[2405.13729] ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2405.13729 (cs) [Submitted on 22 May 2024 ( v1 ), last revised 29 Apr 2026 (this version, v3)] Title: ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models Authors: Rui Xu , Jiepeng Wang , Hao Pan , Yang Liu , Xin Tong , Shiqing Xin , Changhe Tu , Taku Komura , Wenping Wang View a PDF of the paper titled ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models, by Rui Xu and 8 other authors View PDF HTML (experimental) Abstract: In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and for various structured generation tasks, additional attributes are combined to associate with data samples. We show that the space spanned by the combination of dimensions and attributes can be insufficiently covered by existing training schemes of diffusion generative models, potentially limiting test time performance. We present a simple fix to this problem by constructing stochastic processes that fully exploit the combinatorial structures, hence the name ComboStoc. Using this simple strategy, we show that network training is significantly accelerated across diverse data modalities, including images and 3D structured shapes. Moreover, ComboStoc enables a new way of test time generation which uses asynchronous time steps for different dimensions and attributes, thus allowing for varying degrees of control over them. Our code is available at: this https URL Comments: ACM Transactions on Graphics, SIGGRAPH 2026 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) Cite as: arXiv:2405.13729 [cs.LG] (or arXiv:2405.13729v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2405.13729 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rui Xu [ view email ] [v1] Wed, 22 May 2024 15:23:10 UTC (44,621 KB) [v2] Fri, 24 May 2024 07:05:59 UTC (44,577 KB) [v3] Wed, 29 Apr 2026 09:10:05 UTC (36,003 KB) Full-text links: Access Paper: View a PDF of the paper titled ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models, by Rui Xu and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-05 Change to browse by: cs cs.AI cs.CV cs.GR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from