Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion
Korea Advanced Institute of Science and Technology(KAIST)（韩国科学技术院（KAIST））
Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high resolution images starting from much coarser resolution images using a {\em single} score function trained with a positional embedding. This enables a neural network to be much lighter and also enables time-efficient image generation without…
Heavy-Tailed Diffusion with Denoising Lévy Probabilistic Models
INRIA - Department of Computer Science, PSL Research University, Paris, France（INRIA-计算机科学系，PSL研究大学，法国巴黎）；École Polytechnique - CMAP, IP Paris, Palaiseau, France（巴黎综合理工学院-CMAP，IP巴黎，法国帕莱苏）
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；National University of Singapore（新加坡国立大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept applic…
Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机科学与数据科学学院）；Research Institute of Quantum Technology, The Hong Kong Polytechnic University（香港理工大学量子技术研究所）
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris（CEREA、ENPC、EDF R&D、巴黎理工学院）；Department of Earth Science & Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；Department of Computing, Imperial College London（帝国理工学院伦敦计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院伦敦数据科学研究院）
An explicit formulation of the learned noise predictor $ε_θ({\bf x}_t, t)$ via the forward-process noise $ε_{t}$ in denoising diffusion probabilistic models (DDPMs)
Department of Mathematics, Hankuk University of Foreign Studies（数学系，韩国外语大学）
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities. This includes corruption with transition matrices that mimic Gaussian kernels in continuous space, matrices based on nearest neighbors in embedding space, and matri…
Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；South China University of Technology（华南理工大学）；University of Surrey（萨里大学）；MSRA（微软亚洲研究院）；Microsoft Azure Speech（微软Azure语音）；Imperial College London（帝国理工学院）
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusi…
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation
Harbin Institute of Technology（哈尔滨工业大学）；Menoufia University（梅努菲亚大学）；Lodz University of Technology（罗兹技术大学）；Galala University（加拉拉大学）；Shenyang Blower Works Group Corporation（沈阳鼓风机集团股份有限公司）
Denoising diffusion models for high-resolution microscopy image restoration
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高级研究所）；Goethe University Frankfurt（法兰克福大学）；Institute of Physical and Theoretical Chemistry（物理与理论化学研究所）
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score…
In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this end, we use diffusion probabilistic models, a class of latent variable models closely connected to score matching and energy-based methods. Our model learns gradients by optimizing a variational bound on the data likelihood and at inference time converts white noise into a sample of the distribution of interest through a Markov chain using Langevin sampling. We demonstrate experi…
Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motions but typically suffer in motion style diversity. For the first time, we propose a framework using the denoising diffusion probabilistic model (DDPM) to synthesize styled human motions, integrating two tasks into one pipeline with increased style diversity compared with traditional motion synthesis methods. Experimental results show that our system…
We propose a novel unpaired image-to-image translation method that uses denoising diffusion probabilistic models without requiring adversarial training. Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain. In particular, we update both domain translation models simultaneously, and we generate target domain images by a denoising Markov Chain Monte Carlo approach tha…
Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted versions back to the original. However, previous work has largely focused on in-place corruption, adding noise to each pixel or token individually while keeping their locations the same. In this work, we consider a broader class of corruption processes and denoising models over sequence data that can insert and delete elements, while still being efficient to train and sample from. We demonstrate that these models outperf…
Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
Department of Electrical Engineering and Computer Science, York University（约克大学电气工程与计算机科学系）；Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机科学学院）；Cistel Technology（Cistel技术公司）
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equat…
Neural Networks are infamously sensitive to small perturbations in their inputs, making them vulnerable to adversarial attacks. This project evaluates the performance of Denoising Diffusion Probabilistic Models (DDPM) as a purification technique to defend against adversarial attacks. This works by adding noise to an adversarial example before removing it through the reverse process of the diffusion model. We evaluate the approach on the PatchCamelyon data set for histopathologic scans of lymph node sections and find an improvement of the robust accuracy by up to 88\% of the original model's ac…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎理工大学LTCI研究所）；University of Turin（都灵大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes（巴黎萨克雷大学、CNRS、CentraleSupélec信号与系统实验室）
We provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting. While most inpainting algorithms require retraining with each new mask, we prove that diffusion based inpainting generalizes well to unseen masks without retraining. We analyze a recently proposed popular diffusion based inpainting algorithm called RePaint (Lugmayr et al., 2022), and show that it has a bias due to misalignment that hampers sample recovery even in a two-state diffusion process. Motivated by our analysis, we propose a modified RePaint algorithm we call ReP…
Generative quantum machine learning via denoising diffusion probabilistic models
Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA（物理与天文学系，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA（明斯赫电气与计算机工程系，南加州大学）；Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820, USA（统计系，伊利诺伊大学厄巴纳-香槟分校）；Department of Mathematics, University of Southern California, Los Angeles, California 90089, USA（数学系，南加州大学）
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
Spin-Quantum Co. LTD（自旋量子有限公司）；School of Mathematics and Statistics, University of New South Wales（新南威尔士大学数学与统计学院）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）
In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of la- belled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham …
