What Actually Makes an AI Portrait Look Real: A Small Parameter Study

We ran a small, controlled set of tests on fal.ai’s FLUX.2 model to see which generation settings actually change how realistic an AI portrait looks — and which ones are mostly noise. This isn’t a definitive study; it’s four isolated comparisons, each with the same subject and seed, changing exactly one thing at a time. Treat it as a starting point worth verifying yourself, not a final rulebook.

1. Prompt phrasing: naming a real camera beats “candid”

It’s tempting to think “unposed” or “candid phone snapshot” would push a model toward something more natural. In our test it did the opposite.

Everything else was identical — same person description, same setting, same seed. Naming an actual camera and lens gives the model something concrete to render: real depth-of-field falloff, real bokeh shape. Vague framing words don’t correspond to any specific visual property, so the model tends to fall back on a flatter, smoother default look.

2. “Photorealistic” as a command vs. describing physical properties

A common instinct is to just tell the model what you want: “photorealistic, hyperrealistic, ultra detailed.” We compared that against describing the physical properties a real photo actually has — skin texture, tonal variation, slightly imperfect focus — without ever using the word “realistic.”

The buzzword version leans toward a smoother, more polished “professional headshot” look. Describing the physical properties directly — texture, tone, focus — produced visibly more natural skin detail in our test. Commanding an outcome seems to be less effective than describing what that outcome physically looks like.

3. Guidance scale: a real but subtle effect

Guidance scale controls how strictly the model follows your prompt versus how much freedom it takes. We compared a low value (1.5) against a high one (4.0), same prompt and seed otherwise.

The difference here is real but subtle — slightly richer contrast and fine detail at the higher value in this example. In a wider round of testing (checking whether a trained face stayed recognizable across seeds, not just visual style), going as low as 1.5 was where things started to break down more often; 2.5 and up held up reliably. Our practical takeaway: don’t drop guidance scale below the model’s default if realism and consistency matter to you, but don’t expect a dramatic before/after from this one alone.

4. Inference steps: more isn’t obviously better

Inference steps control how many denoising passes the model runs. More steps generally means more compute time and cost, so it’s worth knowing whether they’re buying you anything.

In our test, the two are close to indistinguishable. That matches what we’ve seen in broader testing on this model: step count doesn’t move identity or quality much, so it’s a reasonable place to cut cost and generation time without an obvious tradeoff — unlike guidance scale, where going too low has a real cost.

Try it yourself

All four of these are things you can test in a couple of minutes on fal.ai’s own playground: swap “candid” for a named camera and lens, describe skin and focus instead of commanding “photorealistic,” nudge guidance scale, try fewer steps. Results will vary by subject and scene — ours is a small sample, not a guarantee — but the direction of each finding held consistently enough in our own testing to be worth trying first.