What is the primary purpose of using negative prompts during image generation?

The primary purpose of using negative prompts during image generation is to explicitly prevent the model from including certain elements, styles, or characteristics in the output image. Positive prompts guide the model towards what to generate, while negative prompts steer it away from undesired outputs. This is crucial because AI models, even with sophisticated prompting, can sometimes introduce unwanted artifacts, generate elements that don't align with the overall vision, or misinterpret aspects of the positive prompt. For example, if you're generating an image of a person, you might use negative prompts like 'blurry', 'deformed hands', or 'double head' to prevent the model from producing images with these common errors. Similarly, if you want a painting in a specific style, you might use negative prompts to exclude other styles, such as 'photorealistic' or 'cartoonish'. Negative prompts act as constraints, refining the generation process and increasing the likelihood of achieving the desired visual outcome. They are particularly effective for addressing recurring issues or biases inherent in the model's training data. The effectiveness of negative prompts relies on the model's ability to understand and interpret these constraints accurately, and often requires experimentation to identify the optimal combination of positive and negative prompts.