A reference for picking models, designing source screenshots, handling expansive and compact languages, writing custom instructions, and exporting at the right device size. Grounded in how lokal actually renders — no guesswork.
lokal supports four image models, two from OpenAI and two from Google. The trade-off is quality versus credit cost — and the cost is set per render, not per locale.
1cr low · 2cr medium · 3cr high
Strongest on dense, info-heavy UI — long lists, tabular layouts, multi-column dashboards. The high preset is the most pixel-faithful option lokal can render.
Pick when: Hero screenshots; dense interfaces; anywhere small mistakes will be noticed.
1cr low · 2cr medium · 3cr high
Same cost ladder as GPT Image 2, slightly less faithful on edge cases. Good baseline for first-pass batches and for screenshots that are mostly typography on a flat background.
Pick when: First-pass batches; simple typographic screenshots.
2 credits · fixed quality
Fixed-quality. Excellent at preserving illustration style and brand color. Two credits per frame, no quality dial.
Pick when: Illustrative or photo-rich screenshots; brand-driven hero shots.
1 credit · fixed quality
Cheapest render in the catalog at one credit per frame. Quick and good enough for previews, draft locales, and screenshots you have not pinned a model on yet.
Pick when: Previews; draft passes; screenshots where price beats fidelity.
The model is told to keep the layout pixel-identical — same colors, icons, illustrations, spacing, font family, font weight, alignment and overall image dimensions. The only thing that changes is the language of the text.
That contract works when the source gives the model room to breathe. Five rules:
lokal applies different rephrasing rules per language group. Knowing which group you are translating into is the single biggest predictor of how clean the first pass will be.
These ten languages run longer than English at the same font size. The default behaviour is to pick the shortest natural phrasing, drop filler words and break compound words rather than overflow a container. German is the worst offender — expect occasional re-rolls on tight buttons.
These five languages are typically equal-to-shorter than English in pixel width. The default behaviour is to translate fully — no compression, no abbreviation. Overflow is rare; the main thing to watch is that your source font has full CJK glyph coverage.
The remaining locales — English, Arabic, Hindi, Swedish, Indonesian — fall into a neutral group: translate naturally and concisely, do not invent content, keep each string inside its bounding box.
Every re-roll accepts a custom instruction. It stacks on top of the default rules — it cannot override the two hard rules (“translate every string”, “do not break the layout”), but it overrides every other default when in conflict.
Translation reads too formal for a consumer app
“Use informal address (tu / du / 너) and casual phrasing.”
Translation drifts away from your terminology
“Prefer the term "workspace" over "project"; keep the word "app" untranslated.”
German / Russian copy is overflowing buttons
“Aim for the shortest natural phrasing; break compound words if needed; never let text overflow.”
Japanese honorific level is off for the brand voice
“Use ですます-form, friendly but not overly polite. No 敬語.”
Keep instructions specific and short. A one-line directive (“use du-form”, “keep the word ‘app’ untranslated”) lands more reliably than a paragraph describing the brand voice.
Default export keeps the source resolution untouched and ships a per-language ZIP with your original filenames. For the App Store, four device presets resize the output to the dimensions Apple expects.
The export ZIP is named <lang>-translations.zip. Inside, every frame keeps the filename you uploaded — drop the archive straight into App Store Connect without renaming.
A two-pass strategy keeps cost predictable without compromising hero quality. Example: 10 screenshots × 20 languages = 200 renders.
Pass 1 — draft on Nano Banana 2
200 renders × 1 credit each. Quick, cheap, good enough to review every frame side-by-side.
200 credits
Pass 2 — promote ~20% to GPT Image 2 medium
The frames Apple is going to scrutinize: the first two screenshots in each locale. ~40 renders × 2 credits.
+ 80 credits
Total
Compared to running everything on GPT Image 2 high (600 credits), the two-pass strategy is roughly 53% cheaper — with the same hero quality where it matters.
280 credits