title: Artist: Aesthetically Controllable Text-Driven Stylization without
    Training
summary: Diffusion models entangle content and style generation during the denoising
  process, leading to undesired content modification when directly applied to
  stylization tasks. Existing methods struggle to effectively control the
  diffusion model to meet the aesthetic-level requirements for stylization. In
  this paper, we introduce Artist, a training-free approach that
  aesthetically controls the content and style generation of a pretrained
  diffusion model for text-driven stylization. Our key insight is to disentangle
  the denoising of content and style into separate diffusion processes while
  sharing information between them. We propose simple yet effective content and
  style control methods that suppress style-irrelevant content generation,
  resulting in harmonious stylization results. Extensive experiments demonstrate
  that our method excels at achieving aesthetic-level stylization requirements,
  preserving intricate details in the content image and aligning well with the
  style prompt. Furthermore, we showcase the highly controllability of the
  stylization strength from various perspectives. Code will be released, project
  home page: https://DiffusionArtist.github.io
paper_authors._id: 669f412fab5548b23ea16ae2
paper_authors.name: Ruixiang Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-23T12:23:47.673Z
paper_authors._id: 6351382f40dffad651ef3fbd
paper_authors.avatarUrl: /avatars/3ac2de7c49086bb37cc4f4bd29ed72f2.svg
paper_authors.fullname: JIANG
paper_authors.type: user
paper_authors.user: Ruixiang
paper_authors._id: 669f412fab5548b23ea16ae3
paper_authors.name: Changwen Chen
paper_title: Artist: Aesthetically Controllable Text-Driven Stylization without
    Training
paper_summary: Diffusion models entangle content and style generation during the denoising
  process, leading to undesired content modification when directly applied to
  stylization tasks. Existing methods struggle to effectively control the
  diffusion model to meet the aesthetic-level requirements for stylization. In
  this paper, we introduce Artist, a training-free approach that
  aesthetically controls the content and style generation of a pretrained
  diffusion model for text-driven stylization. Our key insight is to disentangle
  the denoising of content and style into separate diffusion processes while
  sharing information between them. We propose simple yet effective content and
  style control methods that suppress style-irrelevant content generation,
  resulting in harmonious stylization results. Extensive experiments demonstrate
  that our method excels at achieving aesthetic-level stylization requirements,
  preserving intricate details in the content image and aligning well with the
  style prompt. Furthermore, we showcase the highly controllability of the
  stylization strength from various perspectives. Code will be released, project
  home page: https://DiffusionArtist.github.io
paper_ai_summary: Artist is a training-free method to control a pretrained diffusion model for text-driven stylization, achieving aesthetic results by separating and sharing information between content and style denoising processes.
paper_ai_keywords: diffusion models denoising process text-driven stylization aesthetic-level requirements harmonious stylization stylization strength