Diffusion Sampling

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Period ending 2026-09-21

7 new papers

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Period ending 2026-09-07

3 new papers

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82 papers

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Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics
May 7, 2026Muhammad H. Ashiq, Samanyu Arora, Abhinav N. Harish +3Diffusion Sampling Denoising Diffusion Probabilistic Model
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
May 7, 2026Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2Posterior Sampling Diffusion Sampling
MotionGRPO: Overcoming Low Intra-Group Diversity in GRPO-Based Egocentric Motion Recovery
May 7, 2026Nanjie Yao, Junlong Ren, Wenhao Shen +1Avatar Reconstruction Ego-Motion
Conditional Diffusion Sampling
May 5, 2026Francisco M. Castro-Macías, Pablo Morales-Álvarez, Saifuddin Syed +3Diffusion Sampling
Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
May 5, 2026Aaron Havens, Brian Karrer, Neta ShaulDiffusion Sampling Riemannian Manifolds
Tempered Guided Diffusion
May 5, 2026Andreas Makris, Paul Fearnhead, Chris NemethDiffusion Sampling Diffusion Priors
Information-geometric adaptive sampling for graph diffusion
Apr 30, 2026Yuhui Lu, Wenjing Liu, Kun ZhanDiffusion Sampling Adaptive Sampling
$Z^2$ -Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion ModelsZ^2-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models
Apr 26, 2026Haosen Li, Wenshuo Chen, Shaofeng Liang +3Diffusion Sampling Trajectory Prediction
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Apr 20, 2026Enshu Liu, Xuefei Ning, Yu Wang +1Diffusion Language Models Diffusion Sampling
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
Apr 20, 2026Kijung Jeon, Michael Muehlebach, Molei TaoDiffusion Sampling Inequality Constraints
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Apr 18, 2026Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen +1Ill-Posed Inverse Problem Inverse Problem
Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling
Apr 8, 2026Andrew Millard, Zheng Zhao, Henrik PedersenDiffusion Sampling Advection-Reaction-Diffusion
Training-Free Refinement of Flow Matching with Divergence-based Sampling
Apr 6, 2026Yeonwoo Cha, Jaehoon Yoo, Semin Kim +3Generative Flow Networks Diffusion Sampling
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Feb 18, 2026Yu Xie, Ludwig Winkler, Lixin Sun +10Diffusion Sampling Molecular Dynamics
Score-based diffusion models for severely ill-posed problems in diffuse optical tomography
Feb 3, 2026Fabian Schneider, Meghdoot Mozumder, Konstantin Tamarov +4Ill-Posed Inverse Problem Score-Based Diffusion Model
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Dec 28, 2025Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3Bayesian Experimental Design Bayesian
EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance
Dec 19, 2025Ankit Yadav, Ta Duc Huy, Lingqiao LiuDiffusion Sampling Generative Quality
MACS: Measurement-Aware Consistency Sampling for Inverse Problems
Oct 2, 2025Amirreza Tanevardi, Pooria Abbas Rad Moghadam, Seyed Mohammad Eshtehardian +2Ill-Posed Inverse Problem Diffusion Sampling
Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence
Jul 1, 2025Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. LermusiauxTurbulence Super-Resolution Framework
Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations
Jun 25, 2025Shunqi Mao, Wei Guo, Chaoyi Zhang +3Diffusion Sampling Diffusion Processes
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
Nov 26, 2024Jinho Chang, Changsun Lee, Hyungjin Chung +1Diffusion Sampling Contrastive Loss
Prediction--Loss Alignment for Sampler--Robust Flow Matching Training
Date pendingJiadong Hong, Lei Liu, Xinyu Bian +2Diffusion Sampling Stochastic Gradient Descent

May 7, 2026 · cs.LGJ/K move · Enter open · S save

Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics

Muhammad H. Ashiq, Samanyu Arora, Abhinav N. Harish, Ishaan Kharbanda+2

University of Wisconsin-Madison, Madison, WI, USA.

We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM) and the deterministic Denoising Diffusion Implicit Model (DDIM). We analyze the reverse ODE (DDIM) and SDE (DDPM) for a Gaussian mixture target, proving that after a critical time

τ

, (a) DDIM can become stuck on the segment connecting the two nearest modes and (b) DDPM stochasticity helps it become unstuck from this region, thus avoiding hallucination. Our empirical validation verifies that DDPM has a significantly lower hallucination rate than DDIM when this region is entered. Building on our observations, we exhibit how using additional stochastic steps can help DDIM avoid hallucinations and offer new insights on how to design improved samplers.

Diffusion Sampling Denoising Diffusion Probabilistic Model Hallucinations Theoretical Foundations

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