Conditional Dynamical Systems for Image Generation

[2608.14961] Conditional Dynamical Systems for Image Generation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Emerging Technologies arXiv:2608.14961 (cs) [Submitted on 15 Aug 2026] Title: Conditional Dynamical Systems for Image Generation Authors: Lianlong Sun , Chuan Liu , Tong Geng , Michael Huang View a PDF of the paper titled Conditional Dynamical Systems for Image Generation, by Lianlong Sun and 3 other authors View PDF HTML (experimental) Abstract: Image generation has been dominated by deep generative models running on GPUs, a paradigm whose computational and energy costs raise growing sustainability concerns. Emerging non-von Neumann computing substrates, including quantum, compute-in-memory, photonic, and thermodynamic platforms, promise greater efficiency, yet much of the existing work ports conventional neural architectures onto them and primarily accelerates operations such as matrix multiplication. This does not fully exploit a native capability of many emerging computing substrates: relaxation toward low-energy states can itself perform computation at negligible cost. We develop a family of continuous dynamical systems for image generation, built around this primitive to better harness its computational power. The proposed generator evolves an internal state under dynamics admitting an explicit Lyapunov energy and then renders the resulting state through a compact, class-agnostic decoder. For conditional generation, we introduce energy tilting: programmed pairwise interactions remain fixed and shared across classes, while a class-dependent linear field reshapes the energy without reprogramming the interaction array. An Ising-inspired design reaches a clean-FID of 9.71 on CIFAR-10 with 4096 spin variables. These results suggest that the energy-descending dynamics can serve directly as a generative computation and offer a promising path toward efficient generative tasks beyond GPUs. Subjects: Emerging Technologies (cs.ET) Cite as: arXiv:2608.14961 [cs.ET] (or arXiv:2608.14961v1 [cs.ET] for this version) https://doi.org/10.48550/arXiv.2608.14961 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chuan Liu [ view email ] [v1] Sat, 15 Aug 2026 01:28:04 UTC (292 KB) Full-text links: Access Paper: View a PDF of the paper titled Conditional Dynamical Systems for Image Generation, by Lianlong Sun and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.ET < prev | next > new | recent | 2026-08 Change to browse by: cs 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? ) 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

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