What is diffusion model guidance (classifier-free guidance) in vision generation?

Classifier-free guidance improves diffusion outputs by combining conditional and unconditional predictions.

Classifier-free guidance modifies diffusion sampling by interpolating between conditional and unconditional denoising predictions. This amplifies the effect of conditioning signals like text prompts, improving alignment and image fidelity. It removes the need for a separate classifier while still providing controllable generation.