- GAN-based Super-Resolution for upscaling generated images - Variation of the GigaGAN paper for image-conditioned upscaling - Torch implementation based on the unofficial lucidrains/gigagan-pytorch repository -
GAN-based Super-Resolution for real-world images, a variation of theGigaGANpaper for image-conditioned upscaling. Torch implementation is based on the unofficiallucidrains/gigagan-pytorchrepository. Usage $ pip install aura-sr fromaura_srimportAuraSRaura_sr=AuraSR.from_pretrained() ...
GPL-3.0 license starsforks Notifications Code Issues Pull requests Actions Projects Security Insights Additional navigation options master 1Branch 0Tags Code PyTorch-SRGAN A modern PyTorch implementation of SRGAN It is deeply based onPhoto-Realistic Single Image Super-Resolution Using a Generative Adversaria...
The implementation of the T2SR framework was done using PyTorch version 1.13.1, with Mean Squared Error (MSE) as the loss function. The details of the hyperparameter setup are provided in Table4. TABLE 4.Hyperparameters setup. During training, it became apparent that a lower learning rate of...
# AuraSR: GAN-based Super-Resolution for real-world, a reproduction of the GigaGAN* paper. Implementation is # based on the unofficial lucidrains/gigagan-pytorch repository. Heavily modified from there.# # https://mingukkang.github.io/GigaGAN/ ...
(Official PyTorch Implementation) Update - September 1, 2020 Release training code at Github/Tencent. Update - May 26, 2020 AddDF2K-JPEGModel. Executable filesbased onncnnare available. Test your own images on windows/linux/macos. More details refer torealsr-ncnn-vulkan ...
框架与环境:PyTorch,NVIDIA 2080Ti GPU。 Comparisons with Existing Methods 本文基于D2CRealSR x8 和 RealSR x4、x3 和 x2 进行模型的训练和测试,在 Y 通道上进行指标的评估。 定量结果如表1所示。 定性结果如下图(x4 SR on RealSR dataset)。 对应定性结果: 现有SR 方法(如 RCAN、LP-KPN、CDC)倾向于将细...
The network was implemented in Python using PyTorch libraries, and the code was released online (https://github.com/nianmaodu/SR-Net). Training and testing were performed on Google Collab with one Tesla P100 and Intel(R) Xeon(R) CPU @ 2.30. The number of training epochs was set to 200...
An official PyTorch implementation of theSRResCycGANnetwork as described in the paperDeep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution. This work is participated in theAIM 2020 Real-Image Super-resolutionchallenge track-3 at the high x4 upscaling factor....
Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs/1609.04802) in PyTorch Usage Training usage: main_srresnet.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR] [--step STEP] [--cuda] [--...