Get a Midjourney prompt from any image instantly — with --ar and style baked in
Skip the guesswork on --ar, --v 6, and --style. Get a v6-ready prompt paired with correct parameters in one step.
Upload → generate → copy in seconds.
Free · No signup · --ar + --v 6 included
Real Examples You Can Copy
From photo to Midjourney-ready in one click
Real prompts generated from real images — ready to use.
How to Use This Tool
Image to Midjourney prompt in 3 steps
Why it works
Why these prompts work in Midjourney
Midjourney reads prompts differently from other models — this formats output for its v6 parser.
Part of the image-to-prompt toolkit. Also works with other models — try the Stable Diffusion generator, Flux prompt generator, or Gemini generator.
Midjourney vs Stable Diffusion prompts
Midjourney interprets prompts as natural-language descriptions paired with parameters like --ar, --v, and --style. Stable Diffusion, by contrast, expects comma-separated keyword tags with explicit weighting. A prompt that works well in one model often produces poor results in the other without reformatting.
Midjourney v6 produces the best results when the prompt includes its native parameters (--ar, --s, --v) alongside descriptive keywords. The output here includes those parameters set to sensible defaults inferred from the image, which you can then adjust before submitting.
FAQ
Frequently asked questions
Related tools
Continue your workflow
Working with multiple generators? Each tool below speaks its own dialect.
How Midjourney prompts actually work
Midjourney prompts look simple on paper — a comma-separated list of nouns and adjectives plus a few parameters — but the format encodes more information than it appears to. Below is a short editorial primer on what makes a Midjourney prompt different from prompts for other models, and what to do with the output of this tool.
Parameters carry information that text cannot
The double-dash flags at the end of a Midjourney prompt are not optional decoration. They tune the generation directly: --ar 16:9 sets aspect ratio without you having to specify framing in words; --s 250 raises the stylisation strength so the model leans further into its trained aesthetic; --v 6.1 pins the model version so output stays consistent across runs; --style raw reduces Midjourney’s default photo-stylising instinct for more literal results.
A Midjourney prompt without parameters is functional but underspecified. The tool generates a parameter set tuned to the image — you can adjust them after, but the defaults are usually a reasonable starting point.
Why Midjourney rewards evocative language over precise technical tags
Midjourney was trained on curated image+caption datasets that lean editorial — film stills, fine-art reproductions, fashion photography. The model responds to the kind of language those captions use: mood words, atmospheric descriptors, art-movement references, cinematographic vocabulary. cinematic, golden hour, melancholic atmosphere produces stronger output than f/1.8, 35mm, ISO 400, even though the second is more technically precise.
This is the inverse of how Stable Diffusion responds. SDXL rewards weighted technical tags; Midjourney rewards atmospheric description. A working Midjourney prompt run through Stable Diffusion produces flat output, and vice-versa. The two formats are not interchangeable.
What to edit before submitting
Three quick adjustments to most generated Midjourney prompts:
Adjust the aspect ratio. The tool defaults to whatever ratio matches the source image. If you want a different framing (square for Instagram, vertical for Stories, ultrawide for desktop wallpaper), change the --ar value before submitting.
Tune --s to your taste. The default stylisation value (around 100) is balanced. Push higher (250-500) for more aggressive stylistic interpretation; lower (25-50) for more literal output. There’s no right answer; it depends on whether you want the model’s aesthetic instincts to lead.
Drop one mood word. Generated prompts sometimes stack two or three mood descriptors. If melancholic, dreamlike, atmospheric all appear, pick the strongest one and delete the others. Stacking competing mood words confuses the model — it tries to satisfy all of them and lands somewhere generic.
For prompts in other models’ formats, see the Stable Diffusion generator, Flux generator, or the generator hub for the full comparison.