Fast image models: judging speed, layout, and text rendering

Why speed and layout fidelity matter together Speed isn't a vanity metric. It reshapes the workflow: with sub‑second previews you iterate five to ten times more per minute, catch mistakes early, and explore composition rather than babysit progress bars. Public benchmarks moved in this direction too - MLPerf added an SDXL‑based text‑to‑image task in 2024, and the industry increasingly reports p95 latency instead of cherry‑picked bests. Protocol I use Outputs are fixed at 2K as the baseline, 4K when supported. Every run logs time‑to‑first‑pixel (TTFP) and time‑to‑full‑render, cold and warm. The seeds, prompts, and negative prompts live in versioned JSON. Reference images are stored next to the prompts to avoid accidental drift. Text and layout accuracy over vibes I evaluate three families of tasks: poster typography, chart/diagram alignment, and UI wireframes. The model must place letters legibly, keep grids straight, and obey margins. This predicts real usefulness far more than a handful of “wow” portraits. Identity consistency with multi‑reference For brand or character work I expect identity to hold across a batch. With 3 - 6 references, hairline, eye shape, and profile should survive angle and lighting changes. When identity collapses under batch generation, the cost of manual curation wipes out the headline speed. Cost and memory footprint I track cost per megapixel and peak VRAM. A fast model that explodes memory or doubles egress isn't fast in production. The budget target is “2K under ~2s, stable peak memory, predictable spend per 100 images.” Failure catalog I keep a small gallery of misses: broken ascenders/descenders, mirrored characters, warped grids, faces that drift after the third sample. This catalog guards against regressions when upgrading backends or sampling parameters. Signals from recent releases Open and commercial labs report steady wins on layout and text rendering, while identity and small‑font typography remain the hardest edges. The useful habit is to pin your own house rubric so vendor swaps don't change quality silently. Repeatability and releases Bench artifacts live in the repo: prompts, seeds, refs, and a short rubric. When models or samplers change, I rerun the same suite and diff the gallery so creative teams see exactly what improved or regressed. Verdict Choose models that balance speed with typography and layout fidelity. Keep your own mini‑benchmark and rerun it on upgrades; it keeps marketing honest and output consistent.