title: Think While You Generate: Discrete Diffusion with Planned Denoising
summary: Discrete diffusion has achieved state-of-the-art performance, outperforming
  or approaching autoregressive models on standard benchmarks. In this work, we
  introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework
  that separates the generation process into two models: a planner and a
  denoiser. At inference time, the planner selects which positions to denoise
  next by identifying the most corrupted positions in need of denoising,
  including both initially corrupted and those requiring additional refinement.
  This plan-and-denoise approach enables more efficient reconstruction during
  generation by iteratively identifying and denoising corruptions in the optimal
  order. DDPD outperforms traditional denoiser-only mask diffusion methods,
  achieving superior results on language modeling benchmarks such as text8,
  OpenWebText, and token-based generation on ImageNet 256 times 256. Notably,
  in language modeling, DDPD significantly reduces the performance gap between
  diffusion-based and autoregressive methods in terms of generative perplexity.
  Code is available at https://github.com/liusulin/DDPD.
paper_authors._id: 670d9565d7665bf52989dd9f
paper_authors.name: Sulin Liu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-14T22:04:24.173Z
paper_authors._id: 6704b381e5b7ff340dab6666
paper_authors.avatarUrl: /avatars/f0b88422c0f1103775a75a182ff03a75.svg
paper_authors.fullname: Sulin Liu
paper_authors.type: user
paper_authors.user: sulinliu
paper_authors._id: 670d9565d7665bf52989dda0
paper_authors.name: Juno Nam
paper_authors._id: 670d9565d7665bf52989dda1
paper_authors.name: Andrew Campbell
paper_authors._id: 670d9565d7665bf52989dda2
paper_authors.name: Hannes Stärk
paper_authors._id: 670d9565d7665bf52989dda3
paper_authors.name: Yilun Xu
paper_authors._id: 670d9565d7665bf52989dda4
paper_authors.name: Tommi Jaakkola
paper_authors._id: 670d9565d7665bf52989dda5
paper_authors.name: Rafael Gómez-Bombarelli
paper_title: Think While You Generate: Discrete Diffusion with Planned Denoising
paper_summary: Discrete diffusion has achieved state-of-the-art performance, outperforming
  or approaching autoregressive models on standard benchmarks. In this work, we
  introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework
  that separates the generation process into two models: a planner and a
  denoiser. At inference time, the planner selects which positions to denoise
  next by identifying the most corrupted positions in need of denoising,
  including both initially corrupted and those requiring additional refinement.
  This plan-and-denoise approach enables more efficient reconstruction during
  generation by iteratively identifying and denoising corruptions in the optimal
  order. DDPD outperforms traditional denoiser-only mask diffusion methods,
  achieving superior results on language modeling benchmarks such as text8,
  OpenWebText, and token-based generation on ImageNet 256 times 256. Notably,
  in language modeling, DDPD significantly reduces the performance gap between
  diffusion-based and autoregressive methods in terms of generative perplexity.
  Code is available at https://github.com/liusulin/DDPD.
paper_ai_summary: A novel framework, Discrete Diffusion with Planned Denoising (DDPD), separates generation into a planner and denoiser to optimize denoising order, outperforming traditional methods in language modeling and image generation.
paper_ai_keywords: Discrete diffusion DDPD planner denoiser inference generative perplexity