g. MSVC 2019+, GCC, Clang)
  • Dependencies:
    • ncnn (Vulkan support enabled)
    • Protobuf
    • ONNX
    • Vulkan SDK
  • Platforms: Windows, Linux, macOS (uses Metal on macOS)

Compilation

Ensure all required dependencies (ONNX, NCNN, Vulkan SDK, Protobuf) are discoverable by CMake (e.g., via CMAKE_PREFIX_PATH or system package manager).

uv build --package vapoursynth-mlrt-ncnn

Detailed parameter information from the parent project follows.

vs-mlrt

This project provides VapourSynth ML filter runtimes for a variety of platforms:

To simplify usage, we also provide a Python wrapper vsmlrt.py for all bundled models and a unified interface to select different backends.

Please refer to the wiki for supported models & usage information.

vsov: OpenVINO-based Pure CPU & Intel GPU Runtime

OpenVINO is an AI inference runtime developed by Intel, mainly targeting x86 CPUs and Intel GPUs.

The vs-openvino plugin provides optimized pure CPU & Intel GPU runtime for some popular AI filters. Intel GPU supports Gen 8+ on Broadwell+ and the Arc series GPUs.

To install, download the latest release and extract them into your VS plugins directory.

Please visit the vsov directory for details.

vsort: ONNX Runtime-based CPU/GPU Runtime

ONNX Runtime is an AI inference runtime with many backends.

The vs-onnxruntime plugin provides optimized CPU and CUDA GPU runtime for some popular AI filters.

To install, download the latest release and extract them into your VS plugins directory.

Please visit the vsort directory for details.

vstrt: TensorRT-based GPU Runtime

TensorRT is a highly optimized AI inference runtime for NVidia GPUs. It uses benchmarking to find the optimal kernel to use for your specific GPU, and so there is an extra step to build an engine from ONNX network on the machine you are going to use the vstrt filter, and this extra step makes deploying models a little harder than the other runtimes. However, the resulting performance is also typically much much better than the CUDA backend of vsort.

TensorRT-RTX is a specialization of TensorRT for NVIDIA RTX GPUs, which compiles engines faster with comparable performance with TensorRT.

To install, download the latest release and extract them into your VS plugins directory.

Please visit the vstrt directory for details.

vsmigx: MIGraphX-based GPU Runtime

MIGraphX is a highly optimized AI inference runtime for AMD GPUs. It also uses benchmarking to find the optimal kernel, similar to vstrt.

To install, download the latest release and extract them into your VS plugins directory.

Please visit the vsmigx directory for details.

vsncnn: NCNN-based GPU (Vulkan) Runtime

ncnn is a popular AI inference runtime. vsncnn provides a vulkan based runtime for some AI filters. It includes support for on-the-fly ONNX to ncnn native format conversion so as to provide a unified interface across all runtimes provided by this project. As it uses the device-independent Vulkan interface for GPU accelerated inference, this plugin supports all GPUs that provides Vulkan interface (NVidia, AMD, Intel integrated & discrete GPUs all provide this interface.) Another benefit is that it has a significant smaller footprint than other GPU runtimes (both vsort and vstrt CUDA backends require >1GB CUDA libraries.) The main drawback is that it's slower.

To install, download the latest release and extract them into your VS plugins directory.

Please visit the vsncnn directory for details.

Project links

Data verified by PyPI on Aug 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Ichunjo from gravatar.com Ichunjo

Credits

Author: AmusementClub

Maintainer: Vardë

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

GPL-3.0-or-later
View SPDX License List

Requires

Python >=3.12

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vapoursynth_mlrt_ncnn-16.0.tar.gz (674.8 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

vapoursynth_mlrt_ncnn-16.0-py3-none-win_amd64.whl (5.8 MB view details)

Uploaded Python 3Windows x86-64

vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_35_x86_64.whl (9.1 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ x86-64

vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_34_aarch64.whl (5.9 MB view details)

Uploaded Python 3manylinux: glibc 2.34+ ARM64

vapoursynth_mlrt_ncnn-16.0-py3-none-macosx_15_0_arm64.whl (4.6 MB view details)

Uploaded Python 3macOS 15.0+ ARM64

File details

Details for the file vapoursynth_mlrt_ncnn-16.0.tar.gz.

File metadata

  • Download URL: vapoursynth_mlrt_ncnn-16.0.tar.gz
  • Upload date:
  • Size: 674.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for vapoursynth_mlrt_ncnn-16.0.tar.gz
Algorithm Hash digest
SHA256 493d4590fda512c7efacba90a7ffd3dafbc49f9a4bd117d370d4d846df90e333
MD5 e63d6af05b15d2a3e88bda57f2b4cc72
BLAKE2b-256 cf8d5313444e3124796fc42055808d0cc162a402dff02f17060422cd1f7cf165

See more details on using hashes here.

Provenance

The following attestation bundles were made for vapoursynth_mlrt_ncnn-16.0.tar.gz:

Publisher: cd-publish.yml on Jaded-Encoding-Thaumaturgy/vs-wheels

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vapoursynth_mlrt_ncnn-16.0-py3-none-win_amd64.whl.

File metadata

File hashes

Hashes for vapoursynth_mlrt_ncnn-16.0-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 ee7647295d213204b2d60b277afa7b9e0e8adbdcefeab0564054663e09e24271
MD5 717b480a25ef708ff89fecb8a81571ab
BLAKE2b-256 b45a18134df1a6c1da71f97c63106ce60e3f9fdb400de2f4d07b74434276bd0f

See more details on using hashes here.

Provenance

The following attestation bundles were made for vapoursynth_mlrt_ncnn-16.0-py3-none-win_amd64.whl:

Publisher: cd-publish.yml on Jaded-Encoding-Thaumaturgy/vs-wheels

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 5962dbfc285136c20098aa87968a8f41508b146030ed9231712215c600a5c065
MD5 b8a39d96ba5cf9c9f5f6e4215a3355a2
BLAKE2b-256 76e56380960cc3e83015c3f4d50ee47454faf679d835c477ec695ba18f5a410d

See more details on using hashes here.

Provenance

The following attestation bundles were made for vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_35_x86_64.whl:

Publisher: cd-publish.yml on Jaded-Encoding-Thaumaturgy/vs-wheels

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_34_aarch64.whl.

File metadata

File hashes

Hashes for vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_34_aarch64.whl
Algorithm Hash digest
SHA256 b3bddee3e8b3f13d8c60b2f2cf80a453193432c8abdec7611508a9d9a95ec83e
MD5 aac4189e7fef185a5e5145cb5fa1cdaa
BLAKE2b-256 cbccbcbf044650a00f7c69ad5bb89bee3273a2c75f41c3faac4818c8834ac09c

See more details on using hashes here.

Provenance

The following attestation bundles were made for vapoursynth_mlrt_ncnn-16.0-py3-none-manylinux_2_34_aarch64.whl:

Publisher: cd-publish.yml on Jaded-Encoding-Thaumaturgy/vs-wheels

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vapoursynth_mlrt_ncnn-16.0-py3-none-macosx_15_0_arm64.whl.

File metadata

File hashes

Hashes for vapoursynth_mlrt_ncnn-16.0-py3-none-macosx_15_0_arm64.whl
Algorithm Hash digest
SHA256 3f430ee00e485fc61ec60162ea2b03895cd2f63e2a737d6528636842e7e46433
MD5 b5ad912e4b1215252bee0437e05b31f5
BLAKE2b-256 4bdd47e4b5a31649b73babdbbf7a6f60edbe5af1ddcb5527b4380327e5e11997

See more details on using hashes here.

Provenance

The following attestation bundles were made for vapoursynth_mlrt_ncnn-16.0-py3-none-macosx_15_0_arm64.whl:

Publisher: cd-publish.yml on Jaded-Encoding-Thaumaturgy/vs-wheels

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

16.0 This release

5 files

15.16

6 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page