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Using VapourSynth with super resolution and interpolation models and speeding them up with TensorRT.

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VSGAN-tensorrt-docker

Using image super resolution models with vapoursynth and speeding them up with TensorRT if possible. This repo is the fastest inference code that you can find. Not all codes can use TensorRT due to various reasons, but I try to add that if it works. Using NVIDIA/Torch-TensorRT combined with rlaphoenix/VSGAN. This repo makes the usage of tiling and ESRGAN models very easy. Models can be found on the wiki page. Further model architectures are planned to be added later on.

Table of contents

Currently working:

Model ESRGAN SRVGGNetCompact Rife SwinIR Sepconv EGVSR BasicVSR++ Waifu2x RealBasicVSR RealCUGAN FILM DPIR PAN IFRNet
CUDA - yes yes (rife40, rife41) yes yes yes yes - yes yes yes - yes yes
TensoRT yes (torch_tensorrt / C++ TRT) yes (onnx_tensorrt / C++ TRT) - - - - - yes (C++ TRT) - - - yes (C++ TRT) - -
ncnn yes (realsr ncnn models) yes (2x) yes (all nihui models) - - - - yes - - - -
onnx - yes - - - - - - - yes - - - -

Some important things:

  • Do not use webm video, webm is often broken. It can work, but don't complain about broken output afterwards.
  • Processing variable framerate (vfr) video is dangerous, but you can try to use fpsnum and fpsden. I would recommend to just render the input video into constant framerate (crf).
  • x264 can be faster than ffmpeg, use that instead.
  • ncnn does not work with docker. Docker can only support Nvidia GPUs and even if you want to run ncnn with a supported GPU inside docker, you will just get llvmpipe instead of GPU acceleration. If you want ncnn, install dependencies to your own system.
  • rife4 can use PSNR, SSIM, MS_SSIM deduplication. Quick testing showed quite some speed increase.
  • Colabs have a weak cpu, you should try x264 with --opencl. (A100 does not support NVENC and such)

Usage

# install docker, command for arch
yay -S docker nvidia-docker nvidia-container-toolkit docker-compose

# Download prebuild image from dockerhub (recommended)
docker pull styler00dollar/vsgan_tensorrt:latest
# I also created a development docker, which is more experimental. It has ncnn support.
docker pull styler00dollar/vsgan_tensorrt_dev:latest

# Build docker manually
# Put the dockerfile in a directory and run that inside that directory
# You can name it whatever you want, I just applied the same name as the dockerhub command
docker build -t styler00dollar/vsgan_tensorrt:latest .
# If you want to rebuild from scratch or have errors, try to build without cache
# If you still have problems, try to uncomment "RUN apt-get dist-upgrade -y" in the Dockerfile and try again
docker build --no-cache -t styler00dollar/vsgan_tensorrt:latest . 
# If you encounter 401 unauthorized error, use this command before running docker build
docker pull nvcr.io/nvidia/tensorrt:21.12-py3
# The dev docker has more requirements and requires that you get files manually prior to building
# Look into the docker file for more information

# run the docker with docker-compose
# go into the vsgan folder, inside that folder should be compose.yaml, run this command
# you can adjust folder mounts in the yaml file
docker-compose run --rm vsgan_tensorrt

# run docker manually
# the folderpath before ":" will be mounted in the path which follows afterwards
# contents of the vsgan folder should appear inside /workspace/tensorrt
docker run --privileged --gpus all -it --rm -v /home/vsgan_path/:/workspace/tensorrt styler00dollar/vsgan_tensorrt:latest

# you can use it in various ways, ffmpeg example
vspipe -c y4m inference.py - | ffmpeg -i pipe: example.mkv

# av1an is supported too (with svt)
# Warning: Currently frame interpolation does not properly work, but upscaling does
# Torch-TensorRT backend seems to break, C++ TRT seems to work. Either use engine or CUDA.
av1an -e svt-av1 -i inference.py -o output.mkv

If docker does not want to start, try this before you use docker:

# fixing docker errors
systemctl start docker
sudo chmod 666 /var/run/docker.sock

Windows is mostly similar, but the path needs to be changed slightly:

Example for C://path
docker run --privileged --gpus all -it --rm -v /mnt/c/path:/workspace/tensorrt vsgan_tensorrt:latest
docker run --privileged --gpus all -it --rm -v //c/path:/workspace/tensorrt vsgan_tensorrt:latest

If you are confused, here is a Youtube video showing how to use Python API based TensorRT on Windows. That's the easiest way to get my code running, but I would recommend trying to create .engine files instead. I wrote instructions for that further down below under vs-mlrt (C++ TRT). The difference in speed can be quite big. Look at benchmarks for further details.

Tutorial

There is also batch processing, just edit and use main.py (which calls inference_batch.py, edit the file if needed) instead.

python main.py

Deduplicated inference

You can delete and duplicate video frames, so you only process non-duplicated frames.

from src.dedup import return_frames
frames_duplicated, frames_duplicating = return_frames(video_path, psnr_value=60)
clip = core.std.DeleteFrames(clip, frames_duplicated)
# do upscaling here
clip = core.std.DuplicateFrames(clip, frames_duplicating)

Skipping scenes with scene detection

This avoids interpolation when a scene change happens. Create framelist with pyscenedetect and pass that.

from src.scene_detect import find_scenes
skip_framelist = find_scenes(video_path, threshold=30)
clip = RIFE(clip, multi = 2, scale = 1.0, fp16 = True, fastmode = False, ensemble = True, psnr_dedup = False, psnr_value = 70, ssim_dedup = True, ms_ssim_dedup = False, ssim_value = 0.999, skip_framelist=skip_framelist)

vs-mlrt (C++ TRT)

You need to convert onnx models into engines. You need to do that on the same system where you want to do inference. Download onnx models from here or from my Github page. You can technically just use any ONNX model you want or convert a pth into onnx with convert_esrgan_to_onnx.py. Inside the docker, you do

trtexec --fp16 --onnx=model.onnx --minShapes=input:1x3x8x8 --optShapes=input:1x3x720x1280 --maxShapes=input:1x3x1080x1920 --saveEngine=model.engine --tacticSources=+CUDNN,-CUBLAS,-CUBLAS_LT

Be aware that DPIR (color) needs 4 channels.

trtexec --fp16 --onnx=dpir_drunet_color.onnx --minShapes=input:1x4x8x8 --optShapes=input:1x4x720x1280 --maxShapes=input:1x4x1080x1920 --saveEngine=model.engine --tacticSources=+CUDNN,-CUBLAS,-CUBLAS_LT

and put that engine path into inference.py. Only do FP16 if your GPU does support it.

multi-gpu

Thanks to tepete who figured it out, there is also a way to do inference on multipe GPUs.

stream0 = core.std.SelectEvery(core.trt.Model(clip, engine_path="models/engines/model.engine", num_streams=2, device_id=0), cycle=3, offsets=0)
stream1 = core.std.SelectEvery(core.trt.Model(clip, engine_path="models/engines/model.engine", num_streams=2, device_id=1), cycle=3, offsets=1)
stream2 = core.std.SelectEvery(core.trt.Model(clip, engine_path="models/engines/model.engine", num_streams=2, device_id=2), cycle=3, offsets=2)
clip = core.std.Interleave([stream0, stream1, stream2])

ncnn

If you want to use ncnn, then you need to get the dev docker with docker pull styler00dollar/vsgan_tensorrt_dev:latest or set up your own os for this if you use AMD GPU or simply want to run it locally.

WARNING: It seems like some videos result in a broken output. For some reason a certain webm video produced very weird results, despite it working with other (non-ncnn) models. If you encounter this, just mux to a mkv with ffmpeg -i input.webm -c copy output.mkv and it should work properly again.

Instructions for Manjaro:

yay -S vapoursynth-git ffms2 ncnn

# nvidia
yay -S nvidia-utils
# amd
yay -S vulkan-radeon
or
yay -S vulkan-amdgpu-pro

If you have errors installing ncnn whl files with pip:

It seems like certain pip versions are broken and will not allow certain ncnn whl files to install properly. If you have install erorrs, either run the install with sudo or manually upgrade your pip with

wget https://bootstrap.pypa.io/get-pip.py -O ./get-pip.py
python ./get-pip.py
python3 ./get-pip.py

pip 21.0 is confirmed by myself to be broken.

Rife ncnn C++ (recommended)

I forked HomeOfVapourSynthEvolution/VapourSynth-RIFE-ncnn-Vulkan and added my own models in styler00dollar/VapourSynth-RIFE-ncnn-Vulkan. For the full experience you need to get VMAF and misc.

# VMAF
wget https://github.com/Netflix/vmaf/archive/refs/tags/v2.3.1.tar.gz && \
  tar -xzf  v2.3.1.tar.gz && cd vmaf-2.3.1/libvmaf/ && \
  meson build --buildtype release && ninja -C build && \
  ninja -C build install

git clone https://github.com/HomeOfVapourSynthEvolution/VapourSynth-VMAF && cd VapourSynth-VMAF && meson build && \
  ninja -C build && ninja -C build install

# MISC
git clone https://github.com/vapoursynth/vs-miscfilters-obsolete && cd vs-miscfilters-obsolete && meson build && \
  ninja -C build && ninja -C build install

# RIFE
git clone https://github.com/HomeOfVapourSynthEvolution/VapourSynth-RIFE-ncnn-Vulkan && cd VapourSynth-RIFE-ncnn-Vulkan && \
  git submodule update --init --recursive --depth 1 && meson build -Dbuildtype=debug -Db_lto=false && ninja -C build && ninja -C build install

Rife ncnn (Python API):

You can install precompiled whl files from here. If you want to compile it, visit styler00dollar/rife-ncnn-vulkan-python.

sudo pacman -S base-devel vulkan-headers vulkan-icd-loader vulkan-devel
pip install [URL for whl]

RealSR / ESRGAN ncnn:

You can install precompiled whl files from here. If you want to compile it, visit styler00dollar/realsr-ncnn-vulkan-python.

sudo pacman -S base-devel vulkan-headers vulkan-icd-loader vulkan-devel
pip install [URL for whl]

Any ESRGAN model will work with this (aside RealESRGAN 2x because of pixelshuffle), when you have the fitting param file. Make sure the input is called "data" and output is "output".

If you want to convert a normal pth to ncnn, you need to do pth->onnx->ncnn(bin/param). For the first step you can use torch.onnx and for the second one you can use this website.

Waifu2x ncnn:

sudo pacman -S vapoursynth glslang vulkan-icd-loader vulkan-headers

git clone https://github.com/Nlzy/vapoursynth-waifu2x-ncnn-vulkan.git
cd vapoursynth-waifu2x-ncnn-vulkan
git submodule update --init --recursive
mkdir build
cd build
cmake ..
cmake --build . -j16
sudo su
make install
exit

VFR

Warning: Using variable refresh rate video input will result in desync errors. To check if a video is do

ffmpeg -i video_Name.mp4 -vf vfrdet -f null -

and look at the final line. If it is not zero, then it means it is variable refresh rate. Example:

[Parsed_vfrdet_0 @ 0x56518fa3f380] VFR:0.400005 (15185/22777) min: 1801 max: 3604)

To go around this issue, simply convert everything to constant framerate with ffmpeg.

ffmpeg -i video_input.mkv -vsync cfr -crf 10 -c:a copy video_out.mkv

or use my vfr_to_cfr.py to process a folder.

Manual instructions

If you don't want to use docker, vapoursynth install commands are here and a TensorRT example is here.

Set the input video path in inference.py and access videos with the mounted folder.

mpv

It is also possible to directly pipe the video into mpv, but you most likely wont be able to archive realtime speed. If you use a very efficient model, it may be possible on a very good GPU. Only tested in Manjaro.

yay -S pulseaudio

# start docker with docker-compose
# same instructions as above, but delete compose.yaml and rename compose_mpv.yaml to compose.yaml 
docker-compose run --rm vsgan_tensorrt

# start docker manually
docker run --rm -i -t \
    --network host \
    -e DISPLAY \
    -v /home/vsgan_path/:/workspace/tensorrt \
    --ipc=host \
    --privileged \
    --gpus all \
    -e PULSE_COOKIE=/run/pulse/cookie \
    -v ~/.config/pulse/cookie:/run/pulse/cookie \
    -e PULSE_SERVER=unix:${XDG_RUNTIME_DIR}/pulse/native \
    -v ${XDG_RUNTIME_DIR}/pulse/native:${XDG_RUNTIME_DIR}/pulse/native \
    vsgan_tensorrt:latest
    
# run mpv
vspipe --y4m inference.py - | mpv -
# with custom audio and subtitles
vspipe --y4m inference.py - | mpv - --audio-file=file.aac --sub-files=file.ass
# to increase the buffer cache, you can use
--demuxer-max-bytes=250MiB

Color transfer (experimental)

A small script for color transfer is available. Currently it can only be used outside of VapourSynth. Since it uses color-matcher as a dependency, you need to install it first. I only tested it on a single image for now, but it may be usable for video sequences.

pip install docutils
git clone https://github.com/hahnec/color-matcher
cd color-matcher
python setup.py install

You can choose between rgb, lab, ycbcr, lum, pdf, sot, hm, reinhard, mvgd, mkl, hm-mvgd-hm and hm-mkl-hm. Specify folders.

python color_transfer.py -s input -t target -o output -algo mkl -threads 8

Benchmarks

Warnings:

  • Keep in mind that these benchmarks can get outdated very fast due to rapid code development and configurations.
  • The 3090 benches were done with a low powerlimit and throttled the GPU.
  • The default is ffmpeg.
  • ModifyFrame is depricated. Trying to use FrameEval everywhere and is used by default.
  • ncnn did a lot of performance enhancements lately, so results may be a bit better.
  • TensorRT docker version and ONNX opset seem to influence speed but that wasn't known for quite some time. I have a hard time pinpointing which TensorRT and ONNX opset was used. Take benchmark as a rough indicator.
  • Colab may change hardware like CPU at any point.
  • Sometimes it takes a very long time to reach the final speed. It can happen that not enough time was waited.
Compact (2x) 480p 720p 1080p
rx470 vs+ncnn (np+no tile+tta off) 2.7 1.6 0.6
1070ti vs+ncnn (np+no tile+tta off) 4.2 2 0.9
1070ti TensorRT8 docker 2x (ONNX-TRT+FrameEval) 12 6.1 2.8
1070ti TensorRT8 docker 2x (C++ TRT+FrameEval+num_streams=6) 14 6.7 3
3060ti TensorRT8 docker 2x (ONNX-TRT+FrameEval) 19 7.1 3.2
3060ti TensorRT8 docker 2x (C++ TRT+FrameEval+num_streams=5) 47.93 15.97 7.83
3060ti VSGAN 2x 9.7 3.6 1.77
3060ti ncnn (Windows binary) 2x 7 4.2 1.2
3060ti Joey 2x 2.24 0.87 0.36
3070 TensorRT8 docker 2x (ONNX-TRT+FrameEval) 20 7.55 3.36
3090 TensorRT8 docker 2x (ONNX-TRT+FrameEval) ? ? 6.7
3090 TensorRT8 docker 2x (C++ TRT+FrameEval+num_streams=22) 61 ? 14
V100 (Colab) (vs+CUDA) 8.4 3.8 1.6
V100 (Colab) (vs+TensorRT8+ONNX-TRT+FrameEval) 8.3 3.8 1.7
V100 (Colab High RAM) (vs+CUDA+FrameEval) 29 13 6
V100 (Colab High RAM) (vs+TensorRT7+ONNX-TRT+FrameEval) 21 12 5.5
V100 (Colab High RAM) (vs+TensorRT8.2GA+ONNX-TRT+FrameEval) 21 12 5.5
A100 (Colab) (vs+CUDA+FrameEval) 40 19 8.5
A100 (Colab) (vs+TensorRT8.2GA+ONNX-TRT+FrameEval) 44 21 9.5
A100 (Colab) (vs+TensorRT8.2GA+C++ TRT+ffmpeg+FrameEval+num_streams=50) 52.72 24.37 11.84
A100 (Colab) (vs+TensorRT8.2GA) (C++ TRT+x264 (--opencl)+FrameEval+num_streams=50) 57.16 26.25 12.42
A100 (Colab) (vs+onnx+FrameEval) 26 12 4.9
A100 (Colab) (vs+quantized onnx+FrameEval) 26 12 5.7
A100 (Colab) (jpg+CUDA) 28.2 (9 Threads) 28.2 (7 Threads) 9.96 (4 Threads)
Compact (4x) 480p 720p 1080p
1070ti TensorRT8 docker (ONNX-TensorRT+FrameEval) 11 5.6 X
3060ti TensorRT8 docker (ONNX-TensorRT+FrameEval) 16 6.1 2.7
3060ti TensorRT8 docker 2x (C++ TRT+FrameEval+num_streams=5) 29.78 11 5.24
3060ti VSGAN 4x 7.2 3 1.3
3060ti ncnn (Windows binary) 4x 3.72 0.85 0.53
3060ti Joey 4x 0.65 0.25 0.11
A100 (Colab) (vs+CUDA+FrameEval) 12 5.6 2.9
A100 (Colab) (jpg+CUDA) ? ? 3 (4 Threads)
ESRGAN 4x (64mb) 480p 720p 1080p
1070ti TensorRT8 docker (Torch-TensorRT+ffmpeg+FrameEval) 0.5 0.2 >0.1
3060ti TensorRT8 docker (Torch-TensorRT+ffmpeg+FrameEval) 2 0.7 0.29
3060ti Cupscale (Pytorch) 0.41 0.13 0.044
3060ti Cupscale (ncnn) 0.27 0.1 0.04
3060ti Joey 0.41 0.095 0.043
V100 (Colab) (Torch-TensorRT8.2GA+ffmpeg+FrameEval) 1.8 0.8 ?
V100 (Colab High VRAM) (C++ TensorRT8.2GA+x264 (--opencl)+FrameEval+no tiling) 2.46 OOM (OpenCL) OOM (OpenCL)
V100 (Colab High VRAM) (C++ TensorRT8.2GA+x264+FrameEval+no tiling) 2.49 1.14 0.47
A100 (Colab) (Torch-TensorRT8.2GA+ffmpeg+FrameEval) 5.6 2.6 1.1
RealESRGAN (2x) 480p 720p 1080p
1070ti (vs+TensorRT8+ffmpeg+C++ TRT+num_streams=1+no tiling+opset15) 0.9 0.8 0.3
3060ti (vs+TensorRT8+ffmpeg+C++ TRT+num_streams=1) 8.14 3.12 1.4
V100 (Colab High RAM / 8CPU) (vs+TensorRT8.2GA+x264 (--opencl)+C++ TRT+num_streams=3+no tiling+opset15) 5.09 4.56 2.02
V100 (Colab High RAM / 8CPU) (vs+TensorRT8.2GA+ffmpeg+C++ TRT+num_streams=3+no tiling+opset15) 5.4 4.8 2.2
RealESRGAN (4x) 480p 720p 1080p
3060ti (vs+TensorRT8+ffmpeg+C++ TRT+num_streams=2) 6.8 1.7 0.75
V100 (Colab High RAM) (vs+TensorRT8.2GA+x264 (--opencl)+C++ TRT+num_streams=1+no tiling) 6.82 3.15 OOM (OpenCL)
V100 (Colab High RAM) (vs+TensorRT8.2GA+x264+C++ TRT+num_streams=1+no tiling) ? ? 1.39
A100 (vs+TensorRT8.2GA+x264 (--opencl)+C++ TRT+num_streams=3+no tiling) 14.65 6.74 2.76
Rife4+vs (fastmode False, ensemble False) 480p 720p 1080p
1070ti (vs+ffmpeg+ModifyFrame) 61 30 15
3060ti (vs+ffmpeg+ModifyFrame) 89 45 24
Rife4+vs (fastmode False, ensemble True) 480p 720p 1080p
1070ti Python (vs+ffmpeg+ModifyFrame) 27 13 9.6
1070ti C++ NCNN ? ? 10
3060ti (vs+ffmpeg+ModifyFrame) ? 36 20
3090 (vs+ffmpeg+ModifyFrame) ? 69.6 35
V100 (Colab) (vs+ffmpeg+ModifyFrame) 30 16 7.3
V100 (Colab High RAM) (vs+x264+ModifyFrame) 48.5 33 19.2
V100 (Colab High RAM) (vs+x264+FrameEval) 48.2 35.5 20.6
V100 (Colab High RAM) (vs+x265+FrameEval) 15.2 9.7 4.6
A100 (Colab) (vs+CUDA+ffmpeg+ModifyFrame) 54 39 23
A100 (Colab) (jpg+CUDA+ffmpeg+ModifyFrame) ? ? 19.92 (14 Threads)
Rife4+vs (fastmode True, ensemble False) 480p 720p 1080p
1070ti Python (TensorRT8+ffmpeg+ModifyFrame) 62 31 14
1070ti C++ NCNN ? ? 34
3060ti (TensorRT8+ffmpeg+ModifyFrame) 135 66 33
3090 (TensorRT8+ffmpeg+ModifyFrame) ? 119 58
V100 (Colab) (TensorRT8.2GA+ffmpeg+ModifyFrame) 34 17 7.6
A100 (Colab) (TensorRT8.2GA.2GA+ffmpeg+ModifyFrame) 92 56 29
Rife4+vs (fastmode True, ensemble True) 480p 720p 1080p
1070ti (TensorRT8+ffmpeg+ModifyFrame) 41 20 9.8
1070ti C++ NCNN ? ? 17
3060ti (TensorRT8+ffmpeg+ModifyFrame) 86 49 24
3090 (TensorRT8+ffmpeg+ModifyFrame) ? 90.3 45
EGVSR 480p 720p 1080p
1070ti 3.1 OOM OOM
V100 (Colab) 2 ? ?
A100 (Colab) 5.7 2.3 1.4
RealBasicVSR 480p 720p 1080p
1070ti 0.3 OOM OOM
A100 (Colab) 1.2 ? ?
Sepconv 480p 720p 1080p
V100 (Colab) 22 11 4.9
CAIN (2 groups) 480p 720p 1080p
A100 (Colab) 76 47 25
cugan 2x 480p 720p 1080p
V100 (Colab) (vs+CUDA) 7 3.1 ?
V100 (Colab High RAM) (vs+CUDA) 21 9.7 4
cugan 4x 480p 720p 1080p
3090 26 ? ?
FILM 480p 720p 1080p
V100 (Colab High RAM) (vs+CUDA) 9.8 4.7 2.1
IFRNet (small model) 480p 720p 1080p
V100 (Colab High RAM / 8CPU) (vs+x264+FrameEval) 78 47 23
IFRNet (large model) 480p 720p 1080p
V100 (Colab High RAM / 8CPU) (vs+x264+FrameEval) ? ? 15

Combined Benchmarks

Rife4 (fastmode False, ensemble True) + Compact 2x 480p 720p 1080p
1070ti (ONNX-TensorRT8+ffmpeg+ModifyFrame) 9.3 4.6 2.2
1070ti (C++ TensorRT8+ffmpeg+ModifyFrame) ? ? 2.7
V100 (Colab High RAM) (vs+CUDA+ffmpeg+ModifyFrame) ? ? 5.1
V100 (Colab High RAM) (vs+CUDA+x264+ModifyFrame) ? ? 5.2
V100 (Colab High RAM) (vs+CUDA+x264+FrameEval) ? ? 5.1
V100 (Colab High RAM) (vs+ONNX-TensorRT8.2GA+x264+ModifyFrame) (rife fp16=False) ? ? 4.2
A100 (Colab) (vs+CUDA+ffmpeg+ModifyFrame) 23 13 6.6
A100 (Colab) (vs+ONNX-TensorRT8.2GA+ffmpeg+ModifyFrame) (rife fp16=False) 27 15 7.4
A100 (Colab) (vs+ONNX-TensorRT8.2GA+ffmpeg+ModifyFrame) (rife fp16=False) 27 15 7.4
A100 (Colab) (vs+C++ TensorRT8.2GA+ffmpeg+FrameEval) (num_streams=49) ~29 ~18 9.96
A100 (Colab) (vs+C++ TensorRT8.2GA+x264 (--opencl)+FrameEval) (num_streams=49) 30.10 19.81 10.6
Rife4 (fastmode False, ensemble True) + RealESRGAN (4x) 480p 720p 1080p
A100 (vs+TensorRT8.2GA+x264 (--opencl)+C++ TRT+num_streams=2+no tiling) 14.46 7.39 3.18
Rife4 + cugan 2x 480p 720p 1080p
A100 (vs+CUDA+ffmpeg+FrameEval) 19 10 5

License

This code uses code from other repositories, but the code I wrote myself is under BSD3.

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Using VapourSynth with super resolution and interpolation models and speeding them up with TensorRT.

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