Image Generation
Producing images from a text description.
Overview
Image generation turns a written prompt into an image, usually by reversing a learned noising process. It solves the problem that describing an image in words is far easier than drawing one. It exists because diffusion models made the output sharp and the training stable, and the interesting engineering is in conditioning: how the prompt steers the result, and how much control a user gets beyond a single sentence.
Technology Graph
How Image Generation connects to the rest of the stack.
Uses and depends on
- Attention concept Algorithms Image Generation uses Attention High ✓ source
- Diffusion technique Techniques Image Generation uses Diffusion High ✓ source
- GAN architecture Algorithms Image Generation uses GAN High ✓ source
Applied to
- CNN architecture Algorithms CNN used for Image Generation Medium ✓ source
- Diffusers library Frameworks Diffusers used for Image Generation High ✓ source
- Gradio library Applications Gradio used for Image Generation High ✓ source