Real-ESRGAN
Real-ESRGAN-colab
| Real-ESRGAN | Real-ESRGAN-colab | |
|---|---|---|
| 131 | 1 | |
| 26,111 | 44 | |
| - | - | |
| 2.7 | 0.0 | |
| 19 days ago | over 1 year ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | - |
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Real-ESRGAN
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AI-Powered Nvidia RTX Video HDR Transforms Standard Video into HDR Video
It's not exactly what you're after, as it's anime specific and you need to process the video yourself (eg disassemble to frames, run the upscaler, then assemble back to a movie file), but Real-ESRGAN is really good:
https://github.com/xinntao/Real-ESRGAN/
It's pretty brilliant for cleaning up very old, low resolution anime.
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Photorealistic Video Generation with Diffusion Models
Just a note you can run upscaling on your home desktop with Real-ESRGAN:
https://github.com/xinntao/Real-ESRGAN
- What software to use for upscaling anime edits
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What neural net for SISR?
Maybe Real-ESRGAN is a good fit? Even tho it's a couple of years old
- Cant make concurrent calls to Model
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Outis my beloved
I'm glad you noticed! I upscaled the icon from the wiki using Real-ESRGAN's 4xplus anime model, then photoshopped out the text. Worked far better than waifu2x.
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ComicMerge (Beta testing version - SafeTensors)
A: Try using High-res Fix and R-ESRGAN 4x+ Anime6B as upscaler
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Is there any way to upscale local files permanently using Nvidia's RT VSR?
Maybe try this one https://github.com/xinntao/Real-ESRGAN it may work even better.
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YOASOBI Idol [3840 x 2160]
Screenshotted from the official music video, upscaled to 4k using a state of the art ML model.
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Compilation of (almost) all end of chapter panels
Do you happen to remember which chapter has that "scene"? You could also try to enhance it yourself, I did it using Real-ESRGAN, which is really easy to use.
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?
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
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.
SwinIR - SwinIR: Image Restoration Using Swin Transformer (official repository)
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
traiNNer - traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
BSRGAN - Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
waifu2x - Image Super-Resolution for Anime-Style Art
Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
Image-Super-Resolution-via-Iterative-Refinement - Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch