AI models generate images from prompts using a process called Generative Modeling. Here’s a simplified explanation:
- Training: The AI model is trained on a large dataset of images. During this process, the model learns to understand the patterns and features in the data. For example, it might learn what makes an image of a cat look like a cat - the shape of the ears, the size of the eyes, etc.
- Generation: Once trained, the model can generate new images. When given a prompt (like “a cat sitting on a mat”), the model uses its understanding of the data to create an image that fits the description. It does this by generating pixels one by one, each time predicting the next pixel based on the pixels it has already generated and the prompt.
- Refinement: The generated image is then refined. The model might adjust colors, shapes, and other details to make the image look more realistic.
This is a very high-level explanation and the actual process involves complex mathematical operations and algorithms. The exact method can vary depending on the specific type of AI model used (like GANs, VAEs, etc.).
