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Theory Lesson · Image & Diffusion Models

Diffusion Models, Actually

Not a lab — a read. What a diffusion model is actually doing when it turns noise into a picture, the different ways one can be handed a prompt, and why the best image pipelines don't send your exact words to the model at all.
Done

The prompt you type is never the only prompt.

By the time your one sentence reaches an actual diffusion model in a real pipeline, it's usually been checked, decomposed, expanded by a language model, and rewritten once per target model — and none of that is a magic trick, it's a documented sequence of ordinary steps, the same "physics, not vibes" instinct this track's encryption lesson already built. The lab that follows this lesson runs that real sequence against Fire's own image endpoints and shows you the expansion happening in a live response, not just described in a diagram.
Grounded in Fire's own /v1/image and /v1/workflows/image endpoints — API.md, this portfolio's real, currently-running image pipeline.
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