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

Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 6159-6168

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.