Compensation Sampling for Improved Convergence in Diffusion Models
Hui Lu Albert ali Salah Ronald Poppe
Diffusion models achieve remarkable quality in image generation, but at a
cost. Iterative denoising requires many time steps to produce high fidelity
images. We argue that the denoising process is crucially limited by an
accumulation of the reconstruction error due to an initial inaccurate
reconstruction of the target data. This leads to lower quality outputs, and
slower convergence. To address this issue, we propose compensation sampling to
guide the generation towards the target domain. We introduce a compensation
term, implemented as a U-Net, which adds negligible computation overhead during
training and, optionally, inference. Our approach is flexible and we
demonstrate its application in unconditional generation, face inpainting, and
face de-occlusion using benchmark datasets CIFAR-10, CelebA, CelebA-HQ,
FFHQ-256, and FSG. Our approach consistently yields state-of-the-art results in
terms of image quality, while accelerating the denoising process to converge
during training by up to an order of magnitude.