arXiv AI June 10, 2026 By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 2

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502. 04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more.

By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana

diffusion benchmarks

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.

By Hasan Amin, Yuan Gao, Yaser Souri, Subhojit Som, Ming Yin, Rajiv Khanna, Xia Song

llms diffusion efficiency

arXiv AI

Jun 2

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

arXiv:2503. 07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals.

By Jiaming Song, Linqi Zhou

diffusion efficiency

arXiv Machine Learning

Jul 28

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

arXiv:2607. 23226v1 Announce Type: new Abstract: Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterization, and numerical discretization jointly dictate generative quality remains underdeveloped.

By Jinshu Huang, Yiming Jiang, Chunlin Wu

diffusion

arXiv AI

Jul 24

Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

arXiv:2607. 20913v1 Announce Type: new Abstract: Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the pretrained model's broad generative capability.

By Yi Xiong, Yuan-Yuan Cheng, Xiao-Ming Fu

diffusion fine-tuning

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.

By Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood

diffusion fine-tuning

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Efficient Weighted Sampling via Score-based Generative Models

Consistent Diffusion Language Models

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

Integration Matters: Rollout-Based Training for Constrained Diffusion Models