title: InstantStyle-Plus: Style Transfer with Content-Preserving in
    Text-to-Image Generation
summary: Style transfer is an inventive process designed to create an image that
  maintains the essence of the original while embracing the visual style of
  another. Although diffusion models have demonstrated impressive generative
  power in personalized subject-driven or style-driven applications, existing
  state-of-the-art methods still encounter difficulties in achieving a seamless
  balance between content preservation and style enhancement. For example,
  amplifying the style's influence can often undermine the structural integrity
  of the content. To address these challenges, we deconstruct the style transfer
  task into three core elements: 1) Style, focusing on the image's aesthetic
  characteristics; 2) Spatial Structure, concerning the geometric arrangement and
  composition of visual elements; and 3) Semantic Content, which captures the
  conceptual meaning of the image. Guided by these principles, we introduce
  InstantStyle-Plus, an approach that prioritizes the integrity of the original
  content while seamlessly integrating the target style. Specifically, our method
  accomplishes style injection through an efficient, lightweight process,
  utilizing the cutting-edge InstantStyle framework. To reinforce the content
  preservation, we initiate the process with an inverted content latent noise and
  a versatile plug-and-play tile ControlNet for preserving the original image's
  intrinsic layout. We also incorporate a global semantic adapter to enhance the
  semantic content's fidelity. To safeguard against the dilution of style
  information, a style extractor is employed as discriminator for providing
  supplementary style guidance. Codes will be available at
  https://github.com/instantX-research/InstantStyle-Plus.
paper_authors._id: 6683c0bd21b4b78318103968
paper_authors.name: Haofan Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-30T09:37:33.259Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 6683c0bd21b4b78318103969
paper_authors.name: Peng Xing
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-30T07:18:15.742Z
paper_authors._id: 649f7b742bf2e21d955c1067
paper_authors.avatarUrl: /avatars/270026405d5640886a86f961a001b057.svg
paper_authors.fullname: xing
paper_authors.type: user
paper_authors.user: xingpng
paper_authors._id: 6683c0bd21b4b7831810396a
paper_authors.name: Renyuan Huang
paper_authors._id: 6683c0bd21b4b7831810396b
paper_authors.name: Hao Ai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-02T21:08:58.061Z
paper_authors._id: 6464d47c07a7f745c59ce7f4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6464d47c07a7f745c59ce7f4/6mOib8MSaQ5RejmJboIya.jpeg
paper_authors.fullname: aihao2000
paper_authors.type: user
paper_authors.user: AisingioroHao0
paper_authors._id: 6683c0bd21b4b7831810396c
paper_authors.name: Qixun Wang
paper_authors._id: 6683c0bd21b4b7831810396d
paper_authors.name: Xu Bai
paper_title: InstantStyle-Plus: Style Transfer with Content-Preserving in
    Text-to-Image Generation
paper_summary: Style transfer is an inventive process designed to create an image that
  maintains the essence of the original while embracing the visual style of
  another. Although diffusion models have demonstrated impressive generative
  power in personalized subject-driven or style-driven applications, existing
  state-of-the-art methods still encounter difficulties in achieving a seamless
  balance between content preservation and style enhancement. For example,
  amplifying the style's influence can often undermine the structural integrity
  of the content. To address these challenges, we deconstruct the style transfer
  task into three core elements: 1) Style, focusing on the image's aesthetic
  characteristics; 2) Spatial Structure, concerning the geometric arrangement and
  composition of visual elements; and 3) Semantic Content, which captures the
  conceptual meaning of the image. Guided by these principles, we introduce
  InstantStyle-Plus, an approach that prioritizes the integrity of the original
  content while seamlessly integrating the target style. Specifically, our method
  accomplishes style injection through an efficient, lightweight process,
  utilizing the cutting-edge InstantStyle framework. To reinforce the content
  preservation, we initiate the process with an inverted content latent noise and
  a versatile plug-and-play tile ControlNet for preserving the original image's
  intrinsic layout. We also incorporate a global semantic adapter to enhance the
  semantic content's fidelity. To safeguard against the dilution of style
  information, a style extractor is employed as discriminator for providing
  supplementary style guidance. Codes will be available at
  https://github.com/instantX-research/InstantStyle-Plus.
paper_ai_summary: InstantStyle-Plus enhances style transfer by maintaining content integrity and semantic fidelity while efficiently integrating target styles using advanced components like ControlNet and semantic adapters.
paper_ai_keywords: diffusion models style transfer InstantStyle ControlNet semantic content semantic adapter style extractor discriminator