Image-Super-Resolution-via-Iterative-Refinement
Real-ESRGAN
| Image-Super-Resolution-via-Iterative-Refinement | Real-ESRGAN | |
|---|---|---|
| 5 | 3 | |
| 3,377 | 399 | |
| - | 2.5% | |
| 0.0 | 0.0 | |
| 6 months ago | 22 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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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.
Real-ESRGAN
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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.
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"a dog in a sweater. no watermark." (4 images) generated by CogView 2. ~256x256 cropped screenshot upscaled 4x with Real-ESRGAN from ruDALL-E demo.
I used this Colab for the upscaling. GitHub repo for the upscaler.
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Real-ESRGAN (an upscaler) implementation used by ruDALL-E demo seems to create a lot more fine details than the other implementation of Real-ESRGAN that I used. Gallery contains upscaler comparisons for 2 input images. An implementation of SwinIR upscaler is also included.
Colab for Real-ESRGAN used by ruDALL-E demo. GitHub repo.
What are some alternatives?
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
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.
Image-Super-Resolution-via-Itera
RealSR - Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model (ICCV 2019)
EGVSR - Efficient & Generic Video Super-Resolution
onnx-web - web UI for GPU-accelerated ONNX pipelines like Stable Diffusion, even on Windows and AMD
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.
realesrgan-gui - 实用、美观的 Real-ESRGAN 图形界面，同时支持 Windows、Ubuntu 和 macOS 平台。现在也支持 Real-CUGAN 了！(Cross-platform GUI for image upscaler Real-ESRGAN with additional features. Now with Real-CUGAN support!)