ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Yuxi Ren Jie Wu Yanzuo Lu Huafeng Kuang Xin Xia Xionghui Wang Qianqian Wang Yixing Zhu Pan Xie Shiyin Wang Xuefeng Xiao Yitong Wang Min Zheng Lean Fu
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 to enhancing aesthetics and image-text alignment, while also
introducing a dense, pixel-level reward model tailored to foster coherence in
the output. Furthermore, we propose a pioneering adversarial and progressive
feedback learning strategy to expedite the model's inference speed. Through
extensive large-scale user evaluations, we demonstrate that ByteEdit surpasses
leading generative image editing products, including Adobe, Canva, and MeiTu,
in both generation quality and consistency. ByteEdit-Outpainting exhibits a
remarkable enhancement of 388% and 135% in quality and consistency,
respectively, when compared to the baseline model. Experiments also verfied
that our acceleration models maintains excellent performance results in terms
of quality and consistency.