  To sample from an unconditionally trained Denoising Diffusion Probabilistic
Model (DDPM), classifier guidance adds conditional information during sampling,
but the gradients from classifiers, especially those not trained on noisy
images, are often unstable. This study conducts a gradient analysis comparing
robust and non-robust classifiers, as well as multiple gradient stabilization
techniques. Experimental results demonstrate that these techniques
significantly improve the quality of class-conditional samples for non-robust
classifiers by providing more stable and informative classifier guidance
gradients. The findings highlight the importance of gradient stability in
enhancing the performance of classifier guidance, especially on non-robust
classifiers.
