OpenAI Blog
Jun 20, 2024
Consistency Models
Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation.
Simplifying, stabilizing, and scaling continuous-time consistency models
We’ve simplified, stabilized, and scaled continuous-time consistency models, achieving comparable sample quality to leading diffusion models, while using only two sampling steps.
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arXiv Machine Learning
Jul 9
Diffusion Models in Simulation-Based Inference: A Tutorial Review
arXiv:2512. 20685v3 Announce Type: replace-cross Abstract: Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data.
arXiv Machine Learning
Jul 3
A Mathematical Introduction to Diffusion Models
arXiv:2607. 01693v1 Announce Type: new Abstract: These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics to modern diffusion samplers, their error analysis, and inference-time control.
arXiv Machine Learning
Jun 2
Consistent Diffusion Language Models
arXiv:2605. 00161v2 Announce Type: replace Abstract: Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet practical gains remain elusive as high-quality samples still demand hundreds of refinement steps.
arXiv AI
Jul 1
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
arXiv:2510. 17917v2 Announce Type: replace-cross Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model.
Hugging Face Trending Papers
Jun 11
Uncertainty Estimation for Molecular Diffusion Models
Diffusion models have seen wide adoption for 3D molecular generation, yet they offer no principled signal of when a generated molecule is likely to be of low quality. We propose a post-hoc method for estimating per-sample uncertainty in pretrained molecular diffusion models.
arXiv Machine Learning
Jun 2
Error Bounds for a Diffusion Model-Based Drift Estimator
arXiv:2606. 02115v1 Announce Type: cross Abstract: Parameter estimation in stochastic differential equations is a classical statistical problem of much importance in many scientific fields.
Hugging Face Trending Papers
Jun 1
Error Bounds for a Diffusion Model-Based Drift Estimator
Parameter estimation in stochastic differential equations is a classical statistical problem of much importance in many scientific fields. Recent work of Tapia Costa et al.
arXiv Machine Learning
Jun 2
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
arXiv:2606. 00309v1 Announce Type: new Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models.
arXiv Machine Learning
Jul 14
Likelihood Matching for Diffusion Models
arXiv:2508. 03636v3 Announce Type: replace-cross Abstract: We propose a Likelihood Matching approach for training diffusion models by first establishing an equivalence between the likelihood of the target data distribution and a likelihood along the sample path of the reverse diffusion.
arXiv Machine Learning
Jun 16
Temporal Difference Learning for Diffusion Models
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
arXiv AI
Jul 17
Integration Matters: Rollout-Based Training for Constrained Diffusion Models
arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.