ByteEdit: Boost, Comply and Accelerate Generative Image Editing

arXiv:2404.04860v1 Announce Type: new
Abstract: Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ByteEdit, an innovative feedback learning framework meticulously designed to Boost, Comply, and Accelerate Generative Image Editing tasks. ByteEdit seamlessly integrates image reward models dedicated …

abstract arxiv boost challenges cs.cv diffusion editing efficiency generative iii image inpainting landscape outpainting quality tasks type