How does AI image generation actually work?

22 Tem 14:56

When talking about text-to-image systems, there are two main approaches to consider: **diffusion models** (like Stable Diffusion and DALL·E 3) and **transformer-based architectures** (like Imagen). I worked with Imagen for a long time and noticed that diffusion models tend to produce more stable images. The process of starting from noise and gradually refining it into a clear image ensures that even the smallest details of the text prompt are captured. For example, if someone says "photorealistic cyberpunk park," the result is almost indistinguishable from a real photo—something most transformer models struggle to achieve. Another advantage of diffusion is its ability to generate high-resolution outputs. I once tested it at 1024x1024 resolution and found that the composition remained intact, whereas older transformer models often had issues with facial expressions or lighting. However, diffusion models have their drawbacks too: training takes a very long time, and hyperparameter tuning requires precision. When working with Imagen, adjusting the "CFG scale" parameter to find the right balance sometimes took hours. On the other hand, transformers excel in text understanding and stylistic flexibility—they handle abstract terms like "poetic surrealism" much better. In the end, if you want stable, high-quality images, diffusion models are the way to go; if abstract text interpretation is your priority, transformer-based solutions are more efficient. Personally, I see a lot of potential in hybrid models (like Kandinsky 3.0) that combine the strengths of both—imagine the visual quality of diffusion with the language comprehension of transformers, leading to a real leap in output quality.