title: Style Aligned Image Generation via Shared Attention
summary: Large-scale Text-to-Image (T2I) models have rapidly gained prominence across
  creative fields, generating visually compelling outputs from textual prompts.
  However, controlling these models to ensure consistent style remains
  challenging, with existing methods necessitating fine-tuning and manual
  intervention to disentangle content and style. In this paper, we introduce
  StyleAligned, a novel technique designed to establish style alignment among a
  series of generated images. By employing minimal `attention sharing' during the
  diffusion process, our method maintains style consistency across images within
  T2I models. This approach allows for the creation of style-consistent images
  using a reference style through a straightforward inversion operation. Our
  method's evaluation across diverse styles and text prompts demonstrates
  high-quality synthesis and fidelity, underscoring its efficacy in achieving
  consistent style across various inputs.
paper_authors._id: 656e8eaf2c331f3e079c0a0b
paper_authors.name: Amir Hertz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T12:10:09.666Z
paper_authors._id: 63071a86fd156a1d624a4c46
paper_authors.avatarUrl: /avatars/bfc2a2169c080304124eb7f9ab04306d.svg
paper_authors.fullname: Amir Hertz
paper_authors.type: user
paper_authors.user: amirhertz
paper_authors._id: 656e8eaf2c331f3e079c0a0c
paper_authors.name: Andrey Voynov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T12:10:18.227Z
paper_authors._id: 630551fd80bc5e03dad21ea6
paper_authors.avatarUrl: /avatars/849e41404df698cc89c68939de45ec9a.svg
paper_authors.fullname: Andrey Voynov
paper_authors.type: user
paper_authors.user: avoin
paper_authors._id: 656e8eaf2c331f3e079c0a0d
paper_authors.name: Shlomi Fruchter
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T12:10:25.483Z
paper_authors._id: 6335ae887fcbeb615b0f9924
paper_authors.avatarUrl: /avatars/60fd9305ccc56f17d6007f781c54fe2b.svg
paper_authors.fullname: Shlomi Fruchter
paper_authors.type: user
paper_authors.user: shlomif
paper_authors._id: 656e8eaf2c331f3e079c0a0e
paper_authors.name: Daniel Cohen-Or
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T12:10:32.021Z
paper_authors._id: 628507161949ebcae8e24ec3
paper_authors.avatarUrl: /avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
paper_authors.fullname: Daniel Cohen-Or
paper_authors.type: user
paper_authors.user: cohenor
paper_title: Style Aligned Image Generation via Shared Attention
paper_summary: Large-scale Text-to-Image (T2I) models have rapidly gained prominence across
  creative fields, generating visually compelling outputs from textual prompts.
  However, controlling these models to ensure consistent style remains
  challenging, with existing methods necessitating fine-tuning and manual
  intervention to disentangle content and style. In this paper, we introduce
  StyleAligned, a novel technique designed to establish style alignment among a
  series of generated images. By employing minimal `attention sharing' during the
  diffusion process, our method maintains style consistency across images within
  T2I models. This approach allows for the creation of style-consistent images
  using a reference style through a straightforward inversion operation. Our
  method's evaluation across diverse styles and text prompts demonstrates
  high-quality synthesis and fidelity, underscoring its efficacy in achieving
  consistent style across various inputs.
paper_ai_summary: StyleAligned is a novel technique for maintaining style consistency in large-scale Text-to-Image models by employing minimal attention sharing during the diffusion process.
paper_ai_keywords: Text-to-Image (T2I) style alignment diffusion process attention sharing style consistency inversion operation