Remasking Discrete Diffusion Models with Inference-Time Scaling
Part of the success of diffusion models stems from their ability to perform
iterative refinement, i.e., repeatedly correcting outputs during generation.
However, modern masked discrete diffusion lacks this capability: when a token
is generated, it cannot be updated again, even when it introduces an error.
Here, we address this limitation by introducing the remasking diffusion model
(ReMDM) sampler, a method that can be applied to pretrained masked diffusion
models in a principled way and that is derived from a discrete diffusion model
with a custom remasking backward process. Most interestingly, ReMDM endows
discrete diffusion with a form of inference-time compute scaling. By increasing
the number of sampling steps, ReMDM generates natural language outputs that
approach the quality of autoregressive models, whereas when the computation
budget is limited, ReMDM better maintains quality. ReMDM also improves sample
quality of masked diffusion models for discretized images, and in scientific
domains such as molecule design, ReMDM facilitates diffusion guidance and
pushes the Pareto frontier of controllability relative to classical masking and
uniform noise diffusion. We provide the code along with a blog post on the
project page: https://remdm.github.io.
67c9fcfc765ba582a758e083
Guanghan Wang
67c9fcfc765ba582a758e084
Yair Schiff
67c9fcfc765ba582a758e085
Subham Sekhar Sahoo
67c9fcfc765ba582a758e086
Volodymyr Kuleshov
Remasking Discrete Diffusion Models with Inference-Time Scaling
Part of the success of diffusion models stems from their ability to perform
iterative refinement, i.e., repeatedly correcting outputs during generation.
However, modern masked discrete diffusion lacks this capability: when a token
is generated, it cannot be updated again, even when it introduces an error.
Here, we address this limitation by introducing the remasking diffusion model
(ReMDM) sampler, a method that can be applied to pretrained masked diffusion
models in a principled way and that is derived from a discrete diffusion model
with a custom remasking backward process. Most interestingly, ReMDM endows
discrete diffusion with a form of inference-time compute scaling. By increasing
the number of sampling steps, ReMDM generates natural language outputs that
approach the quality of autoregressive models, whereas when the computation
budget is limited, ReMDM better maintains quality. ReMDM also improves sample
quality of masked diffusion models for discretized images, and in scientific
domains such as molecule design, ReMDM facilitates diffusion guidance and
pushes the Pareto frontier of controllability relative to classical masking and
uniform noise diffusion. We provide the code along with a blog post on the
project page: https://remdm.github.io.
Remasking diffusion model (ReMDM) enhances discrete diffusion by allowing iterative refinement, improving quality in natural language, image, and molecular design applications.
diffusion models iterative refinement remasking diffusion model ReMDM pretrained masked diffusion models discrete diffusion model remasking backward process inference-time compute scaling autoregressive models masked diffusion models discretized images molecule design diffusion guidance Pareto frontier