pix2pixHD VS co-mod-gan

Compare pix2pixHD vs co-mod-gan and see what are their differences.


[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (by zsyzzsoft)
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pix2pixHD co-mod-gan
3 1
5,597 273
1.1% -
0.0 5.3
3 days ago 22 days ago
Python Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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Posts with mentions or reviews of pix2pixHD. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-25.


Posts with mentions or reviews of co-mod-gan. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing pix2pixHD and co-mod-gan you can also consider the following projects:

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments

generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)

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

sofgan - [TOG 2021] SofGAN: A Portrait Image Generator with Dynamic Styling

Im2Vec - [CVPR 2021 Oral] Im2Vec Synthesizing Vector Graphics without Vector Supervision

ArtGAN - ArtGAN: This work presents a series of new approaches to improve Generative Adversarial Network (GAN) for conditional image synthesis and we name the proposed model as “ArtGAN”. Implementations are in Caffe/Tensorflow.

DeblurGANv2 - [ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang

DeepSIM - Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral)

ComboLoss - Official PyTorch Implementation for Paper <ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks> (State-of-the-art Performance on 3 Popular Benchmark Dataset)