What is image generation?
Image generation is the creation of new images by an AI model from a text description, a sketch, or an existing image. A user types “a stainless steel conveyor in a clean food plant, overhead view” and receives several photorealistic candidates in seconds. The same models can edit an existing image: remove a background, change a product’s color, or extend a photo beyond its original frame.
Most current systems are diffusion models. During training, the model learns to remove noise from images step by step. During generation, it starts from pure noise and removes it under the guidance of the text prompt until an image emerges. Generative adversarial networks (GANs) were the earlier approach and still appear in some face and upscaling tools. Autoregressive models, which produce an image piece by piece the way a language model produces tokens, are the other major family.
Image generation in practice
A furniture retailer photographs each product once against a plain background and uses image generation to place it in a dozen room settings for the website and catalog, reducing the number of studio shoots it needs. A packaging manufacturer generates variations of label designs for a customer to react to before a designer refines the chosen direction. In a different use, a quality team at an electronics plant generates synthetic images of rare solder defects to supplement the handful of real examples available for training its inspection model, then checks that accuracy on real defects actually improves.
What to watch for
- Clarify the license. Terms differ on whether you own the output, whether it can appear in advertising, and whether your uploads may be used to train the vendor’s model.
- Copyright for AI-generated images is unsettled in many jurisdictions, so keep a human designer in the chain for anything you need to protect.
- Generated images of real products can misrepresent them; a chair rendered with the wrong leg profile is a returns problem.
- The EU AI Act requires that AI-generated images be marked as such and that deepfakes be disclosed, and AI content watermarking and content provenance standards help meet that duty.
- Open-weight image models can run on a company’s own graphics processing units (GPUs), which keeps unreleased product designs off third-party servers.