DemoFusion: Democratising High-Resolution Image Generation With No $$$
Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Abstract
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but due to the enormous capital investment required for training it is increasingly centralised to a few large corporations and hidden behind paywalls. This paper aims to democratise high-resolution GenAI by advancing the frontier of high-resolution generation while remaining accessible to a broad audience. We demonstrate that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation. Our novel DemoFusion framework seamlessly extends open-source GenAI models employing Progressive Upscaling Skip Residual and Dilated Sampling mechanisms to achieve higher-resolution image generation. The progressive nature of DemoFusion requires more passes but the intermediate results can serve as "previews" facilitating rapid prompt iteration.
BibTeX
@inproceedings{cvpr2024_demofusiondemocr,
title = {DemoFusion: Democratising High-Resolution Image Generation With No $$$},
author = {Ruoyi Du and Dongliang Chang and Timothy Hospedales and Yi-Zhe Song and Zhanyu Ma},
booktitle = {CVPR 2024},
year = {2024}
}