SwinIR
Ne2Ne-Image-Denoising
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| SwinIR | Ne2Ne-Image-Denoising | |
|---|---|---|
| 27 | 1 | |
| 4,082 | 27 | |
| - | - | |
| 0.0 | 3.0 | |
| 26 days ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | - |
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SwinIR
- Certain directories (e.g. SwinIR) are empty (version: Empire Media Science A1111 Web UI Installer)
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I used Real-ESRGAN to upscale my image, but if you zoom in you can see that “water particles” looks like some random lines and image overall looks cartoonish. Is there a way to fix it?
003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth
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Any luck changing the upscaler? They seem to be hard coded
I was trying to get a new upscaler working, as someone pointed me to one that did a good job of preserving and creating new details: https://github.com/JingyunLiang/SwinIR
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Spatial-temporal denoising
SwinIR: https://github.com/JingyunLiang/SwinIR
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A Monster Hunter: World Virtual Photography Tutorial - YouTube
Upscalers that I use SwinIR https://github.com/JingyunLiang/SwinIR https://github.com/AUTOMATIC1111/stable-diffusion-webui (Use 'extras' tab for the upscaler function) Topaz Gigapixel AI https://www.topazlabs.com/gigapixel-ai
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what are the alternatives to letsenhance.io?
You could try out chaiNNer, it is a free local/offline application. There are a lot of (upscaling) models which you can download an use with it. You can for example try out SwinIR-L (link will start a model download) or any other model you like depending on your input images.
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[R] Swin transformer while using a rectangular attention window
the relative attention bias can be made non-square in the original implementation, there is a parameter window_size, at 7, that is forced to (7,7) directly, but you can change it easily. https://github.com/JingyunLiang/SwinIR/blob/main/models/network_swinir.py
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Robot dance animation with Robo-Diffusion (1024x576)
Use SwinIR medium model to upscale by 2 times. This will result in a video of 2048x1152.
- Help Need to get my VQGAN images to 10000 x 10000
- Made with Outpainting + Inpainting(original picture and promt in comments)
Ne2Ne-Image-Denoising
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Neighbour2Neighbour: The new self-supervised Image Denoising training
It gives outstanding denoising performance with just 300 training images. Have a look at these image results and minimal network implementation here: https://github.com/neeraj3029/Ne2Ne-Image-Denoising
What are some alternatives?
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
Real-ESRGAN-ncnn-vulkan - NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
lightly - A python library for self-supervised learning on images.
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
MPRNet - [CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
policy-adaptation-during-deployment - Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.
LaTeX-OCR - pix2tex: Using a ViT to convert images of equations into LaTeX code.
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)