Learning Quantized Adaptive Conditions for Diffusion Models

The curvature of ODE trajectories in diffusion models hinders their ability to generate high-quality images in a few number of function evaluations (NFE). In this paper, we propose a novel and effective approach to reduce trajectory curvature by utilizing auto-encoded guidance. By employing a extremely light-weight quantized guidance encoder, our method incurs only an additional 1% of training parameters, eliminates the need for extra regularization terms, yet achieves significantly better sample quality. In contrast to previous work, our approach retains the key features of score-based diffusion without hindering the use of other acceleration methods. Extensive experiments verify that our method can generate high quality results under extremely limited sampling costs. With only 6 NFE, we achieve 5.14 FID on CIFAR-10, 6.91 FID on FFHQ 64×64 and 3.10 FID on AFHQv2.