Cloud Diffusion Part 1: Theory and Motivation

• URL: http://arxiv.org/abs/2507.05496v1
• Date: Mon, 07 Jul 2025 21:36:16 GMT
• Title: Cloud Diffusion Part 1: Theory and Motivation
• Authors: Andrew Randono,
• Abstract summary: Diffusion models for image generation function by progressively adding noise to an image set and training a model to separate out the signal from the noise.<n>We argue that these models can lead to faster inference, improved high-frequency details, and greater controllability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models for image generation function by progressively adding noise to an image set and training a model to separate out the signal from the noise. The noise profile used by these models is white noise -- that is, noise based on independent normal distributions at each point whose mean and variance is independent of the scale. By contrast, most natural image sets exhibit a type of scale invariance in their low-order statistical properties characterized by a power-law scaling. Consequently, natural images are closer (in a quantifiable sense) to a different probability distribution that emphasizes large scale correlations and de-emphasizes small scale correlations. These scale invariant noise profiles can be incorporated into diffusion models in place of white noise to form what we will call a ``Cloud Diffusion Model". We argue that these models can lead to faster inference, improved high-frequency details, and greater controllability. In a follow-up paper, we will build and train a Cloud Diffusion Model that uses scale invariance at a fundamental level and compare it to classic, white noise diffusion models.

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