Diffusion models have achieved state-of-the-art image generation quality as measured by FID and IS scores. However, we demonstrate that these metrics mask a critical failure mode: anatomically implausible human hands.
Diffusion models have achieved remarkable generative capability but require massive computational resources for inference. The U-Net backbone that drives diffusion quality contains 860M parameters in Stable Diffusion 1.
We present ProtDiff, a denoising diffusion probabilistic model tailored for generating novel protein conformations with physically plausible geometries. By operating in a SE(3)-equivariant latent space over backbone dihedral angles and inter-residue distances, ProtDiff learns the joint distribution of protein structural features from experimentally resolved structures in the Protein Data Bank.