AI gives a slightly different result on every generation. That is richness for one image and a serious problem for ten posts sitting side by side in a feed.
An account where every post has a different style reads as scattered and amateur. The same problem appears in a book's illustrations, an app's icons and a brand's campaign imagery.
The solution has three layers, each stronger and more laborious than the last. Climb only as far as you need.
Layer 1: Prompt discipline
The simplest layer, and sufficient in most cases. Split the prompt in two: a variable subject section and a fixed style section.
Save the style section as a text block and paste it verbatim at the end of every prompt: "muted terracotta and cream palette, soft natural window light, generous negative space, shot on 50mm". Change only the subject.
Naming palette colours explicitly is that block's most effective part. "Warm tones" is vague and reinterpreted every time; "ochre, teal, cream" is precise.
This layer produces approximate consistency: the images belong to one family without speaking exactly the same language. Usually enough for a social feed.
Layer 2: Style reference
Markedly more reliable than repeating prompts, and the level a publication or brand actually needs.
In Midjourney you pass the link of an image you like with `--sref`; new generations inherit its colour, texture and line character while the subject changes.
In Leonardo AI and Recraft, saved styles do the same job: upload a few reference images to define a visual language, then bind all generations to it.
Recraft adds one advantage: you can define a brand style and generate an entire icon set with the same stroke weight, corner radius and visual weight. Decisive if you are building a design system.
Layer 3: Character and subject consistency
If the same person, product or mascot recurs, style reference is not enough; the subject itself has to stay fixed.
Midjourney's character reference (`--cref`) is built for this. The `--cw` value sets how tightly the likeness binds: high also fixes the clothing, low only the face.
For complete consistency, the stronger method is training a LoRA for that subject on the Stable Diffusion side. Fifteen or twenty images, then invoked in every prompt. This is the real solution for holding one character across thirty pages of a children's book.
The variety of the training images determines the result: all from one angle and the model learns the angle rather than the subject.
Which layer do you need?
For a social feed, layer one is usually enough and layer two improves it markedly.
For a magazine, book or brand campaign, layer two is mandatory. The consistency of the series matters more than the quality of individual images.
If the same character or product recurs, layer three becomes unavoidable — and at that point so does Stable Diffusion.
Frequently asked questions
Isn't repeating the prompt enough?
It gives approximate consistency but not enough; the same prompt produces slightly different results each time because the starting noise is random. Style reference binds markedly tighter.
How do I keep the same character?
Midjourney's --cref parameter is built for this and is sufficient in most cases. For complete consistency, train a LoRA for that character on the Stable Diffusion side.
Can free tools do consistency?
Partly. Leonardo AI's saved styles work on the free tier and give good results. Bing Image Creator and Google Gemini have no style reference; only prompt discipline is available there.