AI Image Prompts: Midjourney, GPT Image, and Stable Diffusion Guide

Image prompting is not the same as chatbot prompting. You are not asking for an explanation; you are giving a visual brief. Good prompts describe the subject, setting, composition, light, style, camera or medium, and constraints. Great prompts also match the tool.

Midjourney likes concise visual language plus parameters. OpenAI GPT Image works well with natural language and iterative editing. Stable Diffusion rewards structured prompts, negative prompts, and workflow control.

Prompt Formula

Use this structure:

subject + action/pose + setting + composition + lighting + style/medium + technical notes + exclusions

Example:

A ceramic coffee mug on a walnut desk, morning light through a window, close-up product photograph, shallow depth of field, soft shadows, neutral background, no text or logo

Platform Syntax

ElementMidjourneyGPT ImageStable Diffusion
Main promptConcise visual phraseNatural language briefComma-separated weighted terms
Aspect ratio--ar 16:9Ask for format/ratio or use API paramsWidth/height settings
Negative prompts--no text, watermarkState what should not appearNegative prompt field
Style controlStyle refs, moodboards, --style rawDescribe the style in wordsCheckpoints, LoRAs, weights
RepeatabilitySeed and referencesPrompt/version disciplineSeed, checkpoint, sampler
EditingVary, region tools, referencesImage edit / conversational editsInpaint, img2img, ControlNet

Midjourney Prompting

Midjourney currently defaults to V7, with V8/V8.1 alpha testing available separately. Use the current docs because parameters can change.

Basic structure:

editorial portrait of a robotics engineer in a clean lab, soft window light, 85mm lens, realistic detail --ar 4:5 --style raw --v 7

Useful controls:

  • --ar for aspect ratio.
  • --style raw for less default stylization.
  • --v for model version.
  • --no for exclusions.
  • Style references and moodboards for consistent look.

Tips:

  • Keep the prompt visually concrete.
  • Put the most important subject early.
  • Use references for style and consistency.
  • Avoid stuffing ten conflicting styles into one prompt.

GPT Image Prompting

GPT Image models respond well to full creative direction. You can be more conversational than with Midjourney.

Example:

Create a clean square social media graphic for a productivity app. Show a tidy desk with a laptop, a paper planner, and a small plant. Use bright natural lighting, modern minimal composition, and leave empty space at the top for a headline. Do not include any readable text.

Good for:

  • Posters.
  • Product scenes.
  • Text or layout-heavy images.
  • Iterative edits.
  • Transparent backgrounds and asset workflows through API settings.

Tips:

  • Describe the final use.
  • Specify where text or empty space should go.
  • Ask for brand-safe, non-copyrighted, original visuals.
  • Proofread any text in the image.

Stable Diffusion Prompting

Stable Diffusion depends heavily on the model/checkpoint and workflow. A prompt that works in one checkpoint may fail in another.

Example:

(professional product photo:1.3), ceramic coffee mug, walnut desk, morning window light, soft shadows, shallow depth of field, neutral background, realistic, high detail

Negative prompt:

text, watermark, logo, blurry, distorted, extra objects, low quality

Useful controls:

  • Checkpoint/model.
  • LoRA.
  • Seed.
  • Sampler and steps.
  • CFG scale.
  • Negative prompt.
  • ControlNet for pose, depth, line art, or composition.
  • Inpainting for local fixes.

Tips:

  • Choose the right model first.
  • Use negative prompts for repeated problems.
  • Keep seeds for repeatable results.
  • Use ControlNet when composition matters.

Composition Words That Work

Use concrete composition language:

  • Centered composition.
  • Rule of thirds.
  • Wide establishing shot.
  • Close-up macro.
  • Over-the-shoulder view.
  • Low-angle heroic shot.
  • Top-down flat lay.
  • Negative space on the left.
  • Symmetrical layout.
  • Leading lines toward the subject.

Lighting Words That Work

Lighting often changes image quality more than style words:

  • Soft window light.
  • Golden hour.
  • Blue hour.
  • Overcast daylight.
  • Studio softbox.
  • Rim light.
  • Backlit silhouette.
  • Dramatic side lighting.
  • Volumetric light.
  • High-key product lighting.

Style Words That Work

Use medium and production context:

  • Editorial photography.
  • Product photography.
  • Children’s book illustration.
  • Technical diagram.
  • Watercolor illustration.
  • Ink drawing.
  • 3D render.
  • Vector poster.
  • Minimal UI mockup.
  • Cinematic still.

Avoid asking for a living artist’s exact style. Use broader movements, media, and qualities instead.

Negative Prompting

Use exclusions for common failures:

ProblemExclusion
Unwanted textno text, no letters, no watermark
Messy handsno extra fingers, no distorted hands
Bad logosno brand logos, no trademarks
Wrong moodno dark lighting, no dramatic shadows
Clutterminimal background, no extra objects

For GPT Image, phrase exclusions naturally. For Midjourney, use --no. For Stable Diffusion, use the negative prompt field.

Workflow for Better Results

  1. Start broad and generate options.
  2. Pick the strongest composition.
  3. Refine one variable at a time: lighting, pose, background, style.
  4. Use references when consistency matters.
  5. Edit locally instead of regenerating everything.
  6. Save prompt, seed, model, date, and tool version for repeatability.

FAQ

How long should an image prompt be?

Long enough to specify the visual brief. Midjourney often works well with concise prompts. GPT Image handles longer creative briefs. Stable Diffusion depends more on weights, model, and negative prompts than raw length.

Why does my image contain weird text?

Older models often struggle with text. Use GPT Image or Ideogram for text-heavy outputs, and still proofread carefully.

How do I make a consistent character?

Use reference images, consistent descriptions, seeds where available, and the same model/version. For Stable Diffusion, a LoRA or fine-tuned workflow may be needed.

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