How Midjourney interprets prompts differently
Midjourney processes prompts differently from DALL-E or Stable Diffusion. It tends to favor aesthetic interpretation over literal accuracy, which means your style keywords carry more weight here than in other models. Understanding this bias is critical: Midjourney will often beautify your prompt even when you did not ask it to. This guide covers how to work with that tendency, not against it, to get the most controlled and polished results from your prompts.
Essential parameters every user should know
The core parameters you need to know are --ar for aspect ratio, --stylize (or --s) for how much artistic interpretation the model applies, --chaos for variation between outputs, --quality for rendering detail, --no for negative prompting, and --seed for reproducibility. The --ar parameter is essential for any non-square output. Use --ar 16:9 for landscapes, --ar 9:16 for vertical social content, and --ar 3:2 for editorial photography. The --stylize parameter ranges from 0 to 1000, where lower values follow your prompt more literally and higher values let Midjourney apply more artistic license.
Style keywords that consistently produce results
Certain style keywords work exceptionally well in Midjourney because the model was trained with strong aesthetic preferences. Terms like cinematic, editorial, dramatic lighting, atmospheric, moody, and hyper-detailed consistently produce more polished results. Camera-specific terms like shot on Hasselblad, 85mm portrait lens, and medium format also help because Midjourney understands photographic language deeply. Avoid generic terms like beautiful or amazing, as they add no useful visual direction.
Advanced techniques for professional output
For professional-level output, master the art of prompt weighting using :: syntax. You can write landscape::2 autumn forest::1 to give more emphasis to the landscape composition than the forest detail. Multi-prompt syntax lets you separate concepts with :: to prevent them from blending. Image prompting with URLs lets you use reference images as style guides. Permutation prompts with {option1, option2} generate multiple variations in one command. These advanced features are what separate casual users from professionals.
Midjourney v7 changes and what they mean for prompts
Midjourney v7 brought significant changes to how prompts are interpreted. The model now handles longer, more descriptive prompts better than previous versions. It also improved at understanding spatial relationships, so terms like foreground, background, and left side are more reliable. The --stylize parameter now has a wider effective range, and the default quality has been raised. If you are using prompt templates from v5 or v6, they will still work but may benefit from less aggressive parameter usage since the base quality is already higher.