AI image generation uses negative prompt words to explicitly instruct the model about elements, features, or styles that should not appear in the generated output. By providing these negative keywords alongside the main (positive) prompts, users can refine and control the visual outcome more precisely.
When a user inputs a prompt to generate an image—such as “a serene mountain landscape at sunrise”—they might also include negative prompts like “blurry, people, buildings, text, noise.” These negative terms guide the AI to avoid including undesired objects, artifacts, or visual distortions in the final image.
For example:
In this case, the AI will attempt to create a sharp, high-quality image of a futuristic city with flying cars and glowing lights, while actively avoiding elements like blurriness, distorted structures, human figures, or any textual overlays.
Negative prompts are particularly useful for eliminating common unwanted artifacts in AI-generated images, such as:
In the context of cloud-based AI image generation services, platforms like Tencent Cloud's AI-powered image synthesis tools often provide interfaces where users can input both positive and negative prompts. These services leverage advanced diffusion models and deep learning techniques to interpret the prompts and generate visuals that align closely with the user’s vision. By using negative prompts effectively, users can achieve higher-quality, more tailored results with fewer iterations.