BasicSR
ESRGAN-PyTorch
| BasicSR | ESRGAN-PyTorch | |
|---|---|---|
| 5 | 1 | |
| 6,233 | 130 | |
| 2.8% | - | |
| 0.0 | 7.0 | |
| 14 days ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BasicSR
- About Open Source Image and Video Restoration Toolbox
-
Super-Resolution Generative Adversarial Networks (SRGAN)
I think you might be interested in https://github.com/XPixelGroup/BasicSR
-
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.
-
Anyone currently able to run Real-ESRGAN notebooks on colab currently?
!pip install git+https://github.com/xinntao/BasicSR.git
-
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.
ESRGAN-PyTorch
-
[D] Image Quality Assessment
For perceptual though, as /u/zshn25 points out, the difficult but possibly applicable solution is to build a Convolutional Discriminator as if for training a GAN, and then your own image set with each image having a grade from 0.0 (bad) to 1.0 (great), or whatever range (don't use activation on the output), and then training it with augmentations so hopefully the discriminator can figure out what exactly it is you like and try to project it to a number. This requires you sitting there and making the dataset though.
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.
onnx-web - web UI for GPU-accelerated ONNX pipelines like Stable Diffusion, even on Windows and AMD
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
EGVSR - Efficient & Generic Video Super-Resolution
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
torchSR - Super Resolution datasets and models in Pytorch
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
EDSR_Tensorflow - TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.