title: FABRIC: Personalizing Diffusion Models with Iterative Feedback
summary: In an era where visual content generation is increasingly driven by machine
  learning, the integration of human feedback into generative models presents
  significant opportunities for enhancing user experience and output quality.
  This study explores strategies for incorporating iterative human feedback into
  the generative process of diffusion-based text-to-image models. We propose
  FABRIC, a training-free approach applicable to a wide range of popular
  diffusion models, which exploits the self-attention layer present in the most
  widely used architectures to condition the diffusion process on a set of
  feedback images. To ensure a rigorous assessment of our approach, we introduce
  a comprehensive evaluation methodology, offering a robust mechanism to quantify
  the performance of generative visual models that integrate human feedback. We
  show that generation results improve over multiple rounds of iterative feedback
  through exhaustive analysis, implicitly optimizing arbitrary user preferences.
  The potential applications of these findings extend to fields such as
  personalized content creation and customization.
paper_authors._id: 64b8ab546a68a9a715e0c83a
paper_authors.name: Dimitri von Rütte
paper_authors.status: claimed_verified
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paper_authors._id: 640501091a3babee78e41e99
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paper_authors.fullname: Dimitri
paper_authors.type: user
paper_authors.user: dvruette
paper_authors._id: 64b8ab546a68a9a715e0c83b
paper_authors.name: Elisabetta Fedele
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-20T07:16:35.271Z
paper_authors._id: 62df0ae635a8bfe88fc4ac80
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paper_authors.fullname: elisabetta fedele
paper_authors.type: user
paper_authors.user: efedele
paper_authors._id: 64b8ab546a68a9a715e0c83c
paper_authors.name: Jonathan Thomm
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-20T09:52:10.016Z
paper_authors._id: 63dec36ee742e86dc920f2cb
paper_authors.avatarUrl: /avatars/3c5fd4ab5e5286dbf948ca113f7cbfb4.svg
paper_authors.fullname: Jonathan Thomm
paper_authors.type: user
paper_authors.user: jthomm
paper_authors._id: 64b8ab546a68a9a715e0c83d
paper_authors.name: Lukas Wolf
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-20T11:01:07.762Z
paper_authors._id: 63ea0c6e2e476376eb08b9fe
paper_authors.avatarUrl: /avatars/a92b5261bd090f142945b60bdba19714.svg
paper_authors.fullname: Lukas 
paper_authors.type: user
paper_authors.user: luwo
paper_title: FABRIC: Personalizing Diffusion Models with Iterative Feedback
paper_summary: In an era where visual content generation is increasingly driven by machine
  learning, the integration of human feedback into generative models presents
  significant opportunities for enhancing user experience and output quality.
  This study explores strategies for incorporating iterative human feedback into
  the generative process of diffusion-based text-to-image models. We propose
  FABRIC, a training-free approach applicable to a wide range of popular
  diffusion models, which exploits the self-attention layer present in the most
  widely used architectures to condition the diffusion process on a set of
  feedback images. To ensure a rigorous assessment of our approach, we introduce
  a comprehensive evaluation methodology, offering a robust mechanism to quantify
  the performance of generative visual models that integrate human feedback. We
  show that generation results improve over multiple rounds of iterative feedback
  through exhaustive analysis, implicitly optimizing arbitrary user preferences.
  The potential applications of these findings extend to fields such as
  personalized content creation and customization.
paper_ai_keywords: diffusion-based text-to-image models FABRIC self-attention layer feedback images iterative feedback evaluation methodology generative visual models