Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Overview

Real-ESRGAN

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Ported from https://github.com/xinntao/Real-ESRGAN

Dependencies

  • NumPy
  • PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
  • VapourSynth

Installation

pip install --upgrade vsrealesrgan
python -m vsrealesrgan

Usage

from vsrealesrgan import RealESRGAN

ret = RealESRGAN(clip)

See __init__.py for the description of the parameters.

Comments
  • Installing on portable vapoursynth?

    Installing on portable vapoursynth?

    I'm getting this error:

    ` python -m pip install --upgrade vsrealesrgan Collecting vsrealesrgan Using cached vsrealesrgan-3.1.0-py3-none-any.whl (7.4 kB) Collecting tqdm Using cached tqdm-4.64.0-py2.py3-none-any.whl (78 kB) Requirement already satisfied: numpy in d:\vapoursynth\lib\site-packages (from vsrealesrgan) (1.22.3) Collecting VapourSynth>=55 Using cached VapourSynth-58.zip (558 kB) Preparing metadata (setup.py) ... error error: subprocess-exited-with-error

    × python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [15 lines of output] Traceback (most recent call last): File "C:\Users*\AppData\Local\Temp\pip-install-2415kpn4\vapoursynth_712c69d39f4a4718a3f6b523a85b39eb\setup.py", line 64, in dll_path = query(winreg.HKEY_LOCAL_MACHINE, REGISTRY_PATH, REGISTRY_KEY) File "C:\Users*\AppData\Local\Temp\pip-install-2415kpn4\vapoursynth_712c69d39f4a4718a3f6b523a85b39eb\setup.py", line 38, in query reg_key = winreg.OpenKey(hkey, path, 0, winreg.KEY_READ) FileNotFoundError: [WinError 2] The system cannot find the file specified

      During handling of the above exception, another exception occurred:
    
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "C:\Users\**\AppData\Local\Temp\pip-install-2415kpn4\vapoursynth_712c69d39f4a4718a3f6b523a85b39eb\setup.py", line 67, in <module>
          raise OSError("Couldn't detect vapoursynth installation path")
      OSError: Couldn't detect vapoursynth installation path
      [end of output]
    

    note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed

    × Encountered error while generating package metadata. ╰─> See above for output.

    note: This is an issue with the package mentioned above, not pip. hint: See above for details. `

    opened by manus693 8
  • 'vapoursynth.VideoFrame' object is not subscriptable

    'vapoursynth.VideoFrame' object is not subscriptable

    Error on frame 15 request: 'vapoursynth.VideoFrame' object is not subscriptable

    py3.6.4 vs.core.version: VapourSynth Video Processing Library\nCopyright (c) 2012-2018 Fredrik Mellbin\nCore R44\nAPI R3.5\nOptions: -\n torch.version: 1.10.0+cu111

    vpy: import vapoursynth as vs import sys sys.path.append("C:\C\Transcoding\VapourSynth\core64\plugins\Scripts") import mvsfunc as mvf sys.path.append(r"C:\Users\liujing\AppData\Local\Programs\Python\Python36\Lib\site-packages\vsrealesrgan") from vsrealesrgan import RealESRGAN

    core = vs.get_core(accept_lowercase=True) source = core.ffms2.Source(sourcename) source = mvf.ToRGB(source,depth=32) source = RealESRGAN(source) source= mvf.ToYUV(source,depth=16) source.set_output()

    opened by splinter21 4
  • TensorRT

    TensorRT "Ran out of input"?

    Using:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    import site
    import os
    # Adding torch dependencies to PATH
    path = site.getsitepackages()[0]+'/torch_dependencies/'
    path = path.replace('\\', '/')
    os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
    # Loading Plugins
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
    # source: 'G:\TestClips&Co\files\test.avi'
    # current color space: YUV420P8, bit depth: 8, resolution: 640x352, fps: 25, color matrix: 470bg, yuv luminance scale: limited, scanorder: progressive
    # Loading G:\TestClips&Co\files\test.avi using LWLibavSource
    clip = core.lsmas.LWLibavSource(source="G:/TestClips&Co/files/test.avi", format="YUV420P8", stream_index=0, cache=0, prefer_hw=0)
    # Setting color matrix to 470bg.
    clip = core.std.SetFrameProps(clip, _Matrix=5)
    clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
    clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
    # Setting color range to TV (limited) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
    # making sure frame rate is set to 25
    clip = core.std.AssumeFPS(clip=clip, fpsnum=25, fpsden=1)
    clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0)
    original = clip
    from vsrealesrgan import RealESRGAN
    # adjusting color space from YUV420P8 to RGBH for VsRealESRGAN
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")
    # resizing using RealESRGAN
    clip = RealESRGAN(clip=clip, device_index=0, trt=True, trt_cache_path="G:/Temp", num_streams=4) # 2560x1408
    # resizing 2560x1408 to 640x352
    # adjusting resizing
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, range_s="limited")
    clip = core.fmtc.resample(clip=clip, w=640, h=352, kernel="lanczos", interlaced=False, interlacedd=False)
    original = core.resize.Bicubic(clip=original, width=640, height=352)
    # adjusting output color from: RGBS to YUV420P8 for x264Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P8, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
    original = core.text.Text(clip=original,text="Original",scale=1,alignment=7)
    clip = core.text.Text(clip=clip,text="Filtered",scale=1,alignment=7)
    stacked = core.std.StackHorizontal([original,clip])
    # Output
    stacked.set_output()
    

    I get

    Failed to evaluate the script: Python exception: Ran out of input

    Traceback (most recent call last):
    File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
    File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
    File "C:\Users\Selur\Desktop\test_2.vpy", line 32, in 
    clip = RealESRGAN(clip=clip, device_index=0, trt=True, trt_cache_path="G:/Temp", num_streams=4) # 2560x1408
    File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
    File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsrealesrgan\__init__.py", line 284, in RealESRGAN
    module = [torch.load(trt_engine_path) for _ in range(num_streams)]
    File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsrealesrgan\__init__.py", line 284, in 
    module = [torch.load(trt_engine_path) for _ in range(num_streams)]
    File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\serialization.py", line 795, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
    File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\serialization.py", line 1002, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
    EOFError: Ran out of input
    

    Works fine with trt=False.

    ->Any idea what is going wrong there?

    opened by Selur 3
  • [REQ] SwinIR port

    [REQ] SwinIR port

    opened by forart 1
  • Vapoursynth R58 support

    Vapoursynth R58 support

    When trying to install vs-realesrgan in Vapoursynth R58 I get:

    I:\Hybrid\64bit\Vapoursynth>python -m pip install --upgrade vsrealesrgan
    Collecting vsrealesrgan
      Using cached vsrealesrgan-2.0.0-py3-none-any.whl (12 kB)
    Collecting VapourSynth>=55
      Using cached VapourSynth-57.zip (567 kB)
      Preparing metadata (setup.py) ... error
      error: subprocess-exited-with-error
    
      × python setup.py egg_info did not run successfully.
      │ exit code: 1
      ╰─> [15 lines of output]
          Traceback (most recent call last):
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-7_na63f8\vapoursynth_4864864388024a95a1e8b4adda80b293\setup.py", line 64, in <module>
              dll_path = query(winreg.HKEY_LOCAL_MACHINE, REGISTRY_PATH, REGISTRY_KEY)
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-7_na63f8\vapoursynth_4864864388024a95a1e8b4adda80b293\setup.py", line 38, in query
              reg_key = winreg.OpenKey(hkey, path, 0, winreg.KEY_READ)
          FileNotFoundError: [WinError 2] Das System kann die angegebene Datei nicht finden
    
          During handling of the above exception, another exception occurred:
    
          Traceback (most recent call last):
            File "<string>", line 2, in <module>
            File "<pip-setuptools-caller>", line 34, in <module>
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-7_na63f8\vapoursynth_4864864388024a95a1e8b4adda80b293\setup.py", line 67, in <module>
              raise OSError("Couldn't detect vapoursynth installation path")
          OSError: Couldn't detect vapoursynth installation path
          [end of output]
    
      note: This error originates from a subprocess, and is likely not a problem with pip.
    error: metadata-generation-failed
    
    × Encountered error while generating package metadata.
    ╰─> See above for output.
    
    note: This is an issue with the package mentioned above, not pip.
    hint: See above for details.
    

    any idea how to fix this?

    opened by Selur 0
  • 'vapoursynth.VideoFrame' object has no attribute 'get_read_array'

    'vapoursynth.VideoFrame' object has no attribute 'get_read_array'

    I have been trying to use this plugin, however I get the below error when trying to preview the video in VapourSynth Editor r19-mod-2-x86_64

    Error on frame 0 request: 'vapoursynth.VideoFrame' object has no attribute 'get_read_array'

    The code I am getting this error from is below

    from vapoursynth import core
    from vsrealesrgan import RealESRGAN
    import havsfunc as haf
    import vapoursynth as vs
    video = core.ffms2.Source(source='EDIT.mkv')
    video = haf.QTGMC(video, Preset="slow", MatchPreset="slow", MatchPreset2="slow", SourceMatch=3, TFF=True)
    video = core.std.SelectEvery(clip=video, cycle=2, offsets=0)
    video = core.std.Crop(clip=video, left=8, right=8, top=0, bottom=0)
    video = core.resize.Spline36(clip=video, width=640, height=480)
    video = core.resize.Bicubic(clip=video, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
    video = RealESRGAN(clip=video, device_index=0)
    video = core.resize.Bicubic(clip=video, format=vs.YUV420P10, matrix_s="470bg", range_s="limited")
    video = core.resize.Spline36(clip=video, width=1440, height=1080)
    video = core.std.AssumeFPS(clip=video, fpsnum=30000, fpsden=1001)
    video.set_output()
    
    opened by silentsudin 0
Releases(v4.0.1)
Owner
Holy Wu
Holy Wu
Shape-Adaptive Selection and Measurement for Oriented Object Detection

Source Code of AAAI22-2171 Introduction The source code includes training and inference procedures for the proposed method of the paper submitted to t

houliping 24 Nov 29, 2022
A transformer-based method for Healthcare Image Captioning in Vietnamese

vieCap4H Challenge 2021: A transformer-based method for Healthcare Image Captioning in Vietnamese This repo GitHub contains our solution for vieCap4H

Doanh B C 4 May 05, 2022
NeurIPS 2021, self-supervised 6D pose on category level

SE(3)-eSCOPE video | paper | website Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation Xiaolong Li, Yijia Weng,

Xiaolong 63 Nov 22, 2022
Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

Multi-Domain Incremental Learning for Semantic Segmentation This is the Pytorch implementation of our work "Multi-Domain Incremental Learning for Sema

Pgxo20 24 Jan 02, 2023
(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]

xshen 29 Dec 06, 2022
gACSON software for visualization, processing and analysis of three-dimensional electron microscopy images

gACSON gACSON software is to visualize, segment, and analyze the morphology of neurons in three-dimensional electron microscopy images. If you use any

Andrea Behanova 2 May 31, 2022
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

State Entropy Maximization with Random Encoders for Efficient Exploration (RE3) (ICML 2021) Code for State Entropy Maximization with Random Encoders f

Younggyo Seo 47 Nov 29, 2022
QQ Browser 2021 AI Algorithm Competition Track 1 1st Place Program

QQ Browser 2021 AI Algorithm Competition Track 1 1st Place Program

249 Jan 03, 2023
Leaderboard and Visualization for RLCard

RLCard Showdown This is the GUI support for the RLCard project and DouZero project. RLCard-Showdown provides evaluation and visualization tools to hel

Data Analytics Lab at Texas A&M University 246 Dec 26, 2022
MPViT:Multi-Path Vision Transformer for Dense Prediction

MPViT : Multi-Path Vision Transformer for Dense Prediction This repository inlcu

Youngwan Lee 272 Dec 20, 2022
KaziText is a tool for modelling common human errors.

KaziText KaziText is a tool for modelling common human errors. It estimates probabilities of individual error types (so called aspects) from grammatic

ÚFAL 3 Nov 24, 2022
Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

A Self-Supervised Descriptor for Image Copy Detection (SSCD) This is the open-source codebase for "A Self-Supervised Descriptor for Image Copy Detecti

Meta Research 68 Jan 04, 2023
Deep learning toolbox based on PyTorch for hyperspectral data classification.

Deep learning toolbox based on PyTorch for hyperspectral data classification.

Nicolas 304 Dec 28, 2022
Generate vibrant and detailed images using only text.

CLIP Guided Diffusion From RiversHaveWings. Generate vibrant and detailed images using only text. See captions and more generations in the Gallery See

Clay M. 401 Dec 28, 2022
This is the implementation of the paper "Self-supervised Outdoor Scene Relighting"

Self-supervised Outdoor Scene Relighting This is the implementation of the paper "Self-supervised Outdoor Scene Relighting". The model is implemented

Ye Yu 24 Dec 17, 2022
Hide screen when boss is approaching.

BossSensor Hide your screen when your boss is approaching. Demo The boss stands up. He is approaching. When he is approaching, the program fetches fac

Hiroki Nakayama 6.2k Jan 07, 2023
DeRF: Decomposed Radiance Fields

DeRF: Decomposed Radiance Fields Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi Links Paper Project Page Abstract

UBC Computer Vision Group 24 Dec 02, 2022
Code and models used in "MUSS Multilingual Unsupervised Sentence Simplification by Mining Paraphrases".

Multilingual Unsupervised Sentence Simplification Code and pretrained models to reproduce experiments in "MUSS: Multilingual Unsupervised Sentence Sim

Facebook Research 81 Dec 29, 2022
YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

Haotian Liu 1.1k Jan 06, 2023
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Monk - A computer vision toolkit for everyone Why use Monk Issue: Want to begin learning computer vision Solution: Start with Monk's hands-on study ro

Tessellate Imaging 507 Dec 04, 2022