Think While You Generate: Discrete Diffusion with Planned Denoising
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.
670d9565d7665bf52989dd9f
Sulin Liu
extracted_pending
2024-10-14T22:04:24.173Z
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/avatars/f0b88422c0f1103775a75a182ff03a75.svg
Sulin Liu
user
sulinliu
670d9565d7665bf52989dda0
Juno Nam
670d9565d7665bf52989dda1
Andrew Campbell
670d9565d7665bf52989dda2
Hannes Stärk
670d9565d7665bf52989dda3
Yilun Xu
670d9565d7665bf52989dda4
Tommi Jaakkola
670d9565d7665bf52989dda5
Rafael Gómez-Bombarelli
Think While You Generate: Discrete Diffusion with Planned Denoising
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.
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.
Discrete diffusion DDPD planner denoiser inference generative perplexity