HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion
  Models
Authors: name: Seyedmorteza Sadat, name: Farnood Salehi, name: Romann M. Weber

While diffusion models have made remarkable progress in image generation,
their outputs can still appear unrealistic and lack fine details, especially
when using fewer number of neural function evaluations (NFEs) or lower guidance
scales. To address this issue, we propose a novel momentum-based sampling
technique, termed history-guided sampling (HiGS), which enhances quality and
efficiency of diffusion sampling by integrating recent model predictions into
each inference step. Specifically, HiGS leverages the difference between the
current prediction and a weighted average of past predictions to steer the
sampling process toward more realistic outputs with better details and
structure. Our approach introduces practically no additional computation and
integrates seamlessly into existing diffusion frameworks, requiring neither
extra training nor fine-tuning. Extensive experiments show that HiGS
consistently improves image quality across diverse models and architectures and
under varying sampling budgets and guidance scales. Moreover, using a
pretrained SiT model, HiGS achieves a new state-of-the-art FID of 1.61 for
unguided ImageNet generation at 256times256 with only 30 sampling steps
(instead of the standard 250). We thus present HiGS as a plug-and-play
enhancement to standard diffusion sampling that enables faster generation with
higher fidelity.