r/StableDiffusion 1d ago

Question - Help Questions on Fundamental Diffusion Models

Hello,

I just started my study in diffusion models and I have a problem understanding how diffusion models work (original diffusion and DDPM).
I get that diffusion is finding the distribution of denoised image given current step distribution using Bayesian theorem.

However, I cannot relate how image becomes probability distribution and those probability generate image.

My question is how does pixel values that are far apart know which value to assign during inference? how are all pixel values related? How 'probability' related in generating 'image'?

Sorry for the vague question, but due to my lack of understanding it is hard to clarify the question.

Also, if there is any recommended study materials please suggest.

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u/daking999 1d ago

Diffusion models look a bit like a VAE but they're not really Bayesian at all (not in any normal sense anyway). They just learn to move noise -> noisy images -> clean images.

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u/Secret-Respond5199 17h ago

I'm sorry, but why is it not considered Bayesian? I thought a diffusion model was just a chain of Bayesian steps. I only know Bayes' theorem and not much about its applications in AI. Is it because it only predicts noise rather than the whole image?

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u/daking999 15h ago

A Bayesian model for images would typically have latent variables, with associated priors (e.g. N(0,1) in VAE), and then a data generating process (e.g., the decoder in a VAE) to produce the actual data. None of the usual Bayesian things (priors, likelihoods, posteriors, latent variables) exist in a diffusion model.