Lenovo licenses fast, private image-generation model developed through Surrey collaboration
An AI image-generation model that will bring high-quality, private image creation to Lenovo’s upcoming on-device AI has been developed by researchers at the University of Surrey’s Institute for People-Centred AI in collaboration with artificial intelligence company Stability AI. The technology, called Stable Diffusion 3.5 Flash (SD3.5-Flash), enables fast, unlimited text-to-image creation to run directly on consumer devices without relying on cloud connection.
High-fidelity samples from the 4-step model
Unlike conventional AI diffusion models that require 30 to 50 processing steps, SD3.5-Flash can generate images in just four, making it both significantly faster and lightweight enough to run on mobile phones, tablets and laptops – while also reducing the energy and water demands associated with cloud-based AI.
Lenovo has now licensed the model from Stability AI for integration into its upcoming Personal Ambient Intelligence platform, Qira.
The model was created by Surrey doctoral researcher Hmrishav Bandyopadhyay during a university placement internship at Stability AI, with the core idea shaped through work in the SketchX Lab at the Surrey Institute for People-Centred AI (PAI).
The work builds on the team’s SD3.5-Flash research paper, which outlines how large diffusion models – generative AI that creates images – can be compressed into highly efficient versions without compromising image quality. It forms part of a wider programme of research within PAI’s SketchX Lab to make generative AI faster and more practical for real-world use, following earlier projects such as NitroFusion that explored high-fidelity single-step diffusion.
The work also highlights the opportunities available to Surrey students to gain hands-on industry experience during their studies, contributing to real-world projects while developing the technical and professional skills needed for future careers that serve society.
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Notes to editors
- Professor Yi-Zhe Song and Hmrishav Bandyopadhyay are available for interview; please contact mediarelations@surrey.ac.uk to arrange
- The full paper can be found here: https://arxiv.org/abs/2509.21318
- Headshot images and samples from the model are available upon request
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