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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Arpit Bansal · Eitan Borgnia · Hong-Min Chu · Jie Li · Hamid Kazemi · Furong Huang · Micah Goldblum · Jonas Geiping · Tom Goldstein

Great Hall & Hall B1+B2 (level 1) #1918
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[ Paper [ Poster [ OpenReview
Wed 13 Dec 3 p.m. PST — 5 p.m. PST


Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact, an entire family of generative models can be constructed by varying this choice. Even when using completely deterministic degradations (e.g., blur, masking, and more), the training and test-time update rules that underlie diffusion models can be easily generalized to create generative models. The success of these fully deterministic models calls into question the community's understanding of diffusion models, which relies on noise in either gradient Langevin dynamics or variational inference and paves the way for generalized diffusion models that invert arbitrary processes.

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