title: Style-Friendly SNR Sampler for Style-Driven Generation
summary: Recent large-scale diffusion models generate high-quality images but struggle
  to learn new, personalized artistic styles, which limits the creation of unique
  style templates. Fine-tuning with reference images is the most promising
  approach, but it often blindly utilizes objectives and noise level
  distributions used for pre-training, leading to suboptimal style alignment. We
  propose the Style-friendly SNR sampler, which aggressively shifts the
  signal-to-noise ratio (SNR) distribution toward higher noise levels during
  fine-tuning to focus on noise levels where stylistic features emerge. This
  enables models to better capture unique styles and generate images with higher
  style alignment. Our method allows diffusion models to learn and share new
  "style templates", enhancing personalized content creation. We demonstrate the
  ability to generate styles such as personal watercolor paintings, minimal flat
  cartoons, 3D renderings, multi-panel images, and memes with text, thereby
  broadening the scope of style-driven generation.
paper_authors._id: 6743d8895689f9445a5da3e9
paper_authors.name: Jooyoung Choi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T08:34:18.831Z
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paper_authors.avatarUrl: /avatars/9925de0d02b2f2ed03cbafda44fd7de4.svg
paper_authors.fullname: Jooyoung Choi
paper_authors.type: user
paper_authors.user: jychoi
paper_authors._id: 6743d8895689f9445a5da3ea
paper_authors.name: Chaehun Shin
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/590633e163da425bf041e3988d8dd015.svg
paper_authors.fullname: shin
paper_authors.type: user
paper_authors.user: chaehun
paper_authors._id: 6743d8895689f9445a5da3eb
paper_authors.name: Yeongtak Oh
paper_authors.status: claimed_verified
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paper_authors._id: 636f6e8a31af06da86499ebc
paper_authors.avatarUrl: /avatars/9430fbc05774aad8e46c0861769b3c30.svg
paper_authors.fullname: Yeongtak
paper_authors.type: user
paper_authors.user: Yeongtak
paper_authors._id: 6743d8895689f9445a5da3ec
paper_authors.name: Heeseung Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-25T21:17:58.967Z
paper_authors._id: 63f04b085c2ceb16fc73038a
paper_authors.avatarUrl: /avatars/27aff620692fcf1d877f479105dc8d76.svg
paper_authors.fullname: Heeseung Kim
paper_authors.type: user
paper_authors.user: gmltmd789
paper_authors._id: 6743d8895689f9445a5da3ed
paper_authors.name: Sungroh Yoon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-25T21:17:53.412Z
paper_authors._id: 64c99967d33e6a7f82c35f0b
paper_authors.avatarUrl: /avatars/70e77eb570d12e11c2807e10cd4d926d.svg
paper_authors.fullname: Sungroh Yoon
paper_authors.type: user
paper_authors.user: sryoon
paper_title: Style-Friendly SNR Sampler for Style-Driven Generation
paper_summary: Recent large-scale diffusion models generate high-quality images but struggle
  to learn new, personalized artistic styles, which limits the creation of unique
  style templates. Fine-tuning with reference images is the most promising
  approach, but it often blindly utilizes objectives and noise level
  distributions used for pre-training, leading to suboptimal style alignment. We
  propose the Style-friendly SNR sampler, which aggressively shifts the
  signal-to-noise ratio (SNR) distribution toward higher noise levels during
  fine-tuning to focus on noise levels where stylistic features emerge. This
  enables models to better capture unique styles and generate images with higher
  style alignment. Our method allows diffusion models to learn and share new
  "style templates", enhancing personalized content creation. We demonstrate the
  ability to generate styles such as personal watercolor paintings, minimal flat
  cartoons, 3D renderings, multi-panel images, and memes with text, thereby
  broadening the scope of style-driven generation.
paper_ai_summary: The Style-friendly SNR sampler modifies the noise level distribution during fine-tuning to improve style alignment in diffusion models, enabling better capture of unique artistic styles.
paper_ai_keywords: diffusion models signal-to-noise ratio (SNR) fine-tuning noise level distribution stylistic features style templates personalized content creation