Real-ESRGAN-colab
Image-Super-Resolution-via-Iterative-Refinement
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| Real-ESRGAN-colab | Image-Super-Resolution-via-Iterative-Refinement | |
|---|---|---|
| 1 | 5 | |
| 44 | 3,355 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | 6 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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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.
Image-Super-Resolution-via-Iterative-Refinement
- I know nothing about coding - could someone help me get something running?
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New Super Resolution method
Github link
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Google’s New AI Photo Upscaling Tech Is Jaw-Dropping
Here's an unofficial copy of the code: https://github.com/Janspiry/Image-Super-Resolution-via-Itera...
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SR3: Iterative Image Enhancement
https://github.com/Janspiry/Image-Super-Resolution-via-Itera...
Imagine a game using this tech, you could render a game in a lower resolution and possible get a better looking game. But then again they aren't yet dealing with temporal data.
In the previous discussion https://news.ycombinator.com/item?id=27858893 they mentioned that it was only class conditional but it also seems to work on unconditional data.
- I have super limited coding experience but have a question about this github link.
What are some alternatives?
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
BasicSR - Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
Image-Super-Resolution-via-Itera
traiNNer - traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
EGVSR - Efficient & Generic Video Super-Resolution
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.
Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!