This page is part of danielball.com, the personal website of Daniel Ball, and serves as his general guide to AI image generation. Rather than a product landing page, it is a long-form write-up built from the author’s own experiments calling several image providers from Python. The scope ranges from a short history of the field to how models work at a high level, making it a useful starting point for readers who want a structured overview.
The guide covers diffusion models and multimodal models, including the question of whether multimodal models are diffusion under the hood, and compares their behavior. It explains seeds, the different types of image generation, and the practical routes for using them. A notable section details what you actually get from ChatGPT, how billing works, the idea that you pay for the canvas, and the cost per usable image, with a landscape snapshot dated October 2026.
Prompting receives substantial attention, with advice to describe the camera rather than the view, rules of thumb, and a case study on making a poster that does not look AI generated. The author also discusses pixel art as a style rather than a format, illustrated with many sprites, plus photorealistic card table backgrounds and restaurant posters drawn from his experiments.
Developers building image generation into their own code and agents will find the most value, along with designers and marketers who want a realistic sense of output quality and cost. Read the TLDR first, then j
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