title: Nested Diffusion Processes for Anytime Image Generation
summary: Diffusion models are the current state-of-the-art in image generation,
  synthesizing high-quality images by breaking down the generation process into
  many fine-grained denoising steps. Despite their good performance, diffusion
  models are computationally expensive, requiring many neural function
  evaluations (NFEs). In this work, we propose an anytime diffusion-based method
  that can generate viable images when stopped at arbitrary times before
  completion. Using existing pretrained diffusion models, we show that the
  generation scheme can be recomposed as two nested diffusion processes, enabling
  fast iterative refinement of a generated image. We use this Nested Diffusion
  approach to peek into the generation process and enable flexible scheduling
  based on the instantaneous preference of the user. In experiments on ImageNet
  and Stable Diffusion-based text-to-image generation, we show, both
  qualitatively and quantitatively, that our method's intermediate generation
  quality greatly exceeds that of the original diffusion model, while the final
  slow generation result remains comparable.
paper_authors._id: 6476b14a02fc644c810b35bd
paper_authors.name: Noam Elata
paper_authors._id: 6476b14a02fc644c810b35be
paper_authors.name: Bahjat Kawar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-31T08:41:36.837Z
paper_authors._id: 634e99b9d049354d7ee2614a
paper_authors.avatarUrl: /avatars/3dc9cfc40e6e18235a9e0a588c4029d0.svg
paper_authors.fullname: Bahjat Kawar
paper_authors.type: user
paper_authors.user: bahjat-kawar
paper_authors._id: 6476b14a02fc644c810b35bf
paper_authors.name: Tomer Michaeli
paper_authors._id: 6476b14a02fc644c810b35c0
paper_authors.name: Michael Elad
paper_title: Nested Diffusion Processes for Anytime Image Generation
paper_summary: Diffusion models are the current state-of-the-art in image generation,
  synthesizing high-quality images by breaking down the generation process into
  many fine-grained denoising steps. Despite their good performance, diffusion
  models are computationally expensive, requiring many neural function
  evaluations (NFEs). In this work, we propose an anytime diffusion-based method
  that can generate viable images when stopped at arbitrary times before
  completion. Using existing pretrained diffusion models, we show that the
  generation scheme can be recomposed as two nested diffusion processes, enabling
  fast iterative refinement of a generated image. We use this Nested Diffusion
  approach to peek into the generation process and enable flexible scheduling
  based on the instantaneous preference of the user. In experiments on ImageNet
  and Stable Diffusion-based text-to-image generation, we show, both
  qualitatively and quantitatively, that our method's intermediate generation
  quality greatly exceeds that of the original diffusion model, while the final
  slow generation result remains comparable.
paper_ai_keywords: diffusion models denoising steps neural function evaluations anytime diffusion-based method pretrained diffusion models Nested Diffusion approach iterative refinement ImageNet Stable Diffusion-based text-to-image generation