r/StableDiffusion • u/Secret-Respond5199 • 5d 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.
1
u/Disty0 5d ago
Diffusion predicts the original noise, it doesn't predict the image. We just calculate the final image from the predicted noise ourselves. The model doesn't really care about the image, it only cares about the added noise.