BasicSR
stylegan2-pytorch
BasicSR | stylegan2-pytorch | |
---|---|---|
5 | 3 | |
6,233 | 2,667 | |
2.8% | - | |
0.0 | 0.0 | |
14 days ago | 6 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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BasicSR
- About Open Source Image and Video Restoration Toolbox
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Super-Resolution Generative Adversarial Networks (SRGAN)
I think you might be interested in https://github.com/XPixelGroup/BasicSR
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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.
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Anyone currently able to run Real-ESRGAN notebooks on colab currently?
!pip install git+https://github.com/xinntao/BasicSR.git
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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.
stylegan2-pytorch
- Converting a .pkl file to a .pt for StyleGan2 Model
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I'm stumped with installing PyTorch.
Originally I wanted to run https://github.com/JCBrouwer/maua-stylegan2. I was trying to run the convert_weight.py but it resulted in shape mismatch errors in torch torch.Size([1, 512, 4, 4]) vs torch.Size([1]), so I tried the version here https://github.com/rosinality/stylegan2-pytorch/blob/master/convert_weight.py and the result was the same.
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[D] Node Collapse with StyleGAN2 - ada
Used this implementation (https://github.com/rosinality/stylegan2-pytorch) to better understand the code. IMO it is written way more clearly than the official implementation. You should spend a lot of time reviewing the code until you understand what each line is doing. It isn't helpful that their aren't very many comments, but you should be able to recognize different architectures and calculations from the paper. Understanding the details is key. Don't let your eyes glaze over any part of it.
What are some alternatives?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
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 - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
stylegan-waifu-generator - Generate your waifu with styleGAN, stylegan老婆生成器
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
PyTorch-StudioGAN - StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
EGVSR - Efficient & Generic Video Super-Resolution
maua-stylegan2 - This is the repo for my experiments with StyleGAN2. There are many like it, but this one is mine. Contains code for the paper Audio-reactive Latent Interpolations with StyleGAN.