waifu2x-ncnn-vulkan VS Bringing-Old-Photos-Back-to-Life

Hi OP, try going in ubuntu then install there. Mine works fine after doing these steps: sudo apt update && upgrade sudo apt install apt-utils sudo apt install python-is-python3 git clone https://github.com/nihui/waifu2x-ncnn-vulkan.git cd waifu2x-ncnn-vulkan git submodule update --init --recursive mkdir build cd build apt install cmake apt install build-essential cmake ../src cmake --build . -j 4 ./waifu2x-ncnn-vulkan Note: i thought its stucked at 100% after cmake --build but just wait maybe 5mins and completed installing to build, all good, and... im no IT/Dev but it worked for me by following the installation guide. Now i can see these ``` Usage: waifu2x-ncnn-vulkan -i infile -o outfile [options]...

why not https://github.com/nihui/waifu2x-ncnn-vulkan ?

The other is using AI upscaling, which will allow you to upscale it while keeping a high quality look. You could try Waifu2x for that.

https://github.com/nihui/waifu2x-ncnn-vulkanis that a legist question? look it for yourself.

This is an Extension that integrates Bringing Old Photos Back to Life, an old photo restoration algorithm, into the Automatic1111 Webui, as suggested by this post. User can process a photo, then send it to img2img or Inpaint for further refinement.

Is there anyone in this subreddit that could make an Auto1111 extension out of this: https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life

This seems to do exactly the same and is the original source of the scratch detection model that is used in OPs version.

Thanks, if you are taking requests :-) - The inclusion of (or integration with) a facility like this https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life would be very nice !

Why a GAN specifically? There's Bringing Old Photos Back to Life for example, but it uses VAEs, not a GAN.

Thank you. Photo-restore + Deoldify + Animegan + Rife + 4K upscale

Bringing Old Photos to Life

I was recently looking at the microsoft/Bringing-Old-Photos-Back-to-Life repo to use on some old photo. While there is no doubt that the models were created by very smart data scientists, the repo is not very well organized and cumbersome to setup. I think data scientists coming from math/statistics background may not be familiar with software design best practices, and somehow just gets by with infrastructure setup.