Title: Controllable Diffusion via Optimal Classifier Guidance
Primary area: generative models
Keywords: diffusion, classifier guidance, reinforcement learning, controllable generation

Abstract: The controllable generation of diffusion models aims to steer the model to generate samples that optimize some given objective functions. It is desirable for a variety of applications including image generation, molecule generation, and DNA/sequence generation. Reinforcement Learning (RL) based fine-tuning of the base model is a popular approach but it can overfit the reward function while requiring significant resources. We frame controllable generation as a problem of finding a distribution that optimizes a KL-regularized objective function. We present SLCD -- Supervised Learning based Controllable Diffusion, which iteratively trains a small classifier to guide the generation of the diffusion model. Via a reduction to no-regret online learning analysis, we show that the output from SLCD provably converges to the optimal solution of the KL-regularized objective. Further, we empirically demonstrate that SLCD can generate high quality samples with nearly the same inference time as the base model in both image generation and biological sequence generation.