Official PyTorch implementation for FastDPM, a fast sampling algorithm for diffusion probabilistic models

Related tags

Deep LearningFastDPM_pytorch
Overview

Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models".

FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. See paper via this link.

Pretrained models

Download checkpoints from this link and this link. Put them under checkpoints\ema_diffusion_${dataset_name}_model\model.ckpt, where ${dataset_name} is cifar10, celeba64, lsun_bedroom, lsun_church, or lsun_cat.

Usage

General command: python generate.py -ema -name ${dataset_name} -approxdiff ${approximate_diffusion_process} -kappa ${kappa} -S ${FastDPM_length} -schedule ${noise_level_schedule} -n ${number_to_generate} -bs ${batchsize} -gpu ${gpu_index}

  • ${dataset_name}: cifar10, celeba64, lsun_bedroom, lsun_church, or lsun_cat
  • ${approximate_diffusion_process}: VAR or STEP
  • ${kappa}: a real value between 0 and 1
  • ${FastDPM_length}: an integer between 1 and 1000; 10, 20, 50, 100 used in paper.
  • ${noise_level_schedule}: linear or quadratic

CIFAR-10

Below are commands to generate CIFAR-10 images.

  • Standard DDPM generation: python generate.py -ema -name cifar10 -approxdiff STD -n 16 -bs 16
  • FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name cifar10 -approxdiff STEP -kappa 1.0 -S 50 -schedule quadratic -n 16 -bs 16
  • FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name cifar10 -approxdiff STEP -kappa 0.0 -S 50 -schedule quadratic -n 16 -bs 16
  • FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name cifar10 -approxdiff VAR -kappa 1.0 -S 50 -schedule quadratic -n 16 -bs 16
  • FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name cifar10 -approxdiff VAR -kappa 0.0 -S 50 -schedule quadratic -n 16 -bs 16

CelebA

Below are commands to generate CelebA images.

  • Standard DDPM generation: python generate.py -ema -name celeba64 -approxdiff STD -n 16 -bs 16
  • FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name celeba64 -approxdiff STEP -kappa 1.0 -S 50 -schedule linear -n 16 -bs 16
  • FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name celeba64 -approxdiff STEP -kappa 0.0 -S 50 -schedule linear -n 16 -bs 16
  • FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name celeba64 -approxdiff VAR -kappa 1.0 -S 50 -schedule linear -n 16 -bs 16
  • FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name celeba64 -approxdiff VAR -kappa 0.0 -S 50 -schedule linear -n 16 -bs 16

LSUN_bedroom

Below are commands to generate LSUN bedroom images.

  • Standard DDPM generation: python generate.py -ema -name lsun_bedroom -approxdiff STD -n 8 -bs 8
  • FastDPM generation (STEP + DDPM-rev): python generate.py -ema -name lsun_bedroom -approxdiff STEP -kappa 1.0 -S 50 -schedule linear -n 8 -bs 8
  • FastDPM generation (STEP + DDIM-rev): python generate.py -ema -name lsun_bedroom -approxdiff STEP -kappa 0.0 -S 50 -schedule linear -n 8 -bs 8
  • FastDPM generation (VAR + DDPM-rev): python generate.py -ema -name lsun_bedroom -approxdiff VAR -kappa 1.0 -S 50 -schedule linear -n 8 -bs 8
  • FastDPM generation (VAR + DDIM-rev): python generate.py -ema -name lsun_bedroom -approxdiff VAR -kappa 0.0 -S 50 -schedule linear -n 8 -bs 8

Note

To generate 50K samples, set -n 50000 and batchsize (-bs) divisible by 50K.

Compute FID

To compute FID of generated samples, first make sure there are 50K images, and then run

  • python FID.py -ema -name cifar10 -approxdiff STEP -kappa 1.0 -S 50 -schedule quadratic

Code References

Owner
Zhifeng Kong
Ph.D. student, UCSD
Zhifeng Kong
GitHub Repository
A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Project Name: Which Bollywood Celebrity You look like A Deep learning based streamlit web app which can tell with which bollywood celebrity your face

BAPPY AHMED 20 Dec 28, 2021
Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"

Easy-To-Hard The official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks". Gett

Avi Schwarzschild 52 Sep 08, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
PyTorch Connectomics: segmentation toolbox for EM connectomics

Introduction The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individua

Zudi Lin 132 Dec 26, 2022
Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

MilaGraph 117 Dec 09, 2022
Housing Price Prediction

This project aim was to predict the price of houses in the Boston area during the great financial crisis through regression, as well as classify houses into different quality categories according to

Florian Klement 1 Jan 27, 2022
(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework Background: Outlier detection (OD) is a key data mining task for identify

Yue Zhao 127 Jan 05, 2023
Chess reinforcement learning by AlphaGo Zero methods.

About Chess reinforcement learning by AlphaGo Zero methods. This project is based on these main resources: DeepMind's Oct 19th publication: Mastering

Samuel 2k Dec 29, 2022
PyTorch implementation for Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition.

Stochastic CSLR This is the PyTorch implementation for the ECCV 2020 paper: Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuou

Zhe Niu 28 Dec 19, 2022
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

26 Nov 29, 2022
An LSTM based GAN for Human motion synthesis

GAN-motion-Prediction An LSTM based GAN for motion synthesis has a few issues reading H3.6M data from A.Jain et al , will fix soon. Prediction of the

Amogh Adishesha 9 Jun 17, 2022
Match SafeGraph POIs with Data collected through a cultural resource survey in Washington DC.

Match SafeGraph POI data with Cultural Resource Places in Washington DC Match SafeGraph POIs with Data collected through a cultural resource survey in

Changjie Chen 1 Jan 05, 2022
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Auto-ViML Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal" (autovimal logo created by Sanket Ghanmare) N

AutoViz and Auto_ViML 397 Dec 30, 2022
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi

Vitaliy Hramchenko 58 Dec 19, 2022
K-Nearest Neighbor in Pytorch

Pytorch KNN CUDA 2019/11/02 This repository will no longer be maintained as pytorch supports sort() and kthvalue on tensors. git clone https://github.

Chris Choy 65 Dec 01, 2022
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

DeepCTR DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can

浅梦 6.6k Jan 08, 2023
Permute Me Softly: Learning Soft Permutations for Graph Representations

Permute Me Softly: Learning Soft Permutations for Graph Representations

Giannis Nikolentzos 7 Jul 10, 2022
Simple SN-GAN to generate CryptoPunks

CryptoPunks GAN Simple SN-GAN to generate CryptoPunks. Neural network architecture and training code has been modified from the PyTorch DCGAN example.

Teddy Koker 66 Dec 15, 2022
Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".

multilingual-mrc-isdg Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph". This r

Liyan 5 Dec 07, 2022