**Architecture** Generator, neural network, deep learning, GAN (Generative Adversarial Network), convolutional layers, upsampling, downsampling, batch normalization, ReLU activation, Adam optimizer. **Training** Dataset, images, labels, batches, epochs, iteration, loss function, backpropagation, gradient descent, regularization techniques. **Image Synthesis** Realistic, high-resolution, diverse, stylized, abstract, portrait, landscape, object-centric, scene-centric, texture, pattern, color palette. **Style and Aesthetic** Cinematic, cartoonish, watercolor, oil painting, sketch, pixel art, low-poly, futuristic, retro, minimalist, maximalist. **Subject and Composition** Portrait, full-body, landscape, cityscape, still life, animals, vehicles, architecture, food, flowers, abstract shapes, surrealism. **Lighting and Atmosphere** Natural light, artificial light, softbox, backlight, sidelight, warm tone, cool tone, high contrast, low contrast, misty, foggy, dreamy, eerie. These prompts can be combined and reorganized to create more specific and detailed descriptions for the AI image generator. For example