A practical guide to AI image generation, from diffusion to agents(danielball.com)
282 points by westsmith 5 days ago | 94 comments
tl;dr: Image generation models fall into two camps: diffusion models (cheap, fast, seed-controllable, but literal) and multimodal models like GPT Image and Gemini (better at instructions, text, and editing, but pricier and less controllable). You pay per canvas regardless of complexity, prices range 100x across providers, and effective prompting means describing the production process (camera, film, print) rather than hype words. Build thin provider abstractions since APIs change constantly, offload exact work (sizing, text, transparency) to deterministic code, and use agents to write prompts, critique outputs, and iterate—while keeping humans in charge of taste.
HN Discussion:
  • ↑Suggests complementary techniques like procedural graphics or drawing/modeling scenes first to improve generation
  • ↓Article mishandles pixel art by treating it as a style rather than a hardware-constrained format
  • ↓Article is low-value, feels AI-generated and lacks substantive insight
  • ~Article overlooks local/self-hosted generation as a viable alternative to paid APIs
  • •Asks clarifying technical questions about seeds, suffixes, and ControlNets mentioned or omitted