Giter Club home page Giter Club logo

mafiosnik777 / enhancr Goto Github PK

View Code? Open in Web Editor NEW
512.0 14.0 27.0 356.9 MB

Video Frame Interpolation & Super Resolution using NVIDIA's TensorRT & Tencent's NCNN inference, beautifully crafted and packaged into a single app

Home Page: https://dsc.gg/enhancr

License: GNU General Public License v3.0

HTML 5.40% JavaScript 50.68% SCSS 12.66% Python 31.25%
artificial-intelligence cain dpir frame-interpolation ncnn realesrgan rife super-resolution tensorrt upscaling

enhancr's Introduction

heading-icon

enhancr is an elegant and easy to use GUI for Video Frame Interpolation and Video Upscaling which takes advantage of artificial intelligence - built using node.js and Electron. It was created to enhance the user experience for anyone interested in enhancing video footage using artificial intelligence. The GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives.

gui-preview-image

It features blazing-fast TensorRT inference by NVIDIA, which can speed up AI processes significantly. Pre-packaged, without the need to install Docker or WSL (Windows Subsystem for Linux) - and NCNN inference by Tencent which is lightweight and runs on NVIDIA, AMD and even Apple Silicon - in contrast to the mammoth of an inference PyTorch is, which only runs on NVIDIA GPUs.

Features

  • Encodes video on the fly and reads frames from source video, without the need of extracting frames or loading into memory
  • Queue for batch processing
  • Live Preview integrated in the UI, without impact on performance
  • Allows chaining of interpolation, upscaling & restoration
  • Offers the possibility to trim videos before processing
  • Can load custom ESRGAN models in onnx & pth format and converts them automatically
  • Has Scene Detection built-in, to skip interpolation on scene change frames & mitigate artifacts
  • Color Themes for user customization
  • Discord Rich Presence, to show all your friends progress, current speed & what you're currently enhancing
  • Realtime Player (assuming you have a powerful enough GPU) with perfect support for audio, subtitles, fonts, attachments etc.
  • ... and much more

Installation

Release 0.9.9 features a free version ๐ŸŽ‰ https://dl.enhancr.app/setup/enhancr-setup-free-0.9.9.exe

To ensure that you have the most recent version of the software and all necessary dependencies, we recommend downloading the installer from Patreon. Please note that builds and an embeddable python environment for the Pro version are not provided through this repository.

installer

Built-in engines

Interpolation

RIFE (NCNN) - megvii-research/ECCV2022-RIFE - powered by styler00dollar/VapourSynth-RIFE-NCNN-Vulkan

RIFE (TensorRT) - megvii-research/ECCV2022-RIFE - powered by AmusementClub/vs-mlrt & styler00dollar/VSGAN-tensorrt-docker

GMFSS - Union (PyTorch/TensorRT) - 98mxr/GMFSS_Union - powered by HolyWu/vs-gmfss_union

GMFSS - Fortuna (PyTorch/TensorRT) - 98mxr/GMFSS_Fortuna - powered by HolyWu/vs-gmfss_fortuna

CAIN (NCNN) - myungsub/CAIN - powered by mafiosnik/vsynth-cain-NCNN-vulkan (unreleased)

CAIN (DirectML) - myungsub/CAIN - powered by AmusementClub/vs-mlrt

CAIN (TensorRT) - myungsub/CAIN - powered by HubertSotnowski/cain-TensorRT

Upscaling

ShuffleCUGAN (NCNN) - styler00dollar/VSGAN-tensorrt-docker - powered by AmusementClub/vs-mlrt

ShuffleCUGAN (TensorRT) - styler00dollar/VSGAN-tensorrt-docker - powered by AmusementClub/vs-mlrt

RealESRGAN (NCNN) - xinntao/Real-ESRGAN - powered by AmusementClub/vs-mlrt

RealESRGAN (DirectML) - xinntao/Real-ESRGAN - powered by AmusementClub/vs-mlrt

RealESRGAN (TensorRT) - xinntao/Real-ESRGAN - powered by AmusementClub/vs-mlrt

RealCUGAN (TensorRT) - bilibili/ailab/Real-CUGAN - powered by AmusementClub/vs-mlrt

SwinIR (TensorRT) - JingyunLiang/SwinIR - powered by mafiosnik777/SwinIR-TensorRT (unreleased)

Restoration

DPIR (DirectML) - cszn/DPIR - powered by AmusementClub/vs-mlrt

DPIR (TensorRT) - cszn/DPIR - powered by AmusementClub/vs-mlrt

SCUNet (TensorRT) - cszn/SCUNet - powered by mafiosnik777/SCUNet-TensorRT (unreleased)

System Requirements

Minimum:

  • Dual Core CPU with Hyperthreading enabled
  • Vulkan-capable graphics processor for inference with NCNN / DirectX 12-capable graphics processor for inference with DirectML
  • Windows 10

Recommended:

  • Quad Core Intel Kaby Lake/AMD Ryzen or newer with Hyperthreading enabled
  • 16 GB RAM
  • NVIDIA 2000 Series (Ampere) for TensorRT
  • Windows 11

Sidenote: Starting with TensorRT 8.6, support for 2nd generation Kepler and Maxwell (900 Series and below) has been dropped. You will need at least a Pascal GPU (1000 series and up) and CUDA 12.0 + driver version >= 525.xx to run inference using TensorRT.

macOS and Linux Support

The GUI was created with cross-platform compatibility in mind and is compatible with both operating systems. Our primary focus at the moment is ensuring a stable and fully functioning solution for Windows users, but support for Linux and macOS will be made available with the 1.0 update.

enhancr-macos

Support for Apple Silicon is planned as well, but I currently only have an Intel Macbook Pro available for testing i'll get a Apple Silicon instance on Amazon AWS to implement this, in time for the 1.0 release.

Benchmarks

Input size: 1920x1080 @ 2x

RTX 2060S 1 RTX 3070 2 RTX A4000 3 RTX 3090 Ti 4 RTX 4090 5
RIFE / rife-v4.6 (NCNN) 53.78 fps 64.08 fps 80.56 fps 86.24 fps 136.13 fps
RIFE / rife-v4.6 (TensorRT) 70.34 fps 94.63 fps 86.47 fps 122.68 fps 170.91 fps
CAIN / cvp-v6 (NCNN) 9.42 fps 10.56 fps 13.42 fps 17.36 fps 44.87 fps
CAIN / cvp-v6 (TensorRT) 45.41 fps 63.84 fps 81.23 fps 112.87 fps 183.46 fps
GMFSS / Up (PyTorch) - - 4.32 fps - 16.35 fps
GMFSS / Union (PyTorch) - - 3.68 fps - 13.93 fps
GMFSS / Union (TensorRT) - - 6.79 fps - -
RealESRGAN / animevideov3 (TensorRT) 7.64 fps 9.10 fps 8.49 fps 18.66 fps 38.67 fps
RealCUGAN (TensorRT) - - 5.96 fps - -
SwinIR (PyTorch) - - 0.43 fps - -
DPIR / Denoise (TensorRT) 4.38 fps 6.45 fps 5.39 fps 11.64 fps 27.41 fps

1 Ryzen 5 3600X - Gainward RTX 2060 Super @ Stock

2 Ryzen 7 3800X - Gigabyte RTX 3070 Eagle OC @ Stock

3 Ryzen 5 3600X - PNY RTX A4000 @ Stock

4 i9 12900KF - ASUS RTX 3090 Ti Strix OC @ ~2220MHz

5 Ryzen 9 5950X - ASUS RTX 4090 Strix OC - @ ~3100MHz with curve to achieve maximum performance

Troubleshooting and FAQ (Frequently Asked Questions)

This section has moved to the wiki: https://github.com/mafiosnik777/enhancr/wiki

Check it out to learn more about getting the most out of enhancr or how to fix various problems.

Inferences

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 there is an extra step to build an engine on the machine you are going to run the AI on. However, the resulting performance is also typically much much better than any PyTorch or NCNN implementation.

NCNN is a high-performance neural network inference computing framework optimized for mobile platforms. NCNN does not have any third party dependencies. It is cross-platform, and runs faster than all known open source frameworks on most major platforms. It supports NVIDIA, AMD, Intel Graphics and even Apple Silicon. NCNN is currently being used in many Tencent applications, such as QQ, Qzone, WeChat, Pitu and so on.

Supporting this project

I would be grateful if you could show your support for this project by contributing on Patreon or through a donation on PayPal. Your support will help to accelerate development and bring more updates to the project. Additionally, if you have the skills, you can also contribute by opening a pull request. Regardless of the form of support you choose to give, know that it is greatly appreciated.

Plans for the future

I am continuously working to improve the codebase, including addressing any inconsistencies that may have arisen due to time constraints. Regular updates will be released, including new features, bug fixes, and the incorporation of new technologies and models as they become available. Thank you for your understanding and support.

Credits

Our player depends on mpv and ModernX for the OSC.

Thanks to HubertSontowski and styler00dollar for helping out with implementing CAIN.

Join the discord

To interact with the community, share your results or to get help when encountering any problems visit our discord. Previews of upcoming versions are gonna be showcased on there as well.

enhancr's People

Contributors

bubblemint864 avatar icedshake avatar korakoe avatar mafiosnik777 avatar woofinas avatar zeptofine avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

enhancr's Issues

Video gets flipped 90ยฐ

I am trying to upscale a vertical video (taken with a smartphone camera; 848x480p). But after the upscale (used base settings, realesrgan with 4x scale) the video is flipped by 90ยฐ and the quality is absolutly horrible.

can't get normal esrgan to work

got this error:
clip = core.trt.Model(clip, engine_path=engine, num_streams=streams) File "src\cython\vapoursynth.pyx", line 2565, in vapoursynth.Function.__call__vapoursynth.Error: operator (): no valid optimization profile found

i used a model called 2X_DigitalFilmV5_Lite.pth
btw the realesrgan models worked just fine just the esrgan one that i had this problem with
here is the log
log.txt

Integrated + External GPU not supported

I am trying to run this on a windows 11 laptop with an external nvidia gpu, though the tool keeps only recognizing the igpu of the laptops cpu, but not the external gpu. I have tried to force it to use the gpu, but it didnt work. Settings only show the igpu and upscaling something results in an error, that my gpu is unsupported (no capable devices found).
This seems to only be an issue with x265, x264 works on my igpu, though i would like to use x265 and my external gpu

vapoursynth.Error: RIFE: gpu_thread must be between 1 and 1 (inclusive)

I cannot run any interpolation, upscale, or restore models. Logs below. I have an integrated GPU and a dedicated GPU. I used this installer https://github.com/mafiosnik777/enhancr/releases/download/0.9.9/enhancr-setup-free-0.9.9.exe

โ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข enhancr โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ข
OS: Microsoft Windows 11 Home x64
CPU: Intel Coreโ„ข i5-10500H | [6C/12T]
RAM: 16173 MB
GPU: NVIDIA GeForce RTX 3060 Laptop GPU | 6144 MB
Status: Ready
โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข
[enhancr] 'XMassTestClip.mp4': Successfully added to Queue
[enhancr] Encoding parameters: -c:v h264_nvenc -preset p6 -b_adapt 1 -rc-lookahead 30 -qp 18 -qp_cb_offset -2 -qp_cr_offset -2 -pix_fmt nv12
[enhancr] Model: RIFE - v4.6
[enhancr] Starting interpolation process..

[Pipe] [0 Intel(R) UHD Graphics] queueC=0[1] queueG=0[1] queueT=0[1]
[0 Intel(R) UHD Graphics] bugsbn1=0 bugbilz=0 bugcopc=0 bugihfa=0
[0 Intel(R) UHD Graphics] fp16-p/s/a=1/1/1 int8-p/s/a=1/1/1
[0 Intel(R) UHD Graphics] subgroup=32 basic=1 vote=1 ballot=1 shuffle=1
[Pipe] [1 NVIDIA GeForce RTX 3060 Laptop GPU] queueC=2[8] queueG=0[16] queueT=1[2]
[1 NVIDIA GeForce RTX 3060 Laptop GPU] bugsbn1=0 bugbilz=0 bugcopc=0 bugihfa=0
[1 NVIDIA GeForce RTX 3060 Laptop GPU] fp16-p/s/a=1/1/1 int8-p/s/a=1/1/1
[1 NVIDIA GeForce RTX 3060 Laptop GPU] subgroup=32 basic=1 vote=1 ballot=1 shuffle=1
[Pipe] Script evaluation failed:
Python exception: RIFE: gpu_thread must be between 1 and 1 (inclusive)

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 3115, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 3116, in vapoursynth._vpy_evaluate
File "D:\Videos\enhancr\resources\inference\rife.py", line 67, in
clip = core.rife.RIFE(clip, model=model, factor_num=2, gpu_id=0, gpu_thread=threading(), tta=TTA, uhd=UHD, skip=frameskip, sc=sceneDetection)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "src\cython\vapoursynth.pyx", line 2847, in vapoursynth.Function.call
vapoursynth.Error: RIFE: gpu_thread must be between 1 and 1 (inclusive)
[Pipe]
[enhancr] An error has occured.
[enhancr] Completed queue with 1 error(s).

Which engine gives the best quality? [QUESTION]

Which engine gives the best quality? NCNN, DirectML, or TensorRT on Windows? I would also like to know which one is the fastest. Would be nice to have this information as a hover tooltip.

AV1 encoder quits halfway without reason with a completed message

Hi Team,
First of all, I am very impressed by enhance. It's really good. However, I am facing problems with its reliability. AV1 encodes more than 70% of the times do not finish. The frames encoded will encode till just over half the frames then it will end saying the encoding is complete. It is very frustrating since the CPU based AV1 encoding takes almost 90 mins to 120 mins and suddenly after an hour it will just finish abruptly. I do not face this issue if I use the AV1 NVENC hardware encoders. This is not a cache problem too since I always maintained 100 GB of free space.

This is the settings what I use:
-c:v libsvtav1 -qp 50 -preset 7 -svtav1-params tune=0:enable-tf=0:enable-overlays=1:enable-qm=1 -pix_fmt yuv420p10le

Note: qp is 50 since its Anime and I don't see much quality degradation.

Regards,
Neo

Need internet connection to use app?

If I join the patreon to use the app, does it need to be connected to the internet every time I use it? If I cancel the patreon subscription can I still use the last version of the app that I have installed? Or am I paying $120 a year to use the app?

Support Real-ESRGAN x2 Plus

I've run a bunch of tests on upscaling SD movies (VHS and DVD) and found that using that using Real-ESRGAN x2 Plus (RealESRGAN_x2plus.pth) is offering much better quality than the included RealESRGAN encoder, which tends to smooth out details so people look like they're made of plastic.

Unfortunately, using x2 Plus as custom model has a severe impact on the transcoding speed (going from approx 60-110fps for RealESRGAN TensorRT 2x to around 13-24fps for the custom model converted to TensorRT engine).

Is there any way to speed this up, or is there any way you could implement a fast x2 (or x4) upscaler that is much better for realistic movies (as opposed to anime content)?

Restore doesn't work

-Load video
-Add to que
-Click enhance
The cue is immediately completed, nothing is processed and the interface is now unusable and stuck in an infinite loading glitch.

All settings are stock standard.
Project source and destination in Documents folder.
Latest installer on github from releases.
Test with multiple input videos and formats.
Windows 11, everything up to date.

image

Unable to select preferred GPU

On Asus ZenBook 14x with dual Intel Iris Xe integrated graphics and Nvidia RTX 3050 dedicated GPU, enhancr does not detect the RTX 3050 and shows the Intel Iris Xe graphics as the selected GPU in the settings information panel as follows:

โ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข enhancr โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ข
OS: Microsoft Windows 11 Home x64
CPU: Intel Gen Intelยฎ Coreโ„ข i9-13900H | [14C/20T]
RAM: 32372 MB
GPU: Intel(R) Iris(R) Xe Graphics | 1024 MB
Status: Ready
โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข

I have also confirmed that there is no activity on the RTX 3050 GPU when processing videos in enhancr. All hardware decoding/encoding is limited to the Intel Iris Xe integrated GPU, which is obviously very slow when compared to the RTX 3050.

There doesn't appear to be any way to change the GPU selection in enhancer settings, so it would be very helpful if an option could be added to select the preferred GPU.

Add Support for Other Hardware Encoders and Formats.

Due to the massive speedups from Tensor RT it is possible for both CAIN and RIFE to both be bottle necked by transcoding speed instead of the interpolation itself. To Avoid this there is already support for NVENC. However enhancr also supports AMD gpus and soon apple silicon which both don't have NVENC. It would be nice for people on AMD and apple silicon to have hardware encode support to avoid transcoding and cpu bottlenecks.

Currently enhancr only supports HEVC with NVENC as well. It would be nice to support more formats like the older h.264 for compatibility reasons as well as newer formats like AV1 who are file size concerned.

Error with video

Im getting the following error show below when trying to upscale a video:

[enhancr] Preparing media for upscaling process..

[enhancr] Encoding parameters: -c:v libx264 -crf 16 -preset slow -x264-params direct=spatial:me=umh -pix_fmt yuv420p

[enhancr] Scale: 2x

[enhancr] Mode: Upscaling - RealESRGAN (DirectML)

[enhancr] Starting upscaling process..


[Pipe] Starting video output | Threads: 8 | Streams: 2

[Pipe] Script evaluation done in 2.02 seconds
[Pipe] 2023-06-13 18:49:43.3519796 [E:onnxruntime:, sequential_executor.cc:514 onnxruntime::ExecuteKernel] Non-zero status code returned

[Pipe] while running DmlFusedNode_0_0 node. Name:'DmlFusedNode_0_0' Status Message: D:\a\onnxruntime\onnxruntime\onnxruntime\core\providers\dml\DmlExecutionProvider\src\FusedGraphKernel.cpp(365)\onnxruntime.dll!00007FFA8BA1E00A: (caller: 00007FFA8BA1C7FC) Exception(2) tid(5780) 8007000E Not enough memory resources are available to complete this operation.
[Pipe] Error: Failed to retrieve frame 1 with error: operator (): 'ortapi->RunWithBinding(resource.session, nullptr, resource.binding)' failed: Non-zero status code returned while running DmlFusedNode_0_0 node. Name:'DmlFusedNode_0_0' Status Message: D:\a\onnxruntime\onnxruntime\onnxruntime\core\providers\dml\DmlExecutionProvider\src\FusedGraphKernel.cpp(365)\onnxruntime.dll!00007FFA8BA1E00A: (caller: 00007FFA8BA1C7FC) Exception(2) tid(5780) 8007000E Not enough memory resources are available to complete this operation.

[Pipe] Output 8 frames in 16.06 seconds (0.50 fps)
[Pipe] 

[enhancr] Finishing up upscaling..

[enhancr] Muxing in streams..

[Pipe] [matroska,webm @ 00000264e9d5af40] Duplicate element

[matroska,webm @ 00000264e9d5af40] 0x00 at pos 110 (0x6e) invalid as first byte of an EBML number

[matroska,webm @ 00000264e9d5af40] Duplicate element

[matroska,webm @ 00000264e9d5af40] 0x00 at pos 202 (0xca) invalid as first byte of an EBML number

[matroska,webm @ 00000264e9d5af40] Element at 0x67 ending at 0x7cec0100000070 exceeds containing master element ending at 0x1413

C:\Users\KKart\AppData\Local\Temp\enhancr\1686707366850.mkv: End of file

[enhancr] Completed upscaling

[enhancr] Completed queue with 0 error(s). // Not true

This happens to a specific video and I've tried reencoding it still doesn't work.

Selecting an external model crashes the encoder

I'm trying to use external models to upscale my video, specifically 2x VHS upscale and denoise Film and 4x UniScale Balanced.

Settings: x265, "Upscaling - RealESRGAN (DirectML)", 2x

Whether I use pth or onnx files, the result is always:

[enhancr] Preparing media for upscaling process..
[enhancr] Using custom model: C:\Users\AMD\AppData\Roaming\.enhancr\models\RealESRGAN\4x-UniScale-Balanced [72000g].onnx
[enhancr] Encoding parameters: -c:v hevc_nvenc -preset p6 -b_adapt 1 -rc-lookahead 30 -qp 23 -qp_cb_offset -2 -qp_cr_offset -2 -pix_fmt nv12
[enhancr] Scale: 2x
[enhancr] Mode: Upscaling - RealESRGAN (DirectML)
[enhancr] Starting upscaling process..

[Pipe] Starting video output | Threads: 16 | Streams: 2
[Pipe] Frame: 1/43230
[Pipe] Error: Failed to retrieve frame 13 with error: operator (): 'ortapi->Run( resource.session, nullptr, &resource.input_name, &resource.input_tensor, 1, &resource.output_name, 1, &resource.output_tensor )' failed: D:\a\onnxruntime\onnxruntime\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ExecutionProvider.cpp(898)\onnxruntime.dll!00007FF9E82E45B4: (caller: 00007FF9E82E4C0F) Exception(2) tid(13a0) 887A0006 The GPU will not respond to more commands, most likely because of an invalid command passed by the calling application.

Any suggestions?

"nvenc No capable devices found" with software encoding @2x720p RealESRGAN (NCNN)

I can run neither upscaling nor restoration with RealESRGAN (NCNN) on anything 720p and above.
Hardware encoding is turned off in settings. 480*360 worked fine with 3x, 720p and above fails with 2x.
ShuffleCUGAN (NCNN) also works fine.

`โ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข enhancr โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ข
OS: Microsoft Windows 10 Pro x64
CPU: AMD Ryzen 7 5800X3D 8-Core Processor | [8C/16T]
RAM: 32694 MB
GPU: NVIDIA GeForce RTX 3080 | 10240 MB
Status: Ready
โ€ขยฐโ€ขโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ€ขยฐโ€ข
[enhancr] 'vid_720p.mp4': Successfully added to Queue
[enhancr] Preparing media for upscaling process..
[enhancr] Using custom model: C:\Users\xxxxxx\AppData\Roaming.enhancr\models\RealESRGAN
[enhancr] Encoding parameters: -c:v libx264 -crf 16 -preset slow -x264-params direct=spatial:me=umh -pix_fmt yuv420p
[enhancr] Scale: 2x
[enhancr] Mode: Upscaling - RealESRGAN (NCNN)
[enhancr] Starting upscaling process..

[Pipe] [0 NVIDIA GeForce RTX 3080] queueC=2[8] queueG=0[16] queueT=1[2]
[0 NVIDIA GeForce RTX 3080] bugsbn1=0 bugbilz=0 bugcopc=0 bugihfa=0
[0 NVIDIA GeForce RTX 3080] fp16-p/s/a=1/1/1 int8-p/s/a=1/1/1
[0 NVIDIA GeForce RTX 3080] subgroup=32 basic=1 vote=1 ballot=1 shuffle=1
[Pipe] Starting video output | Threads: 8 | Streams: 2
[Pipe] Script evaluation done in 1.22 seconds
[Pipe] [h264_nvenc @ 00000250f3d8cb00] No capable devices found
[Pipe] Error: fwrite() call failed when writing frame: 3, plane: 0, errno: 32
Output 12 frames in 0.95 seconds (12.68 fps)
[Pipe] [enhancr] Finishing up upscaling..
[enhancr] Muxing in streams..
[Pipe] [matroska,webm @ 00000205eda8f900] EBML header parsing failed
[Pipe] D:\temp\enhancr\1684753613579.mkv: Invalid data found when processing input
[enhancr] Completed upscaling
[enhancr] Completed processing queue successfully.`

`[enhancr] 'vid_720p.mp4': Successfully added to Queue
[enhancr] Preparing media for upscaling process..
[enhancr] Using custom model: C:\Users\xxxxxx\AppData\Roaming.enhancr\models\RealESRGAN
[enhancr] Encoding parameters: -c:v ffv1 -coder 2 -context 1 -level 3 -slices 12 -g 1
[enhancr] Scale: 2x
[enhancr] Mode: Upscaling - RealESRGAN (NCNN)
[enhancr] Starting upscaling process..

[Pipe] [0 NVIDIA GeForce RTX 3080] queueC=2[8] queueG=0[16] queueT=1[2]
[0 NVIDIA GeForce RTX 3080] bugsbn1=0 bugbilz=0 bugcopc=0 bugihfa=0
[0 NVIDIA GeForce RTX 3080] fp16-p/s/a=1/1/1 int8-p/s/a=1/1/1
[0 NVIDIA GeForce RTX 3080] subgroup=32 basic=1 vote=1 ballot=1 shuffle=1
[Pipe] Starting video output | Threads: 8 | Streams: 2
[Pipe] Frame: 1/85050
[Pipe] [h264_nvenc @ 0000019c1dad8c80] No capable devices found
[Pipe] Error: fwrite() call failed when writing frame: 3, plane: 0, errno: 32
Output 13 frames in 1.02 seconds (12.79 fps)
[Pipe] [enhancr] Finishing up upscaling..
[enhancr] Muxing in streams..
[Pipe] [matroska,webm @ 0000027f0569f900] EBML header parsing failed
D:\temp\enhancr\1684753681678.mkv: Invalid data found when processing input
[enhancr] Completed upscaling
[enhancr] Completed processing queue successfully.
`

Offline Installer

Due to the unstable nature of my internet connection, I am unable to complete the installation of the application through the provided installer, as it does not offer a retry or resume functionality similar to that of a web browser or download manager. Consequently, I am seeking an offline installer. Additionally, if there are direct links to the primary files, I would be able download them via IDM or web browser.
Thank you in advance

missing colorspace parameter

the input video is mp4 hvc1

`[enhancr] Preparing media for restoration process..
[enhancr] Encoding parameters: -c:v h264_nvenc -preset p6 -b_adapt 1 -rc-lookahead 30 -qp 18 -qp_cb_offset -2 -qp_cr_offset -2 -pix_fmt nv12
[enhancr] Mode: Restoration - DPIR (DirectML)
[enhancr] Starting restoration process..

[Pipe] Starting video output | Threads: 8 | Streams: 2
[Pipe] Script evaluation done in 1.62 seconds
[Pipe] Error: Failed to retrieve frame 0 with error: Resize error: Resize error 3074: unrecognized transfer characteristics (0/0/0 => 0/0/0). May need to specify additional colorspace parameters.
Output 8 frames in 0.02 seconds (343.94 fps)
[Pipe]
[enhancr] Finishing up restoration..
[enhancr] Muxing in streams..
[enhancr] Muxing in streams..
[enhancr] Completed restoring
[enhancr] Completed queue with 0 error(s).`

Request for inclusion of basicVSR++ model

Hi,

I hope this issue finds you well. I am a user of this repository and I have noticed that the basicVSR++ model is currently not included.

I would like to request that the basicVSR++ model be added to the repository. It has excellent video super-resolution performance.

Thank you for your time and consideration.

Best regards

Error with Restore

I installed the software and I get this error

[Pipe] [NULL @ 00000226c616dc00] Unable to find a suitable output format for '='
=: Invalid argument
[Pipe] Error: fwrite() call failed when writing frame: 1, plane: 0, errno: 32
Output 13 frames in 1.54 seconds (8.46 fps)
[Pipe] [enhancr] Finishing up restoration..
[enhancr] Muxing in streams..
[enhancr] Muxing in streams..
[Pipe] [matroska,webm @ 000002e39978ecc0] EBML header parsing failed
C:\Users\i5\AppData\Local\Temp\enhancr\1685654715695.mkv: Invalid data found when processing input
[enhancr] Completed restoring
[enhancr] Completed processing queue successfully.

Hardware accelerated decoding

Was wondering if the option to pass --hwaccel to ffmpeg could be added. For some of the models I tend to run into a CPU bottleneck before CUDA.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.