Classifier-free guidance controls generation strength by mixing conditional and unconditional predictions. Problem: Sampling from conditional diffusion models can produce outputs that don't strongly match the condition (text prompt). Solution: Amplify difference between conditional and unconditional predictions. Steer more strongly toward condition. Formula: ε̃ = ε_unconditional + w × (ε_conditional - ε_unconditional), where w is guidance scale (typically 7-15). Higher w = stronger conditioning but less diversity. Training: Drop conditioning randomly during training (10-20% of time), model learns both conditional and unconditional generation. Inference: Run model twice per step (with and without condition), combine predictions using guidance formula. Effect of guidance scale: w=1 is pure conditional, w>1 amplifies conditioning, high w can cause artifacts/saturation. Trade-offs: Higher guidance = better prompt following but reduced diversity, may cause over-saturation. Alternative: Classifier guidance uses separate classifier gradients (requires training classifier). CFG is simpler; no classifier needed. Standard practice: Default in DALL-E, Stable Diffusion, Midjourney. Essential for controllable high-quality generation.