ByteEdit: Boost, Comply and Accelerate Generative Image Editing
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.
6614c7167018e413bf5cd852
Yuxi Ren
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Jie Wu
6614c7167018e413bf5cd854
Yanzuo Lu
claimed_verified
2024-04-09T19:48:43.978Z
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Yanzuo Lu
user
oliveryanzuolu
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Huafeng Kuang
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Xin Xia
claimed_verified
2024-04-13T18:02:14.926Z
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XinXia
user
XiaXin-Aloys
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Xionghui Wang
claimed_verified
2025-08-29T08:04:23.187Z
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/avatars/f02399e5149da269b394114b3ff43ef8.svg
Xionghui Wang
user
XionghuiWang
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Qianqian Wang
6614c7167018e413bf5cd859
Yixing Zhu
6614c7167018e413bf5cd85a
Pan Xie
6614c7167018e413bf5cd85b
Shiyin Wang
6614c7167018e413bf5cd85c
Xuefeng Xiao
6614c7167018e413bf5cd85d
Yitong Wang
6614c7167018e413bf5cd85e
Min Zheng
6614c7167018e413bf5cd85f
Lean Fu
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
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.
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.
diffusion-based generative image editing image outpainting inpainting image reward models pixel-level reward model adversarial feedback learning progressive feedback learning