title: InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image
    Generation
summary: Tuning-free diffusion-based models have demonstrated significant potential in
  the realm of image personalization and customization. However, despite this
  notable progress, current models continue to grapple with several complex
  challenges in producing style-consistent image generation. Firstly, the concept
  of style is inherently underdetermined, encompassing a multitude of elements
  such as color, material, atmosphere, design, and structure, among others.
  Secondly, inversion-based methods are prone to style degradation, often
  resulting in the loss of fine-grained details. Lastly, adapter-based approaches
  frequently require meticulous weight tuning for each reference image to achieve
  a balance between style intensity and text controllability. In this paper, we
  commence by examining several compelling yet frequently overlooked
  observations. We then proceed to introduce InstantStyle, a framework designed
  to address these issues through the implementation of two key strategies: 1) A
  straightforward mechanism that decouples style and content from reference
  images within the feature space, predicated on the assumption that features
  within the same space can be either added to or subtracted from one another. 2)
  The injection of reference image features exclusively into style-specific
  blocks, thereby preventing style leaks and eschewing the need for cumbersome
  weight tuning, which often characterizes more parameter-heavy designs.Our work
  demonstrates superior visual stylization outcomes, striking an optimal balance
  between the intensity of style and the controllability of textual elements. Our
  codes will be available at https://github.com/InstantStyle/InstantStyle.
paper_authors._id: 660e1590076a6255659f92b0
paper_authors.name: Haofan Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-04-25T02:59:37.953Z
paper_authors._id: 637745113a63a2983ffbde13
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: 660e1590076a6255659f92b1
paper_authors.name: Qixun Wang
paper_authors._id: 660e1590076a6255659f92b2
paper_authors.name: Xu Bai
paper_authors._id: 660e1590076a6255659f92b3
paper_authors.name: Zekui Qin
paper_authors._id: 660e1590076a6255659f92b4
paper_authors.name: Anthony Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:59:03.154Z
paper_authors._id: 6311d9ee04f842f79916158c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PvOYgDEpe0x5ExLyj1BK-.png
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: antonio-c
paper_title: InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image
    Generation
paper_summary: Tuning-free diffusion-based models have demonstrated significant potential in
  the realm of image personalization and customization. However, despite this
  notable progress, current models continue to grapple with several complex
  challenges in producing style-consistent image generation. Firstly, the concept
  of style is inherently underdetermined, encompassing a multitude of elements
  such as color, material, atmosphere, design, and structure, among others.
  Secondly, inversion-based methods are prone to style degradation, often
  resulting in the loss of fine-grained details. Lastly, adapter-based approaches
  frequently require meticulous weight tuning for each reference image to achieve
  a balance between style intensity and text controllability. In this paper, we
  commence by examining several compelling yet frequently overlooked
  observations. We then proceed to introduce InstantStyle, a framework designed
  to address these issues through the implementation of two key strategies: 1) A
  straightforward mechanism that decouples style and content from reference
  images within the feature space, predicated on the assumption that features
  within the same space can be either added to or subtracted from one another. 2)
  The injection of reference image features exclusively into style-specific
  blocks, thereby preventing style leaks and eschewing the need for cumbersome
  weight tuning, which often characterizes more parameter-heavy designs.Our work
  demonstrates superior visual stylization outcomes, striking an optimal balance
  between the intensity of style and the controllability of textual elements. Our
  codes will be available at https://github.com/InstantStyle/InstantStyle.
paper_ai_summary: InstantStyle addresses style consistency and detail retention in image generation by decoupling style and content and focusing style-specific feature injection, enhancing stylization without weight tuning.
paper_ai_keywords: diffusion-based models image personalization style-consistent image generation inversion-based methods adapter-based approaches style degradation fine-grained details parameter-heavy designs visual stylization style-specific blocks style leaks