Stable Diffusion Prompts for Image Generation
Positive: [real subject], [real style], [real lighting], high quality, detailed | Negative: [real things to avoid, e.g. "blurry, distorted hands, extra limbs, low quality"]. Pairing both prompts gives Stable Diffusion the full picture that a positive prompt alone can't provide.
Stable Diffusion prompts work by combining real positive descriptors with real negative prompts — specifying what you want and explicitly what to avoid — since this platform’s negative prompt feature is a genuinely powerful control most other generators handle differently or not at all.
What this produces
A base prompt built from real subject and style detail paired with a real negative prompt, guidance on real common negative-prompt terms worth knowing, and an approach to real weighting syntax for emphasizing specific elements.
Variations
Three ready-to-copy versions for Stable Diffusion generation.
Positive: {real subject}, {real style}, {real lighting}, high quality, detailed
Negative: {real things to avoid, e.g. “blurry, distorted hands, extra limbs, low quality”}. Pairing both prompts is what actually gives Stable Diffusion the full picture — the positive prompt alone can’t rule out common artifacts the negative prompt handles.
{real subject}, ({real element you want emphasized}:1.3), {real style}, {real other details}. The weight value controls how strongly that element is emphasized — start around 1.2-1.3 and adjust from there rather than jumping straight to an extreme value.
{real subject}, in the style of {real art movement or technique, e.g. “impressionist painting” or “1980s anime”}, {real color palette}, {real composition}. Naming a specific real movement or technique produces a more consistent result than a vague “artistic” descriptor.
FAQ
What’s a good starting negative prompt for most images?
Common starting terms include things like “blurry, low quality, distorted” — but build your own real list based on issues you actually encounter with your specific model and subjects.
Does weighting syntax work the same across all Stable Diffusion interfaces?
Syntax can vary slightly between different interfaces and versions — check your specific tool’s documentation for its exact supported format.
Which model version should I use?
This depends on your specific needs and what’s currently available — check current documentation for the latest model versions and their particular strengths.
Can I use both positive and negative prompts with img2img generation?
Yes — the same positive and negative prompt approach generally applies whether you’re generating from text alone or from a real reference image.
