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01 ·Generative Models·★ MEMBER·PAPER·11 min read Build Your Own Diffusion Model — Starting from MNIST A diffusion model built up from nothing on 28×28 handwritten digits: the two conditions a noise schedule has to satisfy, how the step number gets injected into a U-Net, and why the sampler adds noise back at the very end — the places you only discover by writing the code yourself. 02 ·Generative Models·★ MEMBER·PAPER·9 min read Diffusion Models from the Ground Up — Add Noise, Then Subtract It A reading of the DDPM paper (Ho et al., 2020) grounded strictly in its own text: the forward process that adds noise and the reverse process that removes it, why the objective settles into 'predict the noise', how a simplified loss hurt likelihood while improving samples, and what the paper does and does not say about step count.