Title: Dual-Solver: A Generalized ODE Solver for Diffusion Models with Dual Prediction
Primary area: generative models
Keywords: Generative Models, Diffusion Models, Fast Sampling, ODE Solver

Abstract: Diffusion models deliver state-of-the-art image quality. However, sampling is costly at inference time because it requires many model evaluations (number of function evaluations, NFEs).
To reduce NFEs, classical ODE multistep methods have been adopted. Yet differences in the choice of prediction type (noise/data/velocity) and integration domain (half log-SNR/noise-to-signal ratio) lead to different outcomes.
We introduce Dual-Solver, which generalizes multistep samplers by introducing learnable parameters that continuously (i) interpolate among prediction types, (ii) select the integration domain, and (iii) adjust the residual terms.
It maintains the traditional predictor-corrector structure and guarantees second-order local accuracy.
These parameters are learned with a classification-based objective using a frozen pretrained classifier (e.g., ViT or CLIP).
On ImageNet class-conditional generation (DiT, GM-DiT) and text-to-image (SANA, PixArt-$\alpha$), Dual-Solver consistently improves FID and CLIP scores in the low-NFE regime (NFE $\le 6$) across backbones.

TL;DR: This work generalizes multistep samplers via parameterizing the prediction type, integration domain, and second-order residual term, and introduces classification-based parameter learning.