Search: diffusion models

More than 1,000 papers match: here are the 1,000 most recent, ranked by relevance.

• AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis

  Shipeng Cao, Biao Qian, Haipeng Liu, Yang Wang, Meng Wang · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  Text-to-image synthesis has made significant progress, benefiting from the strong generative capabilities of diffusion models. However, these models struggle to achieve precise text-to-image alignment within cross-attention maps during the denoising process. Existing works primarily focus on inter-s…

• Injecting Image Guidance into Text-Conditioned Diffusion Models at Inference

  Agata Żywot, Iason Skylitsis, Thijmen Nijdam, Zoe Tzifa-Kratira, Derck Prinzhorn, Konrad Szewczyk, Aritra Bhowmik · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  Text-to-image diffusion models like Stable Diffusion generate high-quality images from text, but lack a way to inject visual guidance (e.g. sketches, styles) at inference without retraining. Existing methods either require computationally expensive fine-tuning or rely on style transfer techniques th…

• Trajectory-Consistent Calibration for Cache-Accelerated Diffusion Models

  Mingyu Liang, Dingkun Xu, Jingwei Xu · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion Transformers require repeated denoiser evaluations during iterative sampling, making inference computationally expensive. Cache-based acceleration reduces this cost by reusing intermediate representations across denoising steps, but can introduce representation deviations and degrade gener…

• Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces

  Yaoxuan Feng, Yuxin Li, Weijiang Lv, Zixuan Zhao, Yubiao Wang, Wenchao Chen, Bo Chen, Hongwei Liu · 26 May 2026 · Anomaly Detection Techniques and Applications

  Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to increasingly complex and heterogeneous normal distributions. To address this challenge, we propose DPDiff-AD, a Dual Prototy…

• A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models

  Jiayi Fu, Yuxia Wang · 26 May 2026 · Model Reduction and Neural Networks

  This tutorial develops diffusion models from the viewpoint of differential equations. We begin with the conditional Gaussian forward process and show that this path admits both an ordinary differential equation (ODE) representation and a stochastic differential equation (SDE) representation. Averagi…

• Dale meets Langevin: A Multiplicative Denoising Diffusion Model

  Nishanth Shetty, Madhava Prasath, Chandra Sekhar Seelamantula · 26 May 2026 · Stochastic Gradient Optimization Techniques

  Exponentiated gradient descent (EGD), a biologically motivated optimisation algorithm that respects Dale's law, produces log-normally distributed synaptic weights at convergence, in alignment with experimental observations in neuroscience. Since the marginal distribution of geometric Brownian motion…

• A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective

  Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen, Xiaoge Zhang, Kaiyu Tang, Xiao Li, Jing Li · 26 May 2026 · Privacy-Preserving Technologies in Data

  Diffusion models have shown strong performance in generating high-quality tabular data, but they carry privacy risks by reproducing exact training samples. While prior work focuses on dataset-level augmentation to reduce memorization, little is known about which individual samples contribute most. W…

• Paris 2.0: A Decentralized Diffusion Model for Video Generation

  Ali Rouzbayani, Bidhan Roy, Marcos Villagra, Zhiying Jiang · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv:2510.03434), the first ever open-weight Decentralized Diffusion Model (DDM), which showed that image generation can be trained without a monolithic G…

• Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information

  Zachary James, Joseph Guinness, Arthur DeGaetano · 26 May 2026 · Meteorological Phenomena and Simulations

  Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitation. The latest versions of the Global Forecast System (GFS) and related Global Ensemble Forecast System (GEFS) have exhibited a bias towards underesti…

• Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

  Qingyuan Zeng, Pengxiang Cai, Zixin Guan, Ziyang Chen, Anglin Liu, Lang Qin, Xinyao Lai, Jintai Chen · 26 May 2026 · Computational Drug Discovery Methods

  Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation because one candidate must satisfy two binding requirements while preserving drug-likeness and synthesizability. Existing du…

• Local MAP Sampling for Diffusion Models

  Shaorong Zhang, Rob Brekelmans, Greg Ver Steeg · 26 May 2026 · Advanced Mathematical Modeling in Engineering

  Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from $p(x_0 \mid y)$. While posterior sampling is valuable for capturing uncertainty and multi-modality, many classical and practical inverse problem settings ultimately prioritize accurate poi…

• Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model

  Donghwna Lee, Kirok Kim, Jisu Lee, Kyungha Min, Wooju Kim · 26 May 2026 · Video Surveillance and Tracking Methods

  Pose-Guided Person Image Synthesis (PGPIS) aims to generate human images in specified poses while preserving the identity and appearance of a source image. This technology facilitates diverse applications, including virtual try-on, digital avatars, animation, and sign language generation. Despite th…

• Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models

  Saemi Moon, Suhyeon Jun, Seoyeon Lee, Dongwoo Kim · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  Concept unlearning aims to erase a target concept from a pretrained text-to-image diffusion model without retraining. Closed-form methods are attractive in this setting because they apply a single deterministic edit to the cross-attention weights and add no inference-time cost. Existing closed-form …

• Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

  Qingyuan Zeng, Pengxiang Cai, Zixin Guan, Ziyang Chen, Anglin Liu, Lang Qin, Xinyao Lai, Jintai Chen · 26 May 2026 · Computational Drug Discovery Methods

  Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation because one candidate must satisfy two binding requirements while preserving drug-likeness and synthesizability. Existing du…

• Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning

  Ziheng Cheng, Yixiao Huang, Hanlin Zhu, Haoran Geng, Somayeh Sojoudi, Jitendra Malik, Pieter Abbeel, Xin Guo · 26 May 2026 · Reinforcement Learning in Robotics

  Diffusion models are increasingly used as powerful conditional generators, yet real deployments often involve multiple target distributions arising from different tasks, e.g., diverse prompt domains in text-to-image generation, or multiple environments in robotics with diffusion policies. This natur…

• Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo

  Weixin Wang, Yu Yang, Wei Deng, Pan Xu · 26 May 2026 · Advanced Neuroimaging Techniques and Applications

  We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its weights. Recent Sequential Monte Carlo (SMC)-based steering methods approximate reward-tilted target distributions in a principled way, but their prop…

• fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis

  Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew · 25 May 2026 · Functional Brain Connectivity Studies

  Diagnosing Major Depressive Disorder (MDD) from functional magnetic resonance imaging (fMRI) using functional connectivity (FC) analysis requires large amounts of labeled data that are scarce in clinical settings. Existing augmentation methods synthesize FC matrices, which compress fMRI recordings i…

• Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling

  Rui Wang, Edoardo Pasetto, Amer Delilbasic, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro · 25 May 2026 · Tropical and Extratropical Cyclones Research

  Statistical downscaling is a crucial component of the weather modeling field, where high-resolution outputs must be reconstructed from coarse-resolution inputs with the full cost of dynamical refinement. In this work, we investigate a hybrid quantum-classical corrective diffusion model for probabili…

• Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models

  Egor Lifar, Semyon Savkin, Timur Garipov, Shangyuan Tong, Tommi Jaakkola · 25 May 2026 · Generative Adversarial Networks and Image Synthesis

  In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects and handle more complex conditioning beyond their original capabilities. Our method employs a compact trainable network designed to coordinate the den…

• Learned Relay Representations for Forward-Thinking Discrete Diffusion Models

  Benjamin Rozonoyer, Jacopo Minniti, Dhruvesh Patel, Neil Band, Avishek Joey Bose, Tim G. J. Rudner, Andrew McCallum · 25 May 2026 · Generative Adversarial Networks and Image Synthesis

  When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, forcing every subsequent refinement step to recompute the valuable internal information stored as model representations. To avoid a hard reset betwee…

• PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

  Jose Edgar Hernandez Cancino Estrada, Mauro Díaz Lupone, Žiga Emeršič, Vitomir Štruc, Peter Peer, Darian Tomašević · 22 May 2026 · Generative Adversarial Networks and Image Synthesis

  Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as models may continue to synthesize individuals despite their right to be forgotten. While machine unlearning has been extensively studied for concept and …

• Broken Memories: Detecting and Mitigating Memorization in Diffusion Models with Degraded Generations

  Yuanmin Huang, Mi Zhang, Chen Chen, Feifei Li, Geng Hong, Xiaoyu You, Min Yang · 22 May 2026 · Generative Adversarial Networks and Image Synthesis

  While diffusion models excel at generating high-quality images, their tendency to memorize training data poses significant privacy and copyright risks. In this work, we for the first time identify that memorization induces internal numerical instability, often manifesting as visually ``broken'' arti…

• Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness

  Hangyeol Lee, Hyojeong Lee, Joo-Young Kim · 22 May 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion Transformers (DiTs) achieve superior image generation quality but suffer from quadratic computational complexity relative to token count. While various token reduction (TR) methods have been proposed to mitigate this cost, they overlook the primary objective of generative models: minimizin…

• PolycubeNet: A Dual-latent Diffusion Model for Polycube-Based Hexahedral Mesh Generation

  Lu He, Qitao Deng, Junjiang Deng, Liangbin Deng, Yanjun Liang, Wenting Yang, Guoqiang Wang, Na Lei · 22 May 2026 · 3D Shape Modeling and Analysis

  Hexahedral meshes are widely used in simulation pipelines, yet automatic generation remains challenging for complex CAD geometries. Polycube-based hexahedral meshing is a representative approach due to its regular, parameterization-friendly structure, but existing polycube construction methods often…

• Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model

  Suya Li, Kaushik Dutta, Debojyoti Pal, Jingqin Luo, Kooresh I. Shoghi · 22 May 2026 · Medical Imaging Techniques and Applications

  Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but conventional physics-based simulation approaches are computationally intensive, limited in anatomical variability, and often fail to capture heterog…