Universal Local Error and Realized Amplification for the First-Order EDM Predictor

arXiv:2610.10190 · stat.ML, cs.LG · Submitted 2026-10-07 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/nbrosse/edm-error-propagation-code

Terminology

• Accuracy — 11× in this paper · explained in 1 episode
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Wasserstein Distance — 9× in this paper · explained in 4 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Trace — 8× in this paper · explained in 2 episodes
• Generative Models — 7× in this paper · explained in 2 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Non-asymptotic Analysis — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
• Error Bounds for Flow Matching Methods
• Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
• Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance
• Boundary-layer asymptotics for Gaussian-smoothed singular measures
• Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
• Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers
• Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
• The probability flow ODE is provably fast
• Lipschitz-Guided Design of Interpolation Schedules in Generative Models
• Convergence of denoising diffusion models under the manifold hypothesis
• Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
• Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance
• An Introduction to Flow Matching and Diffusion Models
• Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
• Elucidating the Design Space of Diffusion-Based Generative Models
• Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process
• Non-asymptotic error bounds for probability flow ODEs under weak log-concavity
• Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
• Convergence of score-based generative modeling for general data distributions

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