# Diffusion & score-based generation


📂 model-architectures

## MODEL ARCHITECTURES

# Diffusion & score-based generation

Learn to reverse a gradual noising process, producing data by repeatedly transforming noise toward a sample.

## MENTAL MODEL

Start with static and repeatedly remove the predicted noise, guided by a time step and optional condition.

## DATA FLOW

- Clean training sample + sampled noise level

- Noisy sample

- U-Net or Transformer denoiser

- Noise / velocity / score estimate

- Iterative reverse sampler

## How it trains

A network predicts noise, clean data, velocity, or a score at randomly sampled noise levels. Related flow-matching formulations learn a vector field between distributions.

## How inference runs

Sampling integrates a learned reverse process across multiple steps. Sampler choice, step count, guidance, and seed trade speed, diversity, and fidelity.

## Strengths

- •
Stable training relative to adversarial objectives

- •
Flexible conditioning and editing

- •
Strong coverage across image, audio, video, and scientific data

## Trade-offs

- •
Iterative sampling is slower than a one-pass generator

- •
Guidance can reduce diversity or introduce artifacts

- •
The data representation and sampler materially affect results

## Use it when

- High-dimensional conditional generation is central

- Multiple inference steps fit the latency budget

- Controls and sample quality can be evaluated jointly

## Avoid or challenge it when

- One-pass generation is mandatory

- The model must run on a severely constrained device

- “Diffusion” is being treated as one fixed implementation

## Illustrative published families

- • DDPM

- • Score-based models

- • Pixel-space diffusion systems

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures