title: StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image
    Generation
summary: This paper presents a LoRA-free method for stylized image generation that
  takes a text prompt and style reference images as inputs and produces an output
  image in a single pass. Unlike existing methods that rely on training a
  separate LoRA for each style, our method can adapt to various styles with a
  unified model. However, this poses two challenges: 1) the prompt loses
  controllability over the generated content, and 2) the output image inherits
  both the semantic and style features of the style reference image, compromising
  its content fidelity. To address these challenges, we introduce StyleAdapter, a
  model that comprises two components: a two-path cross-attention module (TPCA)
  and three decoupling strategies. These components enable our model to process
  the prompt and style reference features separately and reduce the strong
  coupling between the semantic and style information in the style references.
  StyleAdapter can generate high-quality images that match the content of the
  prompts and adopt the style of the references (even for unseen styles) in a
  single pass, which is more flexible and efficient than previous methods.
  Experiments have been conducted to demonstrate the superiority of our method
  over previous works.
paper_authors._id: 64f7f5e4bcf14b0c9166c6fd
paper_authors.name: Zhouxia Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-06T15:19:48.675Z
paper_authors._id: 64db92a5858f8a41c11669b7
paper_authors.avatarUrl: /avatars/e834d8f1d4781e3bb0b5d6d25b3b3505.svg
paper_authors.fullname: Zhouxia Wang
paper_authors.type: user
paper_authors.user: wzhouxiff
paper_authors._id: 64f7f5e4bcf14b0c9166c6fe
paper_authors.name: Xintao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-06T15:20:36.238Z
paper_authors._id: 60e272ca6c78a8c122b12127
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
paper_authors.fullname: Xintao Wang
paper_authors.type: user
paper_authors.user: Xintao
paper_authors._id: 64f7f5e4bcf14b0c9166c6ff
paper_authors.name: Liangbin Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-06T15:20:46.947Z
paper_authors._id: 6315dcb493fdd4a5633f7b30
paper_authors.avatarUrl: /avatars/c58d228dbdf4f1edb5aa4e635dacb51d.svg
paper_authors.fullname: Liangbin Xie
paper_authors.type: user
paper_authors.user: Liangbin
paper_authors._id: 64f7f5e4bcf14b0c9166c700
paper_authors.name: Zhongang Qi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-19T08:40:32.053Z
paper_authors._id: 660cc64f1b14f691990c0ea0
paper_authors.avatarUrl: /avatars/f172d3120b22d745a41cc3f2eb499ce6.svg
paper_authors.fullname: Zhongang Qi
paper_authors.type: user
paper_authors.user: phoenixqza
paper_authors._id: 64f7f5e4bcf14b0c9166c701
paper_authors.name: Ying Shan
paper_authors._id: 64f7f5e4bcf14b0c9166c702
paper_authors.name: Wenping Wang
paper_authors._id: 64f7f5e4bcf14b0c9166c703
paper_authors.name: Ping Luo
paper_title: StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image
    Generation
paper_summary: This paper presents a LoRA-free method for stylized image generation that
  takes a text prompt and style reference images as inputs and produces an output
  image in a single pass. Unlike existing methods that rely on training a
  separate LoRA for each style, our method can adapt to various styles with a
  unified model. However, this poses two challenges: 1) the prompt loses
  controllability over the generated content, and 2) the output image inherits
  both the semantic and style features of the style reference image, compromising
  its content fidelity. To address these challenges, we introduce StyleAdapter, a
  model that comprises two components: a two-path cross-attention module (TPCA)
  and three decoupling strategies. These components enable our model to process
  the prompt and style reference features separately and reduce the strong
  coupling between the semantic and style information in the style references.
  StyleAdapter can generate high-quality images that match the content of the
  prompts and adopt the style of the references (even for unseen styles) in a
  single pass, which is more flexible and efficient than previous methods.
  Experiments have been conducted to demonstrate the superiority of our method
  over previous works.
paper_ai_keywords: StyleAdapter two-path cross-attention module (TPCA) high-quality images