arxiv preprint - EdgeFusion: On-Device Text-to-Image Generation

In this episode, we discuss EdgeFusion: On-Device Text-to-Image Generation by Thibault Castells, Hyoung-Kyu Song, Tairen Piao, Shinkook Choi, Bo-Kyeong Kim, Hanyoung Yim, Changgwun Lee, Jae Gon Kim, Tae-Ho Kim. The paper "EdgeFusion: On-Device Text-to-Image Generation" explores the difficulties of using Stable Diffusion models in text-to-image generation due to their intensive computational needs. It proposes a new, more efficient model based on a condensed version of Stable Diffusion, which incorporates novel strategies utilizing high-quality image-text pairs and an optimized distillation process specifically suited for the Latent Consistency Model. Their approach results in the ability to quickly generate high-quality, contextually accurate images on low-resource devices, achieving performance under one second per image generation.