A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images. Which solution…

Zero-shot prompts are used for tasks where no examples are given, relying on the model's pre-trained knowledge, and do not specifically address the issue of unrelated image generation.

Negative prompts are a powerful technique in image generation where you specify what you explicitly *don't* want to appear in the generated image. By providing keywords for unrelated or undesirable elements in a negative prompt, the model is guided away from generating those features, thereby increasing the relevance of the output to the desired positive prompt.

Positive prompts define what *should* be in the image. While essential, simply using positive prompts without negative prompts might still result in unrelated elements if the model's understanding or training data biases lead it astray.

Ambiguous prompts would likely increase the chance of generating unrelated or unexpected images, as they provide less clear guidance to the model.