Firstly, the advanced AI models used in image generators, such as DALL-E 2 and Imagen, can combine a wide range of concepts, attributes, and styles to create highly photorealistic outputs from text prompts. These models have been trained on vast datasets of images and text, allowing them to understand the complex relationships between visual elements and generate cohesive, realistic-looking compositions.
Secondly, the use of techniques like neural style transfer and generative adversarial networks (GANs) enables these AI systems to capture the subtle nuances of real-world lighting, textures, and spatial relationships. By learning from a diverse set of reference images, the models can convincingly recreate the visual characteristics of photographic scenes, blending elements in a seamless and natural way.