Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
- URL: http://arxiv.org/abs/2208.09392v1
- Date: Fri, 19 Aug 2022 15:18:39 GMT
- Title: Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
- Authors: Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi,
Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
- Abstract summary: We show that an entire family of generative models can be constructed by varying the choice of image degradation.
The success of fully deterministic models calls into question the community's understanding of diffusion models.
- Score: 52.59444045853966
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. Our code is available at
https://github.com/arpitbansal297/Cold-Diffusion-Models
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