Half Instance Normalization Network for Image Restoration

Related tags

Deep Learning pytorch hinet vapoursynth
Overview

HINet

Half Instance Normalization Network for Image Restoration, based on https://github.com/megvii-model/HINet.

Dependencies

  • NumPy
  • PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
  • VapourSynth
  • (Optional) TensorRT. Note that uff and PyCUDA are not required and hence can be skipped from the guide.
  • (Optional) torch2trt

Installation

pip install --upgrade vshinet
python -m vshinet

Usage

from vshinet import HINet

ret = HINet(clip)

See __init__.py for the description of the parameters.

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Comments
  • mode 1 tiling issue

    mode 1 tiling issue

    using 'HINet(clip=clip, mode=1, device_index=0, tile_x=424, tile_y=480, fp16=True)' I see green artifact artifacts uploaded the full script and the source file to my GoogleDrive

    Okay, correction, this does also happen when tiling is deactivated, so this seems to be a bug in mode=1. Using clip = HINet(clip=clip, mode=1, device_type="cpu") does not show theses artifacts. (using cpu instead of gpu)

    opened by Selur 2
  • [REQ] Restormer port

    [REQ] Restormer port

    opened by AIisCool 1
  • [REQ] Investigating Tradeoffs in Real-World Video Super-Resolution code available !

    [REQ] Investigating Tradeoffs in Real-World Video Super-Resolution code available !

    RealBasicVSR by @ckkelvinchan has just been released:

    https://user-images.githubusercontent.com/7676947/143370499-9fe4069b-46cc-4f12-b6ff-5595e8e5e0b8.mp4

    https://user-images.githubusercontent.com/7676947/143370350-91f751f3-0f33-4ee4-9b1a-b9279bf41c18.mp4

    https://user-images.githubusercontent.com/7676947/143370556-9e7019d4-e718-46af-859f-54d5576cd370.mp4

    https://user-images.githubusercontent.com/7676947/143370859-e0293b97-f962-476f-acf8-14fad27cea77.mp4

    (videos have been compressed. Therefore, the results are inferior to that of the actual outputs)

    opened by forart 0
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Holy Wu
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