# Image generation & editing


📂 model-architectures

## MODEL ARCHITECTURES

# Image generation & editing

Synthesize or transform pixels, most often through a text or image conditioner, a latent generator, and an image decoder.

## MENTAL MODEL

A pipeline, not one network: prompt encoder → generative process → latent/pixel decoder, often with separate control and safety components.

## DATA FLOW

- Text, image, mask, or layout condition

- Condition encoder

- Latent diffusion, DiT, or autoregressive generator

- Image decoder

- Candidate image + checks

## How it trains

Modern systems often learn a denoising or flow objective in pixel or compressed latent space. Alignment to text depends on paired data and conditioning; autoencoders may be trained separately.

## How inference runs

A sampler starts from noise or a noised source image, iteratively refines it under the condition, then decodes the latent. Seeds improve reproducibility but do not make all runtime paths deterministic.

## Strengths

- •
High-quality open-ended visual synthesis

- •
Natural support for variation, inpainting, and guided editing

- •
Latent pipelines make high-resolution generation more practical

## Trade-offs

- •
Multiple sampling steps add latency

- •
Text, typography, identity, and exact spatial constraints need explicit evaluation

- •
Training-data rights, provenance, and misuse controls are product concerns

## Use it when

- Creative ideation, asset drafts, or controlled image transformation

- The output can be reviewed or constrained

- Visual quality and prompt adherence are evaluated separately

## Avoid or challenge it when

- Pixel-exact layouts are required

- The output is evidence of a real event or identity

- Rights and provenance requirements are unresolved

## Illustrative published families

- • Latent Diffusion / Stable Diffusion research lineage

- • Diffusion Transformer (DiT) research architecture

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures