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https://github.com/AmusementClub/vs-mlrt

Efficient CPU/GPU/Vulkan ML Runtimes for VapourSynth (with built-in support for waifu2x, DPIR, RealESRGANv2/v3, Real-CUGAN, RIFE, SCUNet and more!)
https://github.com/AmusementClub/vs-mlrt

artificial-intelligence cuda deep-learning directml dpir gpu migraphx ncnn neural-network onnx onnxruntime openvino real-cugan real-esrgan rife tensorrt vapoursynth vulkan waifu2x

Last synced: about 1 month ago
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Efficient CPU/GPU/Vulkan ML Runtimes for VapourSynth (with built-in support for waifu2x, DPIR, RealESRGANv2/v3, Real-CUGAN, RIFE, SCUNet and more!)

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README 

        
# vs-mlrt



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

 - x86 CPUs: [vsov-cpu](#vsov-openvino-based-pure-cpu--intel-gpu-runtime), [vsort-cpu](#vsort-onnx-runtime-based-cpugpu-runtime)

 - Intel GPU (both integrated & discrete): [vsov-gpu](#vsov-openvino-based-pure-cpu--intel-gpu-runtime), [vsncnn-vk](#vsncnn-ncnn-based-gpu-vulkan-runtime)

 - NVidia GPU: [vsort-cuda](#vsort-onnx-runtime-based-cpugpu-runtime), [vstrt](#vstrt-tensorrt-based-gpu-runtime), [vsncnn-vk](#vsncnn-ncnn-based-gpu-vulkan-runtime)

 - AMD GPU: [vsncnn-vk](#vsncnn-ncnn-based-gpu-vulkan-runtime)



To simplify usage, we also provide a Python wrapper [vsmlrt.py](https://github.com/AmusementClub/vs-mlrt/blob/master/scripts/vsmlrt.py)

for all bundled models and a unified interface to select different backends.



Please refer to [the wiki](https://github.com/AmusementClub/vs-mlrt/wiki) for supported models & usage information.



## vsov: OpenVINO-based Pure CPU & Intel GPU Runtime



[OpenVINO](https://docs.openvino.ai/latest/index.html) 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](vsov) directory for details.



## vsort: ONNX Runtime-based CPU/GPU Runtime



[ONNX Runtime](https://onnxruntime.ai/) 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](vsort) directory for details.



## vstrt: TensorRT-based GPU Runtime



[TensorRT](https://developer.nvidia.com/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](vsort).



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



Please visit the [vstrt](vstrt) directory for details.



## vsncnn: NCNN-based GPU (Vulkan) Runtime



[ncnn](https://github.com/Tencent/ncnn) is a popular AI inference runtime. [vsncnn](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](https://en.wikipedia.org/wiki/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](vsncnn) directory for details.