mmagic VS Real-ESRGAN-colab

Compare mmagic vs Real-ESRGAN-colab and see what are their differences.

mmagic

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc. (by open-mmlab)
super-resolution inpainting matting image-generation generative-adversarial-network Pytorch Deep Learning video-interpolation video-frame-interpolation video-super-resolution Computer Vision Image Editing Image processing image-synthesis diffusion text2image aigc generative-ai diffusion-models
Source Code
mmagic.readthedocs.io
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Real-ESRGAN-colab

A Real-ESRGAN model trained on a custom dataset (by boomb0om)
Pytorch super-resolution esrgan real-esrgan denoise Decompress
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mmagic Real-ESRGAN-colab
5 1
6,588 44
1.1% -
8.7 0.0
about 2 months ago over 1 year ago
Jupyter Notebook Python
Apache License 2.0 -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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mmagic

Posts with mentions or reviews of mmagic. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-18.

Real-ESRGAN-colab

Posts with mentions or reviews of Real-ESRGAN-colab. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-07.

What are some alternatives?

When comparing mmagic and Real-ESRGAN-colab you can also consider the following projects:

a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

BasicSR - Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

Image-Super-Resolution-via-Iterative-Refinement - Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch

Real-ESRGAN - PyTorch implementation of Real-ESRGAN model

cnn-watermark-removal - Fully convolutional deep neural network to remove transparent overlays from images

traiNNer - traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.

Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.

contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

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

mmagic vs a-PyTorch-Tutorial-to-Super-Resolution Real-ESRGAN-colab vs Real-ESRGAN mmagic vs Real-ESRGAN Real-ESRGAN-colab vs BasicSR mmagic vs Image-Super-Resolution-via-Iterative-Refinement Real-ESRGAN-colab vs Real-ESRGAN mmagic vs cnn-watermark-removal Real-ESRGAN-colab vs traiNNer mmagic vs Deep-Exemplar-based-Video-Colorization Real-ESRGAN-colab vs NAFNet mmagic vs contrastive-unpaired-translation Real-ESRGAN-colab vs Real-ESRGAN-Video-Batch-Process