title: One-step Diffusion Models with f-Divergence Distribution Matching
summary: Sampling from diffusion models involves a slow iterative process that hinders
  their practical deployment, especially for interactive applications. To
  accelerate generation speed, recent approaches distill a multi-step diffusion
  model into a single-step student generator via variational score distillation,
  which matches the distribution of samples generated by the student to the
  teacher's distribution. However, these approaches use the reverse
  Kullback-Leibler (KL) divergence for distribution matching which is known to be
  mode seeking. In this paper, we generalize the distribution matching approach
  using a novel f-divergence minimization framework, termed f-distill, that
  covers different divergences with different trade-offs in terms of mode
  coverage and training variance. We derive the gradient of the f-divergence
  between the teacher and student distributions and show that it is expressed as
  the product of their score differences and a weighting function determined by
  their density ratio. This weighting function naturally emphasizes samples with
  higher density in the teacher distribution, when using a less mode-seeking
  divergence. We observe that the popular variational score distillation approach
  using the reverse-KL divergence is a special case within our framework.
  Empirically, we demonstrate that alternative f-divergences, such as
  forward-KL and Jensen-Shannon divergences, outperform the current best
  variational score distillation methods across image generation tasks. In
  particular, when using Jensen-Shannon divergence, f-distill achieves current
  state-of-the-art one-step generation performance on ImageNet64 and zero-shot
  text-to-image generation on MS-COCO. Project page:
  https://research.nvidia.com/labs/genair/f-distill
paper_authors._id: 67bbe67c7727595ca5979d2a
paper_authors.name: Yilun Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T15:54:56.422Z
paper_authors._id: 649c37de5ffe05267a105fe8
paper_authors.avatarUrl: /avatars/4f262272e6222f879c6c0fedfa2e5861.svg
paper_authors.fullname: Yilun Xu
paper_authors.type: user
paper_authors.user: AaronXyl
paper_authors._id: 67bbe67c7727595ca5979d2b
paper_authors.name: Weili Nie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T15:55:03.232Z
paper_authors._id: 64c1a69e226e016da8450ae2
paper_authors.avatarUrl: /avatars/54c161e8b8543244ed13cbe47017624e.svg
paper_authors.fullname: Weili Nie
paper_authors.type: user
paper_authors.user: xiaoli08
paper_authors._id: 67bbe67c7727595ca5979d2c
paper_authors.name: Arash Vahdat
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T15:55:08.446Z
paper_authors._id: 66727eb8f1bef807e01c6164
paper_authors.avatarUrl: /avatars/ba8edee083cfd6124b82e2a776f0fb43.svg
paper_authors.fullname: Arash vahdat
paper_authors.type: user
paper_authors.user: ArashAVN
paper_title: One-step Diffusion Models with f-Divergence Distribution Matching
paper_summary: Sampling from diffusion models involves a slow iterative process that hinders
  their practical deployment, especially for interactive applications. To
  accelerate generation speed, recent approaches distill a multi-step diffusion
  model into a single-step student generator via variational score distillation,
  which matches the distribution of samples generated by the student to the
  teacher's distribution. However, these approaches use the reverse
  Kullback-Leibler (KL) divergence for distribution matching which is known to be
  mode seeking. In this paper, we generalize the distribution matching approach
  using a novel f-divergence minimization framework, termed f-distill, that
  covers different divergences with different trade-offs in terms of mode
  coverage and training variance. We derive the gradient of the f-divergence
  between the teacher and student distributions and show that it is expressed as
  the product of their score differences and a weighting function determined by
  their density ratio. This weighting function naturally emphasizes samples with
  higher density in the teacher distribution, when using a less mode-seeking
  divergence. We observe that the popular variational score distillation approach
  using the reverse-KL divergence is a special case within our framework.
  Empirically, we demonstrate that alternative f-divergences, such as
  forward-KL and Jensen-Shannon divergences, outperform the current best
  variational score distillation methods across image generation tasks. In
  particular, when using Jensen-Shannon divergence, f-distill achieves current
  state-of-the-art one-step generation performance on ImageNet64 and zero-shot
  text-to-image generation on MS-COCO. Project page:
  https://research.nvidia.com/labs/genair/f-distill
paper_ai_keywords: diffusion models multi-step diffusion model single-step student generator variational score distillation Kullback-Leibler divergence $f$-divergence $f$-distill distribution matching gradient of $f$-divergence score differences density ratio reverse-KL divergence forward-KL divergence Jensen-Shannon divergence one-step generation performance ImageNet64 zero-shot text-to-image generation MS-COCO