A practical guide to AI image generation, from diffusion to agents

This practical guide to AI image generation explains how current systems work, the differences between diffusion and multimodal approaches, and lessons from hands-on experiments producing pixel art, photorealistic scenes, and posters. It traces the field from GANs and DeepDream through DALL·E, Stable Diffusion, and the rise of multimodal models that both understand language and generate pixels. Key technical points: most models denoise from random noise (diffusion), many operate in a compressed latent space, generation steps trade speed for quality, and “seeds” give repeatable results when supported. Multimodal models plan and follow instructions better, render readable text, and enable conversational editing; diffusion models are cheaper, run locally, and give deterministic seeds. Historical issues like extra fingers and garbled text have largely been fixed by better data and character-aware tokenization, but taste-level tells remain.

The guide gives practical, implementation-focused advice: you pay for canvas size not content, providers’ APIs change frequently, and prices vary by orders of magnitude. Prompting works best when you describe how an image was made (camera, film, layout) rather than vague superlatives. Production pipelines should be hybrids: let AI handle creative generation, use deterministic code for sizing, palettes and layout, and wrap providers with thin interfaces, logging, and retryable jobs. Multimodal agents can iterate - writing prompts, calling APIs, and evaluating results - but human taste and careful testing remain essential.