Learn how to use negative prompting to remove unwanted artifacts, steer style, and gain precise control over your generations.
Negative prompts tell the model **what you *don’t* want**. They’re essential for eliminating artifacts, avoiding over-stylisation, and achieving a clean final result. This guide explains syntax, common pitfalls, and ready-made check-lists you can paste into your workflow.
Most text-to-image models accept a second string that reduces the probability of listed tokens. Think of it as an inverse wish-list: the stronger the weight, the more vigorously the model suppresses those elements.
Group related flaws with commas: “blurry, distorted, extra limbs”. Use weight syntax if supported: “text::-1.4”. Place generic quality terms first, scene-specific terms last.
A popular baseline: “blurry, low quality, jpeg artifacts, watermark, text, signature, extra fingers, extra limbs, disproportional, mutated, deformed”. Tweak as your style evolves.
Eliminates the most common e-commerce flaws while preserving crisp detail.
Keeps anatomy believable even with highly detailed styles.
Remember that AI image generation is both an art and a science. These techniques provide a foundation, but experimentation and practice are key to mastering your craft. Don't be afraid to break rules and try unconventional approaches!