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Image generation model

An image generation model is a machine-learning model that creates, edits, or transforms images from learned patterns and conditioning inputs.

How 2 Manning books explain it

Grokking AI Algorithms, Second Edition

A generative image model is a model that creates or synthesizes images from learned visual patterns.

How it is used
Creates new, potentially prompt-guided visuals for creative, commercial, and content-production tasks rather than only analyzing existing images.
How deep it goes
Traces diffusion from data preparation through noise-prediction training and generation, while contrasting a small implementation with much larger systems.
Examples
Traces a tree emerging from noise and describes concept art, advertising visuals, and product mockups.
What you can do after
Build it from scratch as a small diffusion model by preparing images, training a noise predictor, and generating an image.

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Deep Learning with Python, Third Edition

A model that generates images, here assumed to operate at large scale and potentially use ViT-style architectures.

How it is used
Generates new images by sampling learned visual spaces, either unconditionally or under text guidance.
How deep it goes
Explains latent spaces, compares model families, and demonstrates diffusion training, but stops short of detailed architecture internals.
Examples
Uses generated flowers, imaginary images, latent-space interpolation, image editing, and language-guided generation.
What you can do after
Build it from scratch with diffusion training, or operate a pretrained text-to-image model for realistic image synthesis.

Read how Deep Learning with Python, Third Edition explains image generation model →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.