GFPGAN-for-Video-SR VS Real-ESRGAN-ncnn-vulkan

Compare GFPGAN-for-Video-SR vs Real-ESRGAN-ncnn-vulkan and see what are their differences.

Real-ESRGAN-ncnn-vulkan

NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration. (by xinntao)
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GFPGAN-for-Video-SR Real-ESRGAN-ncnn-vulkan
1 38
31 1,352
- -
0.0 0.0
10 months ago 5 months ago
Jupyter Notebook C
- GNU General Public License v3.0 or later
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GFPGAN-for-Video-SR

Posts with mentions or reviews of GFPGAN-for-Video-SR. We have used some of these posts to build our list of alternatives and similar projects.

Real-ESRGAN-ncnn-vulkan

Posts with mentions or reviews of Real-ESRGAN-ncnn-vulkan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-07.

What are some alternatives?

When comparing GFPGAN-for-Video-SR and Real-ESRGAN-ncnn-vulkan you can also consider the following projects:

nn - ๐Ÿง‘โ€๐Ÿซ 60 Implementations/tutorials of deep learning papers with side-by-side notes ๐Ÿ“; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ๐ŸŽฎ reinforcement learning (ppo, dqn), capsnet, distillation, ... ๐Ÿง 

realsr-ncnn-vulkan - RealSR super resolution implemented with ncnn library

Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!

cupscale - Image Upscaling GUI based on ESRGAN

ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]

gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

CodeFormer - [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

License-super-resolution - A License Plate Image Reconstruction Project in Tensorflow2

SwinIR - SwinIR: Image Restoration Using Swin Transformer (official repository)

Anime4K - A High-Quality Real Time Upscaler for Anime Video

chaiNNer - A flowchart/node-based image processing GUI aimed at making chaining image processing tasks (especially upscaling done by neural networks) easy, intuitive, and customizable. [Moved to: https://github.com/chaiNNer-org/chaiNNer]