title: ByteEdit: Boost, Comply and Accelerate Generative Image Editing
summary: 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.
paper_authors._id: 6614c7167018e413bf5cd852
paper_authors.name: Yuxi Ren
paper_authors._id: 6614c7167018e413bf5cd853
paper_authors.name: Jie Wu
paper_authors._id: 6614c7167018e413bf5cd854
paper_authors.name: Yanzuo Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-09T19:48:43.978Z
paper_authors._id: 6614cbd40bbea65e71db4e1f
paper_authors.avatarUrl: /avatars/ca8ff74887bbf8eb3f5b04ae9bb6d05b.svg
paper_authors.fullname: Yanzuo Lu
paper_authors.type: user
paper_authors.user: oliveryanzuolu
paper_authors._id: 6614c7167018e413bf5cd855
paper_authors.name: Huafeng Kuang
paper_authors._id: 6614c7167018e413bf5cd856
paper_authors.name: Xin Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-13T18:02:14.926Z
paper_authors._id: 63089a78ff78e2aead8d10e7
paper_authors.avatarUrl: /avatars/f326fd08abee7e31599a78923be30003.svg
paper_authors.fullname: XinXia
paper_authors.type: user
paper_authors.user: XiaXin-Aloys
paper_authors._id: 6614c7167018e413bf5cd857
paper_authors.name: Xionghui Wang
paper_authors._id: 6614c7167018e413bf5cd858
paper_authors.name: Qianqian Wang
paper_authors._id: 6614c7167018e413bf5cd859
paper_authors.name: Yixing Zhu
paper_authors._id: 6614c7167018e413bf5cd85a
paper_authors.name: Pan Xie
paper_authors._id: 6614c7167018e413bf5cd85b
paper_authors.name: Shiyin Wang
paper_authors._id: 6614c7167018e413bf5cd85c
paper_authors.name: Xuefeng Xiao
paper_authors._id: 6614c7167018e413bf5cd85d
paper_authors.name: Yitong Wang
paper_authors._id: 6614c7167018e413bf5cd85e
paper_authors.name: Min Zheng
paper_authors._id: 6614c7167018e413bf5cd85f
paper_authors.name: Lean Fu
paper_title: ByteEdit: Boost, Comply and Accelerate Generative Image Editing
paper_summary: 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.
paper_ai_summary: ByteEdit, a feedback learning framework, enhances generative image editing through image reward models, pixel-level coherence, and adversarial learning, significantly improving quality, consistency, and speed compared to existing products.
paper_ai_keywords: diffusion-based generative image editing image outpainting inpainting image reward models pixel-level reward model adversarial feedback learning progressive feedback learning