vstrt 需先在目标机器上从 ONNX 构建 TensorRT engine,部署比其它后端麻烦;vsort 和 vstrt 的 CUDA 后端依赖超过 1GB 的 CUDA 库;vsncnn 虽然兼容性最好但速度较慢。
vs-mlrt
AmusementClubGPL-3.0活跃维护为 VapourSynth 视频处理框架提供 CPU/GPU 高效推理运行时,内置 waifu2x、Real-ESRGAN、RIFE、Real-CUGAN 等超分、降噪、插帧模型,方便在视频后期流程里直接调用。
Efficient CPU/GPU ML Runtimes for VapourSynth (with built-in support for waifu2x, DPIR, RealESRGANv2/v3, Real-CUGAN, RIFE, SCUNet, ArtCNN and more!)
下载最新 release 并解压到 VapourSynth 的 plugins 目录CPU 走 vsov-cpu、vsort-cpu;Intel GPU 需 Gen 8+(Broadwell 及以后)或 Arc 系列;NVidia 可用 vsort-cuda、vstrt、vsncnn-vk;AMD 用 vsncnn-vk 或 vsmigx;Apple SoC 用 vsort-coreml。README 未给出具体显存要求。
给 VapourSynth 提供多后端 ML 滤镜运行时,让超分、降噪、插帧等模型在 CPU 或各厂商 GPU 上跑起来
为 VapourSynth 视频处理框架提供 CPU/GPU 高效推理运行时,内置 waifu2x、Real-ESRGAN、RIFE、Real-CUGAN 等超分、降噪、插帧模型,方便在视频后期流程里直接调用。
核心能力
- 统一 Python 封装 vsmlrt.py,一套接口切换后端
- 覆盖 OpenVINO、ONNX Runtime、TensorRT、MIGraphX、ncnn 多种运行时
- 内置 waifu2x、DPIR、RealESRGAN、Real-CUGAN、RIFE、SCUNet 等模型支持
- vsncnn 走 Vulkan,兼容 NVidia、AMD、Intel 核显与独显
- TensorRT 按本机 GPU 实测选最优 kernel,性能优于 CUDA 后端
容易踩的坑
- v16.4.test1pre-release最新正式版2026-10-30 · 2026-10-30 00:30 UTC
This is an experimental release for TensorRT 11.4.0 and TensorRT-RTX 1.7.
TRT
- Upgraded to TensorRT 11.4.0.
TRT-RTX
- Upgraded to TensorRT-RTX 1.7.
Full Changelog: https://github.com/AmusementClub/vs-mlrt/compare/v16.3.test1...v16.4.test1
- v16.3.test1pre-release2026-09-23 · 2026-09-23 04:59 UTC
查看更新说明
This is an experimental release for TensorRT 11.3.
TRT
- Upgraded to TensorRT 11.3.
General
- Upgraded to CUDA 13.4.2.
Full Changelog: https://github.com/AmusementClub/vs-mlrt/compare/v16.2.test1...v16.3.test1
- v16.2.test1pre-release2026-08-05 · 2026-08-05 09:08 UTC
查看更新说明
This is an experimental release for TensorRT 11.2 and TensorRT-RTX 1.6.
TRT
- Upgraded to TensorRT 11.2.
TRT-RTX
- Fixed fp16 I/O (https://github.com/AmusementClub/vs-mlrt/issues/167)
- Upgraded to TensorRT-RTX 1.6.
General
- Upgraded to CUDA 13.3.1.
Full Changelog: https://github.com/AmusementClub/vs-mlrt/compare/v16.1.test1...v16.2.test1
- v16.1.test1pre-release2026-06-25 · 2026-06-25 02:51 UTC
查看更新说明
This is an experimental release for TensorRT 11.1.
TRT
- Upgraded to TensorRT 11.1.
General
- Upgraded to CUDA 13.3.0.
Full Changelog: https://github.com/AmusementClub/vs-mlrt/compare/v16.test1...v16.1.test1
- v16.test1pre-release2026-06-06 · 2026-06-06 05:22 UTC
查看更新说明
This is an experimental release for TensorRT 11.0.
vsmlrt.py
- Breaking: For the
TRTbackend, fp16 inference of built-in models requires eitheronnxconverter-commonornvidia-modeloptPython packages to be installed. bf16 inference is not supported yet.
TRT
- Upgraded to TensorRT 11.0.
TRT-RTX
- Upgraded to TensorRT-RTX 1.5.
General
- Upgraded to CUDA 13.2.1.
Full Changelog: https://github.com/AmusementClub/vs-mlrt/compare/v15.16...v16.test1
- Breaking: For the
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vs-mlrt
This project provides VapourSynth ML filter runtimes for a variety of platforms:
- x86 CPUs: vsov-cpu, vsort-cpu
- Intel GPU (both integrated & discrete): vsov-gpu, vsncnn-vk
- NVidia GPU: vsort-cuda, vstrt & vstrt_rtx, vsncnn-vk
- AMD GPU: vsncnn-vk, vsmigx
- Apple SoC: vsort-coreml
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.