Reimagining Diffusion Models: Autoregressive Priors for Efficient Initialization

Author(s): Shenggang Li

Originally published on Towards AI.

Exploring a Novel Approach to Diffusion Initialization with Intuitive Illustrations, Applications

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Photo by Gary Fultz on Unsplash

Diffusion models have become a cornerstone of modern AI, especially in generative tasks like creating realistic images or high-quality audio. They’re like digital artists, transforming random noise into stunningly detailed outputs step-by-step. This meticulous approach has made diffusion models a game-changer in the AI world.

Typically, these models begin their work with pure Gaussian noise, which acts as the blank canvas. While effective, this starting point doesn’t take advantage of prior knowledge about the data structure, potentially slowing down the process and affecting sample quality. Imagine if we could give these models a smarter head start.

That’s where Autoregressive Priors (ARPs) come in. I introduce a new approach that integrates Autoregressive Models (ARMs) at the start of the diffusion process, adding structure instead of relying on pure Gaussian noise. This speeds up generation and enhances sample quality. I will explore how ARPs improve diffusion models, break down their mechanics, and compare them with traditional methods.

Imagine restoring a faded photograph: you begin with a nearly blank canvas (random noise) and repeatedly refine it, eventually recovering the original image. Diffusion models operate similarly — they start from random… Read the full blog for free on Medium.

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Published via Towards AI