HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion
  Models
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.
68da4e15cfa03a5158a1ed0d
Seyedmorteza Sadat
claimed_verified
2025-09-29T11:26:12.223Z
63b4b02a103617b0a5b0ee2e
https://cdn-avatars.huggingface.co/v1/production/uploads/63b4b02a103617b0a5b0ee2e/LasBC-pCnPJ6XuCt6qqcp.jpeg
Seyedmorteza Sadat
user
msadat97
68da4e15cfa03a5158a1ed0e
Farnood Salehi
68da4e15cfa03a5158a1ed0f
Romann M. Weber
HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion
  Models
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.
A momentum-based sampling technique called history-guided sampling (HiGS) improves the quality and efficiency of diffusion models in image generation without additional computation or training.
diffusion models image generation neural function evaluations NFEs guidance scales history-guided sampling HiGS model predictions inference step sampling process image quality sampling budgets SiT model FID ImageNet generation