Midjourney

Midjourney Low Quality Images — How to Fix

If your Midjourney images look blurry, lack sharpness, or fail to capture the detail you described in your prompt, you are not alone. This issue commonly affects new users who are unaware of Midjourney's quality and stylization parameters. Understanding how the rendering pipeline interprets prompts and quality flags is the key to consistently generating crisp, high-detail images.

Why does this error happen?

Midjourney uses a diffusion-based rendering model that balances speed and quality based on default parameter values. By default, the --quality flag is set to 1, which allocates a limited number of GPU processing steps to generate your image — enough for a quick preview but insufficient for fine detail, texture clarity, or high-frequency visual information like hair strands, armor engravings, or intricate backgrounds. Additionally, without explicit stylization instructions or detail-focused prompt keywords, the model defaults to a generalized aesthetic that may appear flat or soft. Low aspect ratios and missing compositional guidance can further compress visual information, reducing the perceived resolution and detail of the final output.

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How to fix it

Add the --quality 2 Parameter

Append --quality 2 (or --q 2) to the end of your prompt to double the rendering steps Midjourney allocates to your image. This significantly increases fine detail, texture fidelity, and edge sharpness. Note that this also doubles the GPU time consumed, so use it intentionally on prompts you are ready to refine.

Boost Aesthetic Output with --stylize 750

The --stylize parameter controls how strongly Midjourney applies its trained aesthetic intelligence to your image. The default value of 100 produces relatively literal interpretations, while --stylize 750 pushes the model toward richer composition, lighting, and visual coherence. Add --stylize 750 to your prompt to unlock more polished and visually compelling results.

Include Detail-Focused Keywords in Your Prompt

Prompt language directly influences the level of detail the model targets during generation. Adding phrases like 'highly detailed, 8k, sharp focus, photorealistic' signals to the model that visual fidelity is a priority. Place these keywords near the beginning or end of your prompt for maximum influence on the output.

Use --ar 16:9 for Better Composition and Clarity

The aspect ratio parameter --ar controls the canvas dimensions of your generated image. A widescreen ratio like --ar 16:9 gives the model more horizontal space to distribute detail and improves compositional balance, especially for landscapes, portraits, and cinematic scenes. Avoid the default square ratio when your subject benefits from a broader frame.

Code example

// Example quality parameters
/imagine a portrait of a knight --quality 2 --stylize 750 --ar 16:9

Pro tip

Always combine --quality 2 with detail-rich prompt keywords before upscaling. Running a low-quality generation and upscaling afterward will not recover lost detail — quality must be set at generation time for the best results.

Frequently asked questions

Does --quality 2 use more of my Midjourney subscription credits?

Yes, --quality 2 consumes approximately twice the GPU minutes of a standard --quality 1 generation. If you are on a limited plan, reserve --quality 2 for final renders rather than early exploration.

Why do my images still look soft after adding 'sharp focus' to the prompt?

Text keywords alone may not override the model's default rendering budget — you must also set --quality 2 to increase the number of diffusion steps. Combine both keyword guidance and the quality parameter for the sharpest output.

Is there a --quality 4 option for even higher detail?

Midjourney currently caps the --quality parameter at 2 for most model versions, as higher values showed diminishing returns in testing. Focus on combining --quality 2 with strong prompt keywords and --stylize for maximum detail.

Does the aspect ratio affect image resolution or just composition?

The --ar parameter affects the canvas shape and compositional framing rather than the raw pixel resolution. However, a better-fit aspect ratio reduces awkward cropping artifacts and helps the model distribute detail more naturally across the frame.

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