The implementation of 'Image synthesis via semantic composition'.

Related tags

Deep LearningSCGAN
Overview

Image synthesis via semantic synthesis [Project Page]

by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia.

Introduction

This repository gives the implementation of our semantic image synthesis method in ICCV 2021 paper, 'Image synthesis via semantic synthesis'.

Teaser

Our framework

framework

Usage

git clone https://github.com/dvlab-research/SCGAN.git
cd SCGAN/code

To use this code, please install PyTorch 1.0 and Python 3+. Other dependencies can be installed by

pip install -r requirements.txt

Dataset Preparation

Please refer to SPADE for detailed execution.

Testing

  1. Downloading pretrained models, then putting the folder containing model weights in the folder ./checkpoints.

  2. Producing images with the pretrained models.

python test.py --gpu_ids 0,1,2,3 --dataset_mode [dataset] --config config/scgan_[dataset]_test.yml --fid --gt [gt_path] --visual_n 1

For example,

python test.py --gpu_ids 0,1,2,3 --dataset_mode celeba --config config/scgan_celeba-test.yml --fid --gt /data/datasets/celeba --visual_n 1
  1. Visual results are stored at ./results/scgan_[dataset]/ by default.

Pretrained Models (to be updated)

Dataset Download link
CelebAMask-HQ Baidu Disk (Code: face)

Training

Using train.sh to train new models. Or you can specify training options in config/[config_file].yml.

Key operators

Our proposed dynamic computation units (spatial conditional convolution and normalization) are extended from conditionally parameterized convolutions [1]. We generalize the scalar condition into a spatial one and also apply these techniques to normalization. sc-ops

Citation

If our research is useful for you, please consider citing:

@inproceedings{wang2021image,
  title={Image Synthesis via Semantic Composition},
  author={Wang, Yi and Qi, Lu and Chen, Ying-Cong and Zhang, Xiangyu and Jia, Jiaya},
  booktitle={ICCV},
  year={2021}
}

Acknowledgements

This code is built upon SPADE, Imaginaire, and PyTorch-FID.

Reference

[1] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. Condconv: Conditionally parameterized convolutions for efficient inference. In NeurIPS. 2019.

Contact

Please send email to [email protected].

Owner
DV Lab
Deep Vision Lab
DV Lab
This code is a near-infrared spectrum modeling method based on PCA and pls

Nirs-Pls-Corn This code is a near-infrared spectrum modeling method based on PCA and pls 近红外光谱分析技术属于交叉领域,需要化学、计算机科学、生物科学等多领域的合作。为此,在(北邮邮电大学杨辉华老师团队)指导下

Fu Pengyou 6 Dec 17, 2022
In this project, two programs can help you take full agvantage of time on the model training with a remote server

In this project, two programs can help you take full agvantage of time on the model training with a remote server, which can push notification to your phone about the information during model trainin

GrayLee 8 Dec 27, 2022
Asymmetric metric learning for knowledge transfer

Asymmetric metric learning This is the official code that enables the reproduction of the results from our paper: Asymmetric metric learning for knowl

20 Dec 06, 2022
This is the source code of the solver used to compete in the International Timetabling Competition 2019.

ITC2019 Solver This is the source code of the solver used to compete in the International Timetabling Competition 2019. Building .NET Core (2.1 or hig

Edon Gashi 8 Jan 22, 2022
Understanding the Generalization Benefit of Model Invariance from a Data Perspective

Understanding the Generalization Benefit of Model Invariance from a Data Perspective This is the code for our NeurIPS2021 paper "Understanding the Gen

1 Jan 15, 2022
Official Code Release for Container : Context Aggregation Network

Container: Context Aggregation Network Official Code Release for Container : Context Aggregation Network Comparion between CNN, MLP-Mixer and Transfor

peng gao 42 Nov 17, 2021
Code for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling Using BERT Adapter"

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Code and checkpoints for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling

274 Dec 06, 2022
Semi-supervised Implicit Scene Completion from Sparse LiDAR

Semi-supervised Implicit Scene Completion from Sparse LiDAR Paper Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZH

114 Nov 30, 2022
Implementation of the paper "Generating Symbolic Reasoning Problems with Transformer GANs"

Generating Symbolic Reasoning Problems with Transformer GANs This is the implementation of the paper Generating Symbolic Reasoning Problems with Trans

Reactive Systems Group 1 Apr 18, 2022
Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 07, 2023
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
Classifying cat and dog images using Kaggle dataset

PyTorch Image Classification Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to

Robert Coleman 74 Nov 22, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
A fast Evolution Strategy implementation in Python

Evostra: Evolution Strategy for Python Evolution Strategy (ES) is an optimization technique based on ideas of adaptation and evolution. You can learn

Mika 251 Dec 08, 2022
Official code of "Mitigating the Mutual Error Amplification for Semi-Supervised Object Detection"

CrossTeaching-SSOD 0. Introduction Official code of "Mitigating the Mutual Error Amplification for Semi-Supervised Object Detection" This repo include

Bruno Ma 9 Nov 29, 2022
Syllabus del curso IIC2115 - Programación como Herramienta para la Ingeniería 2022/I

IIC2115 - Programación como Herramienta para la Ingeniería Videos y tutoriales Tutorial CMD Tutorial Instalación Python y Jupyter Tutorial de git-GitH

21 Nov 09, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb

Yu Tian 117 Jan 03, 2023
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

2D-TAN (Optimized) Introduction This is an optimized re-implementation repository for AAAI'2020 paper: Learning 2D Temporal Localization Networks for

Joya Chen 112 Dec 31, 2022
Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

RNN-for-Joint-NLU Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

Kim SungDong 194 Dec 28, 2022
Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

NIRPS-ETC Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph February 2

Nolan Grieves 2 Sep 15, 2022