The conformational landscape of proteins is crucial to understanding theirfunctionality in complex biological processes. Traditional physics-basedcomputational methods, such as molecular dynamics (MD) simulations, suffer fromrare event sampling and long equilibration time problems, hindering theirapplications in general protein systems. Recently, deep generative modelingtechniques, especially diffusion models, have been employed to generate novelprotein conformations. However, existing score-based diffusion methods cannotproperly incorporate important physical prior knowledge to guide the generationprocess, causing large deviations in the sampled protein conformations from theequilibrium distribution. In this paper, to overcome these limitations, wepropose a force-guided SE(3) diffusion model, ConfDiff, for proteinconformation generation. By incorporating a force-guided network with a mixtureof data-based score models, ConfDiff can can generate protein conformationswith rich diversity while preserving high fidelity. Experiments on a variety ofprotein conformation prediction tasks, including 12 fast-folding proteins andthe Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our methodsurpasses the state-of-the-art method.