A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

Overview

A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

The official pytorch implementation of the paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis", the paper can be found here.

0. Data

The datasets used in the paper can be found at link.

After testing on over 20 datasets with each has less than 100 images, this GAN converges on 80% of them. I still cannot summarize an obvious pattern of the "good properties" for a dataset which this GAN can converge on, please feel free to try with your own datasets.

1. Description

The code is structured as follows:

  • models.py: all the models' structure definition.

  • operation.py: the helper functions and data loading methods during training.

  • train.py: the main entry of the code, execute this file to train the model, the intermediate results and checkpoints will be automatically saved periodically into a folder "train_results".

  • eval.py: generates images from a trained generator into a folder, which can be used to calculate FID score.

  • benchmarking: the functions we used to compute FID are located here, it automatically downloads the pytorch official inception model.

  • lpips: this folder contains the code to compute the LPIPS score, the inception model is also automatically download from official location.

  • scripts: this folder contains many scripts you can use to play around the trained model. Including:

    1. style_mix.py: style-mixing as introduced in the paper;
    2. generate_video.py: generating a continuous video from the interpolation of generated images;
    3. find_nearest_neighbor.py: given a generated image, find the closest real-image from the training set;
    4. train_backtracking_one.py: given a real-image, find the latent vector of this image from a trained Generator.

2. How to run

Place all your training images in a folder, and simply call

python train.py --path /path/to/RGB-image-folder

You can also see all the training options by:

python train.py --help

The code will automatically create a new folder (you have to specify the name of the folder using --name option) to store the trained checkpoints and intermediate synthesis results.

Once finish training, you can generate 100 images (or as many as you want) by:

cd ./train_results/name_of_your_training/
python eval.py --n_sample 100 

3. Pre-trained models

The pre-trained models and the respective code of each model are shared here.

You can also use FastGAN to generate images with a pre-packaged Docker image, hosted on the Replicate registry: https://beta.replicate.ai/odegeasslbc/FastGAN

4. Important notes

  1. The provided code is for research use only.

  2. Different model and training configurations are needed on different datasets. You may have to tune the hyper-parameters to get the best results on your own datasets.

    2.1. The hyper-parameters includes: the augmentation options, the model depth (how many layers), the model width (channel numbers of each layer). To change these, you have to change the code in models.py and train.py directly.

    2.2. Please check the code in the shared pre-trained models on how each of them are configured differently on different datasets. Especially, compare the models.py for ffhq and art datasets, you will get an idea on what chages could be made on different datasets.

5. Other notes

  1. The provided scripts are not well organized, contributions are welcomed to clean them.
  2. An third-party implementation of this paper can be found here, where some other techniques are included. I suggest you try both implementation if you find one of them does not work.
Owner
Bingchen Liu
Bingchen Liu
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
ECLARE: Extreme Classification with Label Graph Correlations

ECLARE ECLARE: Extreme Classification with Label Graph Correlations @InProceedings{Mittal21b, author = "Mittal, A. and Sachdeva, N. and Agrawal

Extreme Classification 35 Nov 06, 2022
Contains code for Deep Kernelized Dense Geometric Matching

DKM - Deep Kernelized Dense Geometric Matching Contains code for Deep Kernelized Dense Geometric Matching We provide pretrained models and code for ev

Johan Edstedt 83 Dec 23, 2022
[CVPR 2021] "Multimodal Motion Prediction with Stacked Transformers": official code implementation and project page.

mmTransformer Introduction This repo is official implementation for mmTransformer in pytorch. Currently, the core code of mmTransformer is implemented

DeciForce: Crossroads of Machine Perception and Autonomy 232 Dec 31, 2022
Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.

Updates (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training. Pyr

1.3k Jan 04, 2023
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation".

IR-GAIL This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation". Dependency The experiments are de

Zhao-Heng Yin 1 Jul 14, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

Phil Wang 55 Jan 01, 2023
MoveNetを用いたPythonでの姿勢推定のデモ

MoveNet-Python-Example MoveNetのPythonでの動作サンプルです。 ONNXに変換したモデルも同梱しています。変換自体を試したい方はMoveNet_tf2onnx.ipynbを使用ください。 2021/08/24時点でTensorFlow Hubで提供されている以下モデ

KazuhitoTakahashi 38 Dec 17, 2022
Active and Sample-Efficient Model Evaluation

Active Testing: Sample-Efficient Model Evaluation Hi, good to see you here! 👋 This is code for "Active Testing: Sample-Efficient Model Evaluation". P

Jannik Kossen 19 Oct 30, 2022
Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Şebnem 6 Jan 18, 2022
A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Rce-KGQA A novel pipeline framework for multi-hop complex KGQA task. This framework mainly contains two modules, answering_filtering_module and relati

金伟强 -上海大学人工智能小渣渣~ 16 Nov 18, 2022
Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

Pytorch Implementation of Deep Orthogonal Fusion of Local and Global Features (DOLG) This is the unofficial PyTorch Implementation of "DOLG: Single-St

DK 96 Jan 06, 2023
[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

wseg Overview The Pytorch implementation of Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast. [arXiv] Though image-level weakly

Ye Du 96 Dec 30, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
Toontown: Galaxy, a new Toontown game based on Disney's Toontown Online

Toontown: Galaxy The official archive repo for Toontown: Galaxy, a new Toontown

1 Feb 15, 2022
Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

VT-UNet This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of VT-UNet. Environmen

Himashi Amanda Peiris 114 Dec 20, 2022
RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow. They have a parallel sampling feature in order to increase computation speed (especially in high-performance computing (HPC)).

Fangjian Li 3 Dec 28, 2021