The Weight Is Over：消费级 GPU 上的交互式扩散推理

Computer Science > Machine Learning arXiv:2609.21849 (cs) [Submitted on 18 Sep 2026] Title:The Weight Is Over - Interactive Diffusion on Consumer GPUs Authors:Frieder Ganz, Maximilian Müller View a PDF of the paper titled The Weight Is Over - Interactive Diffusion on Consumer GPUs, by Frieder Ganz and Maximilian M\"uller View PDF HTML (experimental) Abstract:On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry, latency-sensitive, and require orchestrating an embedder, a transformer, a decoder, and often further postprocessing that is not as standardized as LLM inference loops are. We navigate the trade-off between performance, quality, and model footprint to reach as many client devices in the wild as possible. We make three contributions: an embedding translator that maps a small text encoder into a large encoder space to cut weight and latency; a reproducible sweep recipe for navigating the speed/quality/memory triangle in diffusion pipelines; and an interactive on-device image generation editor achieving sub-second TTFI on recent GPUs. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Performance (cs.PF) Cite as: arXiv:2609.21849 [cs.LG] (or arXiv:2609.21849v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.21849 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3829339.3847852 Focus to learn more DOI(s) linking to related resources Submission history From: Frieder Ganz [view email] [v1] Fri, 18 Sep 2026 14:42:55 UTC (3,327 KB) Full-text links: Access Paper: View a PDF of the paper titled The Weight Is Over - Interactive Diffusion on Consumer GPUs, by Frieder Ganz and Maximilian M\"uller View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs cs.CV cs.PF 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?)