Python Generative-replay Resources

Introduction Pytorch implementation of Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Expert. | paper Song Park1

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

TransGanFormer (wip) Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. I

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

ZhuSuan is a Python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021) An efficient PyTorch library for Point Cloud Completion.

P2PaLA Page to PAGE Layout Analysis (P2PaLA) is a toolkit for Document Layout Analysis based on Neural Networks. 💥 Try our new DEMO for online baseli

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, ICLR 2021 (Spotlight) Demo | Paper [NEW!] Time to play with our interac

Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p

Training GANs with Stronger Augmentations via Contrastive Discriminator (ICLR 2021) This repository contains the code for reproducing the paper: Train

Bringing Old Photo Back to Life (CVPR 2020 oral)

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Anycost GAN video | paper | website Anycost GANs for Interactive Image Synthesis and Editing Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zh

ASEGAN: Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder 中文版简介 Readme with English Version 介绍 基于SEGAN模型的改进版本，使用自主设计的非

GANsformer: Generative Adversarial Transformers Drew A. Hudson* & C. Lawrence Zitnick *I wish to thank Christopher D. Manning for the fruitf

OpenChat is opensource chatting framework for generative models.

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Gen

This repository contains the code for the ICLR 2021 paper "Counterfactual Generative Networks" by Axel Sauer and Andreas Geiger. If you want to take the CGN for a spin and generate counterfactual images, you can try out the Colab below.

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model The task of age transformation illustrates the change of an individual

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

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