title: Weak-to-Strong Diffusion with Reflection
summary: The goal of diffusion generative models is to align the learned distribution
  with the real data distribution through gradient score matching. However,
  inherent limitations in training data quality, modeling strategies, and
  architectural design lead to inevitable gap between generated outputs and real
  data. To reduce this gap, we propose Weak-to-Strong Diffusion (W2SD), a novel
  framework that utilizes the estimated difference between existing weak and
  strong models (i.e., weak-to-strong difference) to approximate the gap between
  an ideal model and a strong model. By employing a reflective operation that
  alternates between denoising and inversion with weak-to-strong difference, we
  theoretically understand that W2SD steers latent variables along sampling
  trajectories toward regions of the real data distribution. W2SD is highly
  flexible and broadly applicable, enabling diverse improvements through the
  strategic selection of weak-to-strong model pairs (e.g., DreamShaper vs. SD1.5,
  good experts vs. bad experts in MoE). Extensive experiments demonstrate that
  W2SD significantly improves human preference, aesthetic quality, and prompt
  adherence, achieving SOTA performance across various modalities (e.g., image,
  video), architectures (e.g., UNet-based, DiT-based, MoE), and benchmarks. For
  example, Juggernaut-XL with W2SD can improve with the HPSv2 winning rate up to
  90% over the original results. Moreover, the performance gains achieved by W2SD
  markedly outweigh its additional computational overhead, while the cumulative
  improvements from different weak-to-strong difference further solidify its
  practical utility and deployability.
paper_authors._id: 67a5f635c20315f5e3f16f62
paper_authors.name: Lichen Bai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-07T12:56:27.462Z
paper_authors._id: 66348bf4e1555067669870fa
paper_authors.avatarUrl: /avatars/8b8bbc7dff7d9a0a02b0960084bc95ab.svg
paper_authors.fullname: 白立忱
paper_authors.type: user
paper_authors.user: Indulge-Bai
paper_authors._id: 67a5f635c20315f5e3f16f63
paper_authors.name: Masashi Sugiyama
paper_authors._id: 67a5f635c20315f5e3f16f64
paper_authors.name: Zeke Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:20:47.773Z
paper_authors._id: 67a5fb17baac478d94c59fec
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/gMtdNiD8ztyAjB_weexcK.png
paper_authors.fullname: Z.X.
paper_authors.type: user
paper_authors.user: xLeaF
paper_title: Weak-to-Strong Diffusion with Reflection
paper_summary: The goal of diffusion generative models is to align the learned distribution
  with the real data distribution through gradient score matching. However,
  inherent limitations in training data quality, modeling strategies, and
  architectural design lead to inevitable gap between generated outputs and real
  data. To reduce this gap, we propose Weak-to-Strong Diffusion (W2SD), a novel
  framework that utilizes the estimated difference between existing weak and
  strong models (i.e., weak-to-strong difference) to approximate the gap between
  an ideal model and a strong model. By employing a reflective operation that
  alternates between denoising and inversion with weak-to-strong difference, we
  theoretically understand that W2SD steers latent variables along sampling
  trajectories toward regions of the real data distribution. W2SD is highly
  flexible and broadly applicable, enabling diverse improvements through the
  strategic selection of weak-to-strong model pairs (e.g., DreamShaper vs. SD1.5,
  good experts vs. bad experts in MoE). Extensive experiments demonstrate that
  W2SD significantly improves human preference, aesthetic quality, and prompt
  adherence, achieving SOTA performance across various modalities (e.g., image,
  video), architectures (e.g., UNet-based, DiT-based, MoE), and benchmarks. For
  example, Juggernaut-XL with W2SD can improve with the HPSv2 winning rate up to
  90% over the original results. Moreover, the performance gains achieved by W2SD
  markedly outweigh its additional computational overhead, while the cumulative
  improvements from different weak-to-strong difference further solidify its
  practical utility and deployability.
paper_ai_keywords: diffusion generative models gradient score matching latent diffusion models weak-to-strong Difference (W2SD) denoising inversion domain trajectories human preference aesthetic quality prompt adherence SOTA performance UNet-based DiT-based MoE HPSv2 computational overhead