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

In this episode we discuss DemoFusion: Democratising High-Resolution Image Generation With No $$$ by Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma. The paper introduces DemoFusion, a framework designed to enhance open-source Latent Diffusion Models (LDMs) for higher-resolution image generation. It incorporates Progressive Upscaling, Skip Residual, and Dilated Sampling to improve image quality while ensuring the process remains accessible to a broader audience. Additionally, DemoFusion's progressive approach allows for intermediate "previews" that support quick iterations of image prompts.