Classifier Guidance

Key Insight

Classifier guidance was the first trick that made conditional DDPM samples both sharp and on-target: you train a separate image classifier on noisy images, then during sampling nudge each denoising step in the direction its gradient says will make the chosen class more likely. Intuitively the classifier whispers "a little more cat-ness, this way" at every step, and that push — a score-like signal, the gradient of a log-probability with respect to the image — sharpens the output toward the requested class. The catch is that it needs an extra, specially-trained noisy classifier, exactly the cost that classifier-free guidance (CFG) later removed by folding the same effect into the diffusion model itself. This project builds the original: train the noisy classifier on CIFAR-10 and use its gradients to steer the samples.