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DemoFusion: Democratising High-Resolution Image Generation With No $$$

Authors :
Du, Ruoyi
Chang, Dongliang
Hospedales, Timothy
Song, Yi-Zhe
Ma, Zhanyu
Publication Year :
2023

Abstract

High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls. This paper aims to democratise high-resolution GenAI by advancing the frontier of high-resolution generation while remaining accessible to a broad audience. We demonstrate that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation. Our novel DemoFusion framework seamlessly extends open-source GenAI models, employing Progressive Upscaling, Skip Residual, and Dilated Sampling mechanisms to achieve higher-resolution image generation. The progressive nature of DemoFusion requires more passes, but the intermediate results can serve as "previews", facilitating rapid prompt iteration.<br />Comment: Project Page: https://ruoyidu.github.io/demofusion/demofusion.html

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.16973
Document Type :
Working Paper