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Locality in Image Diffusion Models Emerges from Data Statistics
Artem Lukoianov, Chenyang Yuan, Justin Solomon, Vincent Sitzmann
https://arxiv.org/abs/2509.09672 https://…

Locality in Image Diffusion Models Emerges from Data Statistics
Among generative models, diffusion models are uniquely intriguing due to the existence of a closed-form optimal minimizer of their training objective, often referred to as the optimal denoiser. However, diffusion using this optimal denoiser merely reproduces images in the training set and hence fails to capture the behavior of deep diffusion models. Recent work has attempted to characterize this gap between the optimal denoiser and deep diffusion models, proposing analytical, training-free mode…

Data-driven generative simulation of SDEs using diffusion models
Xuefeng Gao, Jiale Zha, Xun Yu Zhou
https://arxiv.org/abs/2509.08731 https://arxiv.org/pdf…

Data-driven generative simulation of SDEs using diffusion models
This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models, a class of generative AI models commonly employed in image and video applications. Unlike the traditional Monte Carlo methods for simulating SDEs, which require explicit specifications of the drift and diffusion coefficients, our method takes a model-free, data-driven approach. Given a finite set of sample paths from an SDE, we utilize conditional diffusion…

🇺🇦 Auf radioeins läuft...
Joy Division:
🎵 Atmosphere
#NowPlaying #JoyDivision
https://mrlckmshp.bandcamp.com/track/innocence-division-beautiful-athmosphere-joy-division-atmosphere-vs-innocence-mission-beautiful-change
https://open.spotify.com/track/0NBgmP6Yg5xZYsnIZ0pGdo

Death of Charlie Kirk lays bare deep US political divisions (Reuters)
https://www.reuters.com/world/us/death-charlie-kirk-lays-bare-deep-us-political-divisions-2025-09-11/
http://www.memeorandum.com/250911/p1#a250911p1

Joint Model-based Model-free Diffusion for Planning with Constraints
Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Yongxin Chen, Danfei Xu, Shreyas Kousik
https://arxiv.org/abs/2509.08775

Joint Model-based Model-free Diffusion for Planning with Constraints
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Naively integrating these modules presents compatibility challenges when diffusion's multi-modal outputs behave adversarially to optimization-based modules. To address this, we introduce Joint Model-based Model-free Diffusion (JM2D), a novel generative modeling framework. JM2D fo…

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
Dohun Lee, Hyeonho Jeong, Jiwook Kim, Duygu Ceylan, Jong Chul Ye
https://arxiv.org/abs/2509.09547

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
Video diffusion models have advanced rapidly in the recent years as a result of series of architectural innovations (e.g., diffusion transformers) and use of novel training objectives (e.g., flow matching). In contrast, less attention has been paid to improving the feature representation power of such models. In this work, we show that training video diffusion models can benefit from aligning the intermediate features of the video generator with feature representations of pre-trained vision enc…

Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
Anjie Qiao, Zhen Wang, Chuan Chen, DeFu Lian, Enhong Chen
https://arxiv.org/abs/2509.09451

Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
Controllable molecular graph generation is essential for material and drug discovery, where generated molecules must satisfy diverse property constraints. While recent advances in graph diffusion models have improved generation quality, their effectiveness in multi-conditional settings remains limited due to reliance on joint conditioning or continuous relaxations that compromise fidelity. To address these limitations, we propose Composable Score-based Graph Diffusion model (CSGD), the first mo…

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Zezeng Li, Rui Yang, Ruochen Chen, ZhongXuan Luo, Liming Chen
https://arxiv.org/abs/2508.06266

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Diffusion policies have recently emerged as a powerful class of visuomotor controllers for robot manipulation, offering stable training and expressive multi-modal action modeling. However, existing approaches typically treat action generation as an unconstrained denoising process, ignoring valuable a priori knowledge about geometry and control structure. In this work, we propose the Adaptive Diffusion Policy (ADP), a test-time adaptation method that introduces two key inductive biases into the …

Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
Xiaodong Wang, Ping Wang, Zhangyuan Li, Xin Yuan
https://arxiv.org/abs/2509.09365 h…

Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
We explore the connection between Plug-and-Play (PnP) methods and Denoising Diffusion Implicit Models (DDIM) for solving ill-posed inverse problems, with a focus on single-pixel imaging. We begin by identifying key distinctions between PnP and diffusion models-particularly in their denoising mechanisms and sampling procedures. By decoupling the diffusion process into three interpretable stages: denoising, data consistency enforcement, and sampling, we provide a unified framework that integrates…

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers
https://arxiv.org/abs/2509.08628

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source…