What Image-Gen AI Really Is
The Core Technology
At their core, these systems work by learning to reverse a noise-adding process. During training, the model learns to predict the noise that was added at each step. When generating images, it starts from pure noise and repeatedly removes it, guided by your prompt.
Training Process
- Analyzes billions of image-caption pairs
- Learns to predict noise patterns
- Understands relationships between text and visuals
Generation Process
- Starts with random noise
- Gradually removes noise based on prompt
- Refines details in multiple steps
Key Components
Converts your text prompt into a numerical representation that the model can understand. This is typically done using a CLIP or T5 encoder, which creates a 3D "semantic cloud" of your prompt.
The core neural network that processes the image. It uses a U-shaped architecture to gradually refine the image, with early steps focusing on composition and later steps adding fine details.
The mechanism that connects your text prompt to the image generation process. It helps the model understand which parts of the image should correspond to which parts of your prompt.
Why This Matters
Knowing how these systems work helps you write more effective prompts. For example:
- Early tokens in your prompt have more influence on the overall composition
- Later tokens tend to affect details and refinements
- Understanding cross-attention helps you structure your prompts more effectively
- Knowing about the noise-removal process helps you understand why certain prompts work better than others
Next: Learn about the step-by-step process from prompt to pixels in our From Prompt to Pixels guide.