BasicSR
Real-ESRGAN-colab
| BasicSR | Real-ESRGAN-colab | |
|---|---|---|
| 5 | 1 | |
| 6,198 | 44 | |
| 2.3% | - | |
| 0.0 | 0.0 | |
| 6 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | - |
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BasicSR
- About Open Source Image and Video Restoration Toolbox
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Super-Resolution Generative Adversarial Networks (SRGAN)
I think you might be interested in https://github.com/XPixelGroup/BasicSR
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Just saw a demo of nvidias super resolution. Is the software already available to the end user and can one upsize ones old „family“ photos to something astonishingly crisp and detailed for prints?
It's not the same but GFPGan works quite well, you might want to check out BasicSR. The Remini mobile app is impressive too.
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Anyone currently able to run Real-ESRGAN notebooks on colab currently?
!pip install git+https://github.com/xinntao/BasicSR.git
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Training ESRGAN: Seemingly impossible
So, I was using a pretty high-end machine with an AMD RX 6900 XT GPU, ready to do some GPU accelerated computing. I had booted into Windows 10. I followed a guide for training on my own dataset. PyTorch was the name of the framework that served as the foundation for [BasicSR[(https://github.com/xinntao/BasicSR), which in turn provided the tools I needed.
Real-ESRGAN-colab
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Retrained Real-ESRGAN version used by ruDALL-E (a text-to-image AI) may be of interest
I noticed that the 256x256 -> 1024x1024 upscalings used by the ruDALL-E (text-to-image AI) demo site looked more detailed than other upscalers I've used. It apparently uses a retrained Real-ESRGAN. Here is a comparison I did of 2 synthetic images upscaled with this and 2 other upscalers. The GitHub repos are here and here. A web app version is here.
What are some alternatives?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
traiNNer - traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
EGVSR - Efficient & Generic Video Super-Resolution
Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!
Image-Super-Resolution-via-Iterative-Refinement - Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch