Color is not the only indicator in graphics
Generated infographics encode meaning in hue. Add patterns, labels, and shape so a color-blind viewer can read the chart, then check deuteranopia.
Every guide, comparison and workflow we’ve published on Image Generation.
32 articles — page 1 of 2
Generated infographics encode meaning in hue. Add patterns, labels, and shape so a color-blind viewer can read the chart, then check deuteranopia.
A canvas far above native size duplicates subjects and limbs. Generate at the model's real ceiling, then upscale, and the test that finds that ceiling.
Shadow edge hardness is set by apparent source size, not by adjectives. A size-and-direction vocabulary that outperforms five style words in every prompt.
Weighted mixes of faces a model already knows produce a novel character that reproduces. The syntax, the ratios that stay stable, and how to lock the result.
Cranking sampling steps almost never fixes softness. A three-question diagnosis separating resolution ceiling, sampler mismatch and guidance from step count.
Mixed lighting, crops and resolutions make a model average two looks into a third person. The hard spec for a reference set that holds one identity.
Named portrait setups encode a lamp position, not a mood. The five worth memorising, the extra clause each one needs, and where the label stops working.
Seeds do not survive precision, hardware or library changes. Pin dtype, batch size, sampler and library version with the seed if a render must be reproducible.
Models default to mid-scale unless something anchors the frame. The lens, camera height and foreground cues that make a subject read enormous.
Negative prompts are a no-op on flow-matching models and leak the negated noun elsewhere. Positive re-specification and the three constraints that replaced it.
Batch size changes the noise each sample gets. A batched call and eight parallel calls diverge, even with the same seeds. Know when that variance is harmless.
Long-window image models take far more instruction than most prompts supply. A block structure for long prompts, and where extra tokens stop mattering.
A class of image model that plans before it paints. Where the extra deliberation measurably helps, where it's wasted, and which models actually do it.
A seed initializes AI video starting noise. Same prompt plus same seed can replay a take; it is not a lock across samplers.
Make quote reels with AI: typography-first cards, subtle loop motion, slideshow assembly, and a batch system that produces a month of reels in an hour.
Image-to-video vs reference-to-video explained: one animates your exact frame, the other recasts your subject in new scenes. When to use each, simply.
An AI thumbnail workflow that earns the click: the psychology of high-CTR thumbnails, five proven archetypes, and a generate-test-iterate loop that compounds.
Build a complete channel branding kit with AI: logo, banner, thumbnail template, lower thirds, intro sting, and caption style — one coherent system in a day.
The best Midjourney alternatives in 2026, reviewed by job: typography, product shots, photorealism, brand assets — and why multi-model access wins.
AI content for ecommerce product catalogs at scale: the per-SKU asset kit, reference images for consistency, batch image generation, on-pack text, and QA rules.
Product shot prompts for AI image models: the angle, surface, and lighting vocabulary that turns generic renders into shelf-ready ecommerce visuals.
Style keywords that actually change AI images: lighting, lens, and medium terms that move pixels, the words that do nothing, and a cheap test protocol.
Negative prompts for AI video and images: when exclusion beats positive phrasing, what modern models still need excluded, and a minimal stack that works.
Character description prompts for consistent AI people: writing a character block, which traits stick between generations, and when to move to references.