Guided Diffusion from Self-Supervised Diffusion Features
Vincent Tao Hu Yunlu Chen Mathilde Caron Yuki M. Asano Cees G. M. Snoek Bjorn Ommer
Guidance serves as a key concept in diffusion models, yet its effectiveness
is often limited by the need for extra data annotation or classifier
pretraining. That is why guidance was harnessed from self-supervised learning
backbones, like DINO. However, recent studies have revealed that the feature
representation derived from diffusion model itself is discriminative for
numerous downstream tasks as well, which prompts us to propose a framework to
extract guidance from, and specifically for, diffusion models. Our research has
yielded several significant contributions. Firstly, the guidance signals from
diffusion models are on par with those from class-conditioned diffusion models.
Secondly, feature regularization, when based on the Sinkhorn-Knopp algorithm,
can further enhance feature discriminability in comparison to unconditional
diffusion models. Thirdly, we have constructed an online training approach that
can concurrently derive guidance from diffusion models for diffusion models.
Lastly, we have extended the application of diffusion models along the constant
velocity path of ODE to achieve a more favorable balance between sampling steps
and fidelity. The performance of our methods has been outstanding,
outperforming related baseline comparisons in large-resolution datasets, such
as ImageNet256, ImageNet256-100 and LSUN-Churches. Our code will be released.