#generative-model
8 articles
01
·Generative Models·★ MEMBER·PAPER·11 min read
The Mathematics of Diffusion — Generation Seen Through Scores and SDEs
A layer beneath 'add noise, then subtract it': diffusion models restated in the language of the score, the gradient of log-density. Why denoising and score estimation are literally the same job, what the forward SDE, reverse SDE and probability flow ODE actually assert, and how those equations turn into the knobs you set at inference time.
02
·Generative Models·FREE·PAPER·11 min read
A Practical Map of Image Generation — SD, ControlNet, and Applying LoRA
A single map for anyone about to touch image generation for the first time: the four boxes of latent diffusion, where the prompt actually takes effect, locking down composition with ControlNet, fine-tuning with LoRA, and the licensing layers that quietly cause the most damage.
03
·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.
04
·Generative Models·FREE·10 min read
VAEs from Scratch — Stir Probability into "Compress and Restore" and You Get a Generator
An autoencoder that only compresses and restores cannot invent anything new. This walks through why a single drop of probability turns it into a generative model — ELBO, the reparameterization trick, and walking the latent space — assuming no prior knowledge.
05
·Generative Models·★ MEMBER·PAPER·8 min read
CFG and Samplers — What the "Strength" Knob in Generative AI Really Does
What "CFG Scale" and "Sampling steps / method" actually do inside an image generator, explained from zero. CFG extrapolates along the difference between the conditional and unconditional prediction; the sampler is a numerical ODE solver. Both knobs trade something away.
06
·Generative Models·★ MEMBER·PAPER·10 min read
The Rise and Fall of GANs — An Invention Trained by Rivalry, and Why Diffusion Won
In 2014, GANs upended image generation with one idea: don't write down what 'realistic' means — learn it. This traces the decade that followed, through mode collapse, the Wasserstein rescue, and the reasons diffusion models eventually took the crown.
07
·Generative Models·★ MEMBER·PAPER·10 min read
Flow Matching from Scratch — What Came After Diffusion, and Why It Goes Straight
Flow matching — the formulation underneath Stable Diffusion 3 and FLUX — explained with no prerequisites. Velocity fields, ODEs, and rectified flow, told through analogy and interactive figures, all the way to the scheduler settings you actually touch in production.
08
·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.